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A CNN (Convolutional Neural Network) is a type of deep learning neural network that uses a combination of convolutional and subsampling layers to learn features from large sets of data. It is commonly used for image recognition and classification tasks. The convolutional layers apply filters to the input data, and subsampling layers reduce the input data size. Convolutional Neural Network architecture aims to learn features from the data that can be used to classify or detect objects in the input. Below are the 5 CNN layers explained. Enrol for the Machine Learning Course from the World’s top Universities. Earn Master, Executive PGP, or Advanced Certificate Programs to fast-track your career. 5 Layers of a Convolutional Neural Network 1. Convolutional Layer: This layer performs the convolution operation on the input data, which extracts various features from the data.  Convolutional Layers in a CNN model architecture are one of the most vital components of CNN layers. These layers are responsible for extracting features from the input data and forming the basis for further processing and learning. A convolutional layer consists of a set of filters (also known as kernels) applied to the input data in a sliding window fashion. Each filter extracts a specific set of features from the input data based on the weights associated with it.  The number of filters used in the convolutional layer is one of the key hyperparameters in the architecture. It is determined based on the type of data being processed as well as the desired accuracy of the model. Generally, more filters will result in more features extracted from the input data, allowing for more complex network architectures to understand the data better. The convolution operation consists of multiplying each filter with the data within the sliding window and summing up the results. This operation is repeated for all the filters, resulting in multiple feature maps for a single convolutional layer. These feature maps are then used as input for the following layers, allowing the network to learn more complex features from the data. Convolutional layers are the foundation of deep learning architectures and are used in various applications, such as image recognition, natural language processing, and speech recognition. By extracting the most critical features from the input data, convolutional layers enable the network to learn more complex patterns and make better predictions. 2. Pooling Layer: This layer performs a downsampling operation on the feature maps, which reduces the amount of computation required and also helps to reduce overfitting. The pooling layer is a vital component of the architecture of CNN. It is typically used to reduce the input volume size while extracting meaningful information from the data. Pooling layers are usually used in the later stages of a CNN, allowing the network to focus on more abstract features of an image or other type of input. The pooling layer operates by sliding a window over the input volume and computing a summary statistic for the values within the window. Common statistics include taking the maximum, average, or sum of the values within the window. This reduces the input volume’s size while preserving important information about the data. The pooling layer is also typically used to introduce spatial invariance, meaning that the network will produce the same output regardless of the location of the input within the image. This allows the network to inherit more general features about the image rather than simply memorizing its exact location. 3. Activation Layer: This layer adds non-linearity to the model by applying a non-linear activation function such as ReLU or tanh. An activation layer in a CNN is a layer that serves as a non-linear transformation on the output of the convolutional layer. It is a primary component of the network, allowing it to learn complex relationships between the input and output data. The activation layer can be thought of as a function that takes the output of the convolutional layer and maps it to a different set of values. This enables the network to learn more complex patterns in the data and generalize better. Common activation functions used in CNNs include ReLu (Rectified Linear Unit), sigmoid, and tanh. Each activation function serves a different purpose and can be used in different scenarios. ReLu is the most commonly used activation function in most convolutional networks. It is a non-linear transformation that outputs 0 for all negative values and the same value as the input for all positive values. This allows the network to imbibe more complex patterns in the data. Sigmoid is another commonly used activation function, which outputs values between 0 and 1 for any given input. This helps the network to understand complex relationships between the input and output data but is more computationally expensive than ReLu. Tanh is the least commonly used activation function, which outputs values between -1 and 1 for any given input. The activation layer is an essential component of the CNN, as it prevents linearity and enhances non-linearity in the output. Choosing the right activation function for the network is essential, as each activation function serves a different purpose and can be used in different scenarios. Selecting a suitable activation function can lead to better performance of the CNN structure. 4. Fully Connected Layer: This layer connects each neuron in one layer to every neuron in the next layer, resulting in a fully-connected network. A fully connected layer in a CNN is a layer of neurons connected to every neuron in the previous layer in the network. This is in contrast to convolutional layers, where neurons are only connected to a subset of neurons in the previous layer based on a specific pattern. By connecting every neuron in one layer to every neuron in the next layer, the fully connected layer allows information from the previous layer to be shared across the entire network, thus providing the opportunity for a more comprehensive understanding of the data. Fully connected layers in CNN are typically used towards the end of a CNN model architecture, after the convolutional layers and pooling layers, as they help to identify patterns and correlations that the convolutional layers may not have recognized. Additionally, fully connected layers are used to generate a non-linear decision boundary that can be used for classification. In conclusion, fully connected layers are an integral part of any CNN and provide a powerful tool for identifying patterns and correlations in the data. 5. Output Layer: This is the final layer of the network, which produces the output labels or values. The output layer of a CNN is the final layer in the network and is responsible for producing the output. It is the layer that takes the features extracted from previous layers and combines them in a way that allows it to produce the desired output. A fully connected layer is typically used when the output is a single value, such as a classification or regression problem. A single neuron layer is generally used when the outcome is a vector, such as a probability distribution. A softmax activation function is used when the output is a probability distribution, such as a probability distribution over classes. The output layer of a CNN is also responsible for performing the necessary computations to obtain the desired output. This includes completing the inputs’ necessary linear or non-linear transformations to receive the output required. Finally, the output layer of a CNN can also be used to perform regularization techniques, such as dropout or batch normalization, to improve the network’s performance. Conclusion The CNN architecture is a powerful tool for image and video processing tasks. It is a combination of convolutional layers, pooling layers, and fully connected layers. It allows for extracting features from images, videos, and other data sources and can be used for various tasks, such as object recognition, image classification, and facial recognition. Overall, this type of architecture is highly effective when applied to suitable functions and datasets. Acquire a proficient skill set in ML and DL with upGrad With upGrad’s Advanced Certificate Programme in Machine Learning & Deep Learning offered by IIIT-B, you can gain proficiency in Machine Learning and Deep Learning. The program covers the fundamentals of ML and DL, including topics such as supervised and unsupervised learning, linear and logistic regression, convolutional neural networks, reinforcement learning, and natural language processing. You will also learn to build and deploy ML and DL models in Python and TensorFlow and gain practical experience by working on real-world projects.  This course also includes benefits such as:  Mentorship and guidance from industry experts  Placement assistance to help you find the right job An Advanced Certificate from IIIT Bangalore You can also check out our free courses offered by upGrad in Management, Data Science, Machine Learning, Digital Marketing, and Technology. All of these courses have top-notch learning resources, weekly live lectures, industry assignments, and a certificate of course completion – all free of cost!

What is a Speech Recognition Software? Speech Recognition Software programs are computer programs that interpret human speech and convert it into text. They do so by analyzing individual segments of the entire audio input as electrical signals via an internal microphone in the computer. Using Natural Language Processing (NLP), it transcribes the signals into texts with the nearest word it matches with.  Utility of Speech Recognition Software Speech Recognition Software provides a hands-free technology. When our hands are engaged in chores like driving a car or cooking in the kitchen, voice recognition software can come into use by enabling the handling of appliances that would have otherwise needed our physical involvement. In other cases, it also greatly helps visually impaired or hearing impaired people by providing a platform with a speech-to-text facility. Speech recognition software also helps train deep learning algorithms to recognize human voices and assist IoT devices to further help improve user experience. The significant growth of artificial intelligence and machine learning is another aspect of speech recognition software contributes towards.  Top 10 Speech Recognition Software Let’s take a look at our list of the top 10 software programs mentioned below. 1. Alibaba Cloud Intelligent Speech Interaction   This Chinese cloud major utilizes various technologies like speech synthesis and voice recognition and offers Intelligent Speech Interaction. This software comes with myriads of language interfaces. The software uses a High Accuracy level and promotes continuous self-learning. It also comes with an excellent multilingual transcription capability. Along with a wide spectrum of Application Programming interfaces (APIs), it also comes with a developer guide. Some other features of this software include real-time subtitling and analysis of service calls. The cost of this software includes an expenditure of $1/hour for recorded files and $1.40/hour for real-time voice recognition. 2. Deepgram This software comes with a user-friendly fluid API that allows the developers to convert speech to text without any hassle. This considerably increases revenue by providing a rich experience and boosts workplace productivity. It provides over 90% speech recognition accuracy. The developers have taken to an innovative way of speech recognition by using heuristics-based voice processing, allowing the users to access the fastest and most accurate AI in the industry with an easy-to-make API call. The software has the potential to transcribe one-hour audio in just under 30 seconds.  The software comes with three price packages, namely, a Pay-As-You-Go package where you can purchase credits of your own volition, a Starter package where you can pre-pay $500-$1999 credits for the year, and a Growth package where you can pre-pay $2000-$4999 credits for the year.  Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. 3. Amazon Transcribe This is a voice recognition software by Amazon Web Services (AWS). It uses Natural Language Processing (NLP) for transcribing speech to text. This transcription platform is cloud-based. Transcribe offers an exciting 80% accuracy level with easy-to-read transcriptions. It provides ten alternative suggestions for transcription and gets better by learning the user’s input. Transcribe is extremely careful while handling sensitive and personal data, provides high security, and maintains privacy.  It comes with a free package of 60 minutes per month that lasts for a year. After that, it charges $0.00780 per minute.  4. Krisp This software comes with AI-empowered Noise and Echo Cancellation technology making its way as a leading software in the industry. It comes with a Talk-Time that gives valuable insights into the call, like the percentage of the call users are speaking. This allows the users to better communication. Krisp utilizes three technologies: Automatic Speech Recognition (ASR), Punctuation and Capitalization of the Text, and Speaker Diarization.  It comes in four packages: A Free package. A Pro package costs $96 annually. A Business package costs $120 annually, and an enterprise package needs personal settlements. 5. Nuance dragon Microsoft owns nuance dragon software. The Automatic Speech Recognition (ASR) technology used by the software comes into various uses, including professional and individual applications. It offers up to 99% speech recognition accuracy, and custom voice commands can be defined in this model. This software comes with various developer resources that allow developers to build chatbots and various voice recognition software applications.  For Windows, the starting prices are $200 for Dragon Home and $150 for annual subscriptions for the Professional edition. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses 6. Google Speech-to-Text API This is a cloud-based Automatic Speech Recognition (ASR) software. The software provides language interface ranges of up to 125 languages, and some models are pre-trained for specific domains. It has an accuracy of 80-85%. Here the users can train the model with specific vocabulary according to the need of their domains. The Enterprise offers data security by leveraging the audio-to-text on-premises. Although the software can handle voice recognition in difficult scenarios, it requires technical expertise for handling the software. The software offers the first 60 minutes for free and charges $0.004/15 seconds or more henceforth. 7. Microsoft Azure Cognitive Services for Speech This software is owned by Microsoft and is built on the Azure cloud. The speech Software Development Kit (SDK) consists of two components that allow developers to build applications and provide a Speech Studio that helps modulate the software’s functionality. Azure has a special feature that recognizes the speaker and the speech. This software can either run on the cloud or edge. Azure provides an accuracy level of 75-80%. It has a language interface spectrum of over 100 languages. The software provides elaborate courses on documentation and user-friendly code in the Studio.  The software comes for free for the first five months and costs $1/hour or more. 8. AssemblyAI This is a 2017 startup with a specialization in Applied AI. The software uses deep learning technology to provide excellent speech recognition and user experience. This software provides an accuracy level of up to 100%. The reason behind providing such a high accuracy level is that the platform consists of automated speech recognition and human transcriptionists working conjointly. Not only does it performs transcription, but also it does audio/video-to-text conversions. The model is continuously adaptable by training with custom vocabulary. It offers developers by providing extensive API documentation.  The usage of this software costs $0.00025/second. 9. Voicegain This software provides accurate Automatic Speech Recognition (ASR) using deep neural networks. This software can be run on the cloud or on-premise and provides batch-based audio conversation. This software provides an accuracy level of 85-90%. Voicegain provides a transcription assistant application that is quite user-friendly and can be used while holding meetings or processing recordings. It is adaptable and can be trained using audio data sets to match the desirable vocabulary. Voicegain also comes with a wide range of APIs. This software’s acoustics and language models are easily modifiable, which adds to the product’s value.  The cost of the cloud version of this software starts at $0.0025/minute. 10. IBM Watson Speech to Text Watson is an AI engine owned by IBM with sound voice recognition capabilities. It also provides myriads of language interfaces, audio formats, and other programming interfaces, making it useful for call center analytics. The software comes with a sound 95% voice recognition accuracy level. It has the potential to transcribe seven different languages’ audio into text simultaneously. The language model is easily customizable and also well adaptable to match the respective product names.  The software comes for free for the first 500 minutes, after which it costs $0.01/minute. Machine Learning with upGrad Hoping to obtain a professional certification in machine learning? Want to learn how speech recognition software assists machine learning to create revolutionary IoT devices? We have your back! upGrad’s Professional Certificate in Machine Learning and Artificial Intelligence can be an excellent push for your ML and AI career, helping you to own proficiency in topics like Predictive Analytics, Natural Language Processing, Decision Tree Models, Hypothesis Testing, and many more. Offered under the University of Maryland, the curriculum is curated by industry experts allowing you to hone in-demand skills.  Conclusion From regular speech-to-text operations to professional speech analysis for machine learning algorithms towards deep learning, speech recognition software programs are meant to support diverse needs, therefore needing a strong interface. Our list compiles some of the best speech recognition software in the market. Check out their features and choose the ones that align with your projects the most.

Artificial intelligence controls computers to resemble the decision-making and problem-solving competencies of a human brain. It works on tasks usually linked with learning or thinking, including reasoning and self-correction. Moreover, artificial intelligence blends robust datasets and computer science to derive solutions to the problem. You can design scalable AI-based solutions and acquire self-learning via practical applications. The choice of artificial intelligence projects depends on various factors like your interest, budget, time, and trending topics. Let’s look at some exciting AI project ideas and topics for beginners to improve their skills and enhance their portfolios. Top AI Project Ideas & Topics 1. Fake News Finder Fake news means false or ambiguous information spread to misguide people. Occasionally, fake news is presented so professionally that people completely trust it. It is imperative to differentiate between original news and fake news. If not detected early, it can create many unimaginable issues. Utilize the Real and Fake News dataset available on Kaggle to develop a fake news detector project. The classification of fake and original news occurs via a pre-trained ML model known as BERT. Essentially, it is an open-source NLP model being loaded into Python. 2. Teachable Machine Working on a Teachable Machine is one of the most interesting artificial intelligence project ideas for beginner-level AI enthusiasts. A Teachable Machine refers to a web-based tool developed to offer people easy access to machine learning functionalities. Its website allows you to upload images of various classes. Subsequently, you can train a client-side ML model on those images. This project enables you to learn many potent machine-learning functionalities. 3. Autocorrect Tool When you start working on such AI based projects, you can gradually streamline your everyday tasks. Autocorrect application of AI is used in daily life, which assists in correcting spelling and grammatical errors.  You can build this project in Python using its TextBlob library. Its function ‘correct()’ will be helpful for this project. 4. Fake Product Review Identification It is one of those AI projects for beginners that can deter business owners who usually upload fake product reviews on their websites. Its implementation will ensure that customers will not be diverted to false product reviews when they perform their product research. You can use Kaggle to build this project. Kaggle contains a Deceptive Opinion Spam Corpus dataset with 1600 reviews (800 positive and 800 negative reviews). Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. 5. Plagiarism Analyzer Plagiarism Analyzer is one of the most prevalent artificial intelligence project ideas. The reason is it can detect plagiarism which is imperative to ensure original content. It can be challenging to determine the originality of the content without using a tool. This project helps you to build a plagiarism analyzer application to ensure originality and authenticity across a piece of content. 6. Bird Species Predictor Topic experts can manually classify birds, but the process can be challenging and monotonous since it needs a massive data collection. The Bird Species Predictor project uses AI-based categorization, which uses a random forest to predict bird species. 7. Stock Price Predictor It is one of the most valuable artificial intelligence projects for finance professionals and students aspiring to embark on a career in finance. This project provides access to a broad range of datasets. These datasets let you learn how to use ML algorithms to inspect a considerable amount of data. The availability of a vast amount of data simplifies finding models and patterns. Ultimately, it becomes easy to predict the future stock market precisely. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses 8. Customer Advice System It is one of the most prominent AI project ideas for those business owners willing to understand the customers’ product preferences. It uses a customer advice system to gain instant feedback on customers’ opinions of products. You need to build a real-time message tool within your e-commerce app. It helps you to communicate with customers and discerns their opinions regarding the products. 9. Lane Line Recognition AI based projects are valuable for vehicles too. This project helps you develop a system connecting line-following robots and self-driven vehicles. So they can have real-time analysis of lane lines on a road. If self-driving cars are not effectively trained, it can lead to roadside accidents. This project solves this problem by using Python’s Computer Vision. It contributes to effectively detecting self-driven vehicles and reduces the risks of roadside accidents. Python’s OpenCV library helps you to accomplish this project. 10. Handwritten Digit Recognition This project aims to develop a system that can identify handwritten digits using artificial neural networks. Usually, characters and digits written by humans represent different sizes, shapes, styles, and curves. The computers must be able to identify manual writing. The mentioned project uses artificial neural networks to develop a handwritten digit identification system to decode the digits that humans write accurately. CNN (Convolution Neural Network) is used for identifying digits on paper. 11. Pneumonia Detection It is one of the most useful AI project ideas to detect pneumonia and ensure good health for people. Capturing patients’ X-ray images help you to detect diseases like a tumor, cancer, pneumonia, etc. But the images feature low visibility, and interpretation can be complex. This project aims to develop an AI system using CNN (Convolution Neural Networks) to identify pneumonia from a patient’s X-ray pictures effectively. It trains software solutions to detect and interpret this disease’s results accurately. The software processes the relevant information and tests it in the built-in database. 12. Recommendation System for Customers It is one of the most versatile and prevalent AI projects for beginners in customer management. It builds a recommendation system that helps customers infer more details on products, music, video, and more. It uses concepts of machine learning, data mining, and ANN. The system drives more customers to the website and ultimately boosts the sales of a business.  13. Recognizing the genre of a song This project imparts AI knowledge to beginners in an easy and fun-filled way. It is one of the famous mini-AI projects that gradually strengthen your AI skills. It helps you to recognize a song’s genre. It uses an artificial neural network to identify the song and its genre. Subsequently, it showcases the appropriate playlist. You need to use Python’s Librosa library to derive all the necessary details of the song. 14. Predicting users’ forthcoming location Travelers usually find it difficult to explore, especially when they travel to unaccompanied places. This AI project predicts the user’s most likely next location. It can be a restaurant or holiday venue. The projects make informed decisions using the LempelZiv (LZ) algorithm, Neural Networks (NNs), Markov Model (MM), Association rules, and  Bayesian Networks.  15. Translator app The translator app is an AI project that uses NLP fundamentals. It helps you to develop a translator app that helps translate a sentence from an unfamiliar language to your native one. It can be challenging and laborious to train an AI model from the beginning. However, you can use this project’s pre-trained models called ‘transformers’ that help you to translate any sentence easily. Python’s GluonNLP library can greatly assist in creating this app. 16. Housing Price Predictor This project idea uses fundamental AI features to estimate home price variations. It also uses ML models and algorithms. To develop your dataset, you must download a public dataset from web scraping or Kaggle. The next step is to clean the dataset by determining different null values, anomalies, duplicate entries, etc. Subsequently, you need to calculate different related histograms. As the project progresses, you will be acquainted with test web scraping methods and huge datasets to hold proficiency in the same.   Get Started With Your Machine Learning Journey on UpGrad Hoping to cement your identity in the innovative world of artificial intelligence? upGrad’s Professional Certificate in Machine Learning and Artificial Intelligence program can be the right push you need to embark on this dynamic journey. This 7-month course imparts skills like Advanced SQL, Machine Learning, Predictive Analytics using Python, Time Series, NLP, Data Visualization, Hypothesis Testing, Decision Tree Models, and more. Its exceptional aspects include 300 hours of hands-on learning, 100+ hours of live sessions, a Capstone project in your preferred domain, fortnightly small group coaching sessions, and more.  You can explore job opportunities as a Data Scientist, Senior Data Analyst, Mathematician/ Statistician, Big Data Engineer/ Data Engineer, Software Developer, etc., after completing this course. Conclusion Kick-start your career by working on such AI projects for beginners and gradually working on more advanced ones to enhance your skills and portfolio. These projects can fuel your growth, enhancing your skills and experience level simultaneously. So, make sure to work on any of the AI projects listed here and start soon!

Taking on machine learning projects as a beginner is an excellent way to gain hands-on experience and develop a better understanding of the fundamentals of the field. Working on machine learning project ideas will help you understand the implications of different algorithms and practice data pre-processing, feature engineering, and model-building skills. Here are 15 machine learning project ideas for you to choose from. Top Machine Learning Project Ideas 1. Email Spam Detection: Use supervised machine learning algorithms to create a model that can detect spam emails A Naive Bayes classifier is the most appropriate ML algorithm to detect spam emails. Train it using labeled data and feed the algorithm with a dataset of emails with labels indicating whether they are spam. The algorithm will then use this data to learn the characteristics of spam emails, such as specific words or phrases, and can then accurately organize future emails as spam. 2. Voice Recognition: Use deep learning (DL) to create a model that can recognize human speech DL models use a combination of techniques, such as artificial neural networks, recurrent neural networks, and convolutional neural networks, to process audio data and recognize patterns in the sound. You can train the models using datasets of audio recordings of human speech and then use them to recognize and classify new audio data. You can also use these models to identify speaker characteristics like accent, age, gender, and more. 3. Text Summarization: Create a system that can automatically summarize long pieces of text This machine learning project would use natural language processing (NLP) algorithms to analyze the text for key points and generate a summary. You can use topic modeling, keyword extraction, sentiment analysis, and text summarization algorithms to identify the most important points in the text and generate a summary. You can also tune the model to allow the user to specify the desired length and complexity of the summary. 4. Automatic Traffic Sign Detection: Train a model to detect and classify traffic signs You can create a model to detect and classify traffic signs using OpenCV. The model should use image processing techniques such as color segmentation, feature extraction, blob detection, and template matching to detect and classify traffic signs. You can also employ ML algorithms such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) to classify the detected traffic signs. You can also choose pre-processing and post-processing techniques to improve the model’s accuracy. The pre-processing step will involve image smoothing, noise reduction, and enhancement. In contrast, the post-processing step will involve using the trained model to detect and classify traffic signs in unseen images. 5. Object Detection: Train a model to detect objects in images and videos There are many approaches to object detection, but the most popular are deep learning and CNN. A CNN can identify features in an image and classify them into different categories. It does this by processing the image through a series of convolutional layers, each looking for patterns in the image. The output of the CNN can then be used to generate a bounding box around each object and classify it into a specific category.  To create a model for object detection, you’ll need to collect a large dataset of images and videos, create labels for each object, and train the CNN model using the labeled data. Once trained, the model can detect objects in new images or videos. 6. Build a predictive model to predict a user’s next purchase based on past purchases A predictive model can be built to predict a user’s next purchase based on past purchases by leveraging data mining techniques such as decision trees, logistic regression, and artificial neural networks.  This model can be used to analyze a user’s previous purchase history (the user’s past purchases, the date of those purchases, the type of product purchased, the store from which the product was purchased, etc.) and identify patterns to predict what they are likely to purchase next. Additionally, you can train the model to incorporate factors such as user demographics, product preferences, and pricing to refine the prediction accuracy further. 7. Create a Chatbot using NLP Creating a chatbot using natural language processing (NLP) requires a few steps. First, program the bot to understand natural language by using NLP algorithms. Then, train it with a set of data, such as conversations and questions, to recognize patterns. Once the bot is trained, test it to ensure that it responds to questions naturally. You can also use this as one of your machine learning projects for final year in college. 8. Analyze customer reviews and use ML to recommend products To build a model to recommend products, you would first need to gather customer reviews and identify the features that would be important to analyze. Then pre-process the data by removing stop words (“a”, “the”, “is”, “are”, etc.) and punctuation and converting the text into numerical values. Next, split the data into training and testing sets and use an ML algorithm, such as a support SVM or a random forest, to train the model. Finally, use the model to predict which products will likely be admired by customers based on their reviews and make product recommendations accordingly. 9. Autocomplete: Create an autocomplete model that can suggest words or phrases based on user input This autocomplete model can be implemented by using a library of words and phrases that can be used to suggest relevant words or phrases based on the user input in a short paragraph. You can also use the autocomplete system to suggest words or phrases synonyms of the user’s input, which can help find alternative words or phrases that may be more suited to the user’s needs using ML algorithms. 10. GANs: Create a GANs system that can generate new images from data Generative adversarial networks (GANs) are a type of neural network architecture that consists of two networks: a generator and a discriminator. The generator network is trained to create new images from data, while the discriminator network is trained to distinguish actual images from the generated ones.  To generate new images from data, the generator network inputs a random vector or noise (e.g., a random vector of numbers) and then creates images from it. The discriminator network takes as input both real images and generated ones. Then it returns a probability for each image, indicating how likely it is that the image is real or generated. The generator network is trained to optimize the model’s parameters such that the generated images have a higher likelihood than the real ones. Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. 11. Image Colorization: Create a machine-learning model that can colorize black-and-white images Creating a machine-learning model that can colorize black-and-white images is a challenging and rewarding task. To achieve this, you can leverage the power of convolutional neural networks (CNNs) combined with supervised learning. The model can be trained on a large dataset of colorized and black-and-white images. During training, feed the model with labeled colored and black-and-white images. This will allow the model to learn the patterns associated with each color. Then optimize the model using a loss function such as mean squared error or cross-entropy loss. You should first initialize the model with a CNN architecture. This network should contain multiple convolutional filters and pooling layers that will extract features from the input image. After feature extraction, generate the output image using a fully connected layer. This output image should contain the colorized version of the input image. Once the model is initialized, supervised learning can be used to train and optimize the model. The model’s accuracy should be evaluated on a test set of images to ensure that the colorization process achieves desirable results. 12. Self-driving cars: Create an ML model to control a self-driving car The model for controlling a self-driving car needs to be trained with data from various sources, such as camera and lidar feeds, GPS information, and the vehicle’s own sensors. The model would need to learn how to make decisions about when to turn, stop, and accelerate. The model also needs to be able to adjust its choices as the environment changes. 13. Language Detection: Identify the language of a given text The language detection task can be solved using machine learning algorithms, such as Support Vector Machines (SVMs), Naive Bayes classifiers, and Decision Trees. Train an ML algorithm on a dataset of labeled language samples, which contain the text and the language of the respective texts. The algorithm can then be used to classify a given text as belonging to a particular language. For example, an SVM can be trained to classify a given text as being in English, Spanish, French, or German. 14. Anomaly Detection: Identify unusual patterns in data Anomaly detection is a machine learning model used to identify unusual patterns in data. This model can detect outliers in data sets, detect fraud and malicious activity, or even discover new and exciting trends. It can identify anomalies in various data types, from financial transactions to network traffic. The model compares data points to a set of predetermined criteria and flags those that do not fit the pattern. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses 15. Image Captioning: Use deep learning to generate captions for images You need to combine image recognition and NLP models to generate captions for images using deep learning: You need to feed an image into a CNN to extract features. Feed these features into an LSTM (long short-term memory) network, a recurrent neural network. This network will take the extracted features of the image and generate a caption. You can use a different NLP model to refine the caption and better the accuracy. Conclusion ML projects are an essential part of any machine learning professional’s career. By taking on ML project ideas, you will gain real-world experience in applying the fundamental knowledge of ML algorithms. It will also provide a way to demonstrate your abilities to interviewers and can even lead to job opportunities.  Begin your journey in Machine Learning with upGrad The Advanced Certificate Programme in Machine Learning & NLP by upGrad is designed to help you understand the core concepts and algorithms of Machine Learning & NLP, develop hands-on experience through projects and assignments, and apply the knowledge to solve real-world problems. This course also includes: More than 250 hours of course material Certificate from IIIT Bangalore One-on-one career mentorship sessions More than five capstone projects and much more

A CNN (Convolutional Neural Network) is a type of deep learning neural network that uses a combination of convolutional and subsampling layers to learn features from large sets of data. It is commonly used for image recognition and classification tasks. The convolutional layers apply filters to the input data, and subsampling layers reduce the input data size. Convolutional Neural Network architecture aims to learn features from the data that can be used to classify or detect objects in the input. Below are the 5 CNN layers explained. 5 Layers of a Convolutional Neural Network 1. Convolutional Layer: This layer performs the convolution operation on the input data, which extracts various features from the data.  Convolutional Layers in a CNN model architecture are one of the most vital components of CNN layers. These layers are responsible for extracting features from the input data and forming the basis for further processing and learning. A convolutional layer consists of a set of filters (also known as kernels) applied to the input data in a sliding window fashion. Each filter extracts a specific set of features from the input data based on the weights associated with it.  The number of filters used in the convolutional layer is one of the key hyperparameters in the architecture. It is determined based on the type of data being processed as well as the desired accuracy of the model. Generally, more filters will result in more features extracted from the input data, allowing for more complex network architectures to understand the data better. The convolution operation consists of multiplying each filter with the data within the sliding window and summing up the results. This operation is repeated for all the filters, resulting in multiple feature maps for a single convolutional layer. These feature maps are then used as input for the following layers, allowing the network to learn more complex features from the data. Convolutional layers are the foundation of deep learning architectures and are used in various applications, such as image recognition, natural language processing, and speech recognition. By extracting the most critical features from the input data, convolutional layers enable the network to learn more complex patterns and make better predictions. 2. Pooling Layer: This layer performs a downsampling operation on the feature maps, which reduces the amount of computation required and also helps to reduce overfitting. The pooling layer is a vital component of the architecture of CNN. It is typically used to reduce the input volume size while extracting meaningful information from the data. Pooling layers are usually used in the later stages of a CNN, allowing the network to focus on more abstract features of an image or other type of input. The pooling layer operates by sliding a window over the input volume and computing a summary statistic for the values within the window. Common statistics include taking the maximum, average, or sum of the values within the window. This reduces the input volume’s size while preserving important information about the data. The pooling layer is also typically used to introduce spatial invariance, meaning that the network will produce the same output regardless of the location of the input within the image. This allows the network to inherit more general features about the image rather than simply memorizing its exact location. 3. Activation Layer: This layer adds non-linearity to the model by applying a non-linear activation function such as ReLU or tanh. An activation layer in a CNN is a layer that serves as a non-linear transformation on the output of the convolutional layer. It is a primary component of the network, allowing it to learn complex relationships between the input and output data. The activation layer can be thought of as a function that takes the output of the convolutional layer and maps it to a different set of values. This enables the network to learn more complex patterns in the data and generalize better. Common activation functions used in CNNs include ReLu (Rectified Linear Unit), sigmoid, and tanh. Each activation function serves a different purpose and can be used in different scenarios. ReLu is the most commonly used activation function in most convolutional networks. It is a non-linear transformation that outputs 0 for all negative values and the same value as the input for all positive values. This allows the network to imbibe more complex patterns in the data. Sigmoid is another commonly used activation function, which outputs values between 0 and 1 for any given input. This helps the network to understand complex relationships between the input and output data but is more computationally expensive than ReLu. Tanh is the least commonly used activation function, which outputs values between -1 and 1 for any given input. The activation layer is an essential component of the CNN, as it prevents linearity and enhances non-linearity in the output. Choosing the right activation function for the network is essential, as each activation function serves a different purpose and can be used in different scenarios. Selecting a suitable activation function can lead to better performance of the CNN structure. Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. 4. Fully Connected Layer: This layer connects each neuron in one layer to every neuron in the next layer, resulting in a fully-connected network. A fully connected layer in a CNN is a layer of neurons connected to every neuron in the previous layer in the network. This is in contrast to convolutional layers, where neurons are only connected to a subset of neurons in the previous layer based on a specific pattern. By connecting every neuron in one layer to every neuron in the next layer, the fully connected layer allows information from the previous layer to be shared across the entire network, thus providing the opportunity for a more comprehensive understanding of the data. Fully connected layers in CNN are typically used towards the end of a CNN model architecture, after the convolutional layers and pooling layers, as they help to identify patterns and correlations that the convolutional layers may not have recognized. Additionally, fully connected layers are used to generate a non-linear decision boundary that can be used for classification. In conclusion, fully connected layers are an integral part of any CNN and provide a powerful tool for identifying patterns and correlations in the data. 5. Output Layer: This is the final layer of the network, which produces the output labels or values. The output layer of a CNN is the final layer in the network and is responsible for producing the output. It is the layer that takes the features extracted from previous layers and combines them in a way that allows it to produce the desired output. A fully connected layer is typically used when the output is a single value, such as a classification or regression problem. A single neuron layer is generally used when the outcome is a vector, such as a probability distribution. A softmax activation function is used when the output is a probability distribution, such as a probability distribution over classes. The output layer of a CNN is also responsible for performing the necessary computations to obtain the desired output. This includes completing the inputs’ necessary linear or non-linear transformations to receive the output required. Finally, the output layer of a CNN can also be used to perform regularization techniques, such as dropout or batch normalization, to improve the network’s performance. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses Conclusion The CNN architecture is a powerful tool for image and video processing tasks. It is a combination of convolutional layers, pooling layers, and fully connected layers. It allows for extracting features from images, videos, and other data sources and can be used for various tasks, such as object recognition, image classification, and facial recognition. Overall, this type of architecture is highly effective when applied to suitable functions and datasets. Acquire a proficient skill set in ML and DL with upGrad With upGrad’s Advanced Certificate Programme in Machine Learning & Deep Learning offered by IIIT-B, you can gain proficiency in Machine Learning and Deep Learning. The program covers the fundamentals of ML and DL, including topics such as supervised and unsupervised learning, linear and logistic regression, convolutional neural networks, reinforcement learning, and natural language processing. You will also learn to build and deploy ML and DL models in Python and TensorFlow and gain practical experience by working on real-world projects.  This course also includes benefits such as:  Mentorship and guidance from industry experts  Placement assistance to help you find the right job An Advanced Certificate from IIIT Bangalore

IoT (Internet of Things) is a network that houses multiple smart devices connected to one Cloud source. This network can be regulated in several ways in the Cloud. IoT allows you to gather, send, and exchange data across any devices present in the network. In other words, IoT allows physical devices to communicate wirelessly across networks. This aspect has resulted in the use of IoT devices in myriad applications. Some widespread examples of IoT devices include smart mobiles, smartwatches, smart refrigerators, smart door locks, smart bicycles, etc. IoT architecture is implemented for wearables, smart homes, smart healthcare systems, smart cars, smart retail, etc. The number of IoT devices globally is estimated to triple nearly from 9.7 billion in 2020 to over 29 billion in 2030. IoT devices are extensively used in all kinds of consumer markets and industry verticals. The primary use cases for these devices in the consumer segment are media devices like smartphones. Other use cases with over 1 billion IoT devices estimated by 2030 are IT infrastructure, smart grid, autonomous vehicles, and resource tracking and inspection. You will come across diverse IoT projects related to various industries which can help you obtain your desired machine learning/AI job. Let’s first go through the following section that highlights the suitability of IoT project ideas: IoT project ideas are suitable to you if: You have realized the enormous possibilities of IoT and are ambitious to contribute to this constantly evolving technology. You intend to work on a few practical IoT projects. You want to understand more about IoT architecture and its devices using Arduino or Raspberry Pi. You are a final-year undergraduate student looking for an interesting IoT-based project. IoT Project Ideas and Topics for beginners. 1. Deploying an IoT-enabled system to monitor the air pollution level Conventional monitoring systems cannot accurately monitor pollution levels in towns. You can deploy a smart air-pollution-level monitor system based on IoT architecture to get in-depth insights into the pollution level in a particular city. The IoT system in this project collects data related to pollution statistics from various sources in real time. This project helps you ascertain the air quality in a city. It also measures different components of polluted air, like carbon monoxide, ozone, nitrous oxide, sulfur dioxide, and particulate matter. 2. Monitoring SpO2 and heart rate using an IoT system It is one of the crucial and beneficial IoT project ideas. This is because it measures two of the most important aspects that define an individual’s health. Specifically, it monitors the SpO2 level and heart rate of patients. It is used in both homes and healthcare systems. To deploy this project, you need to use a testing kit, an Arduino UNO, an Adafruit OLED screen, one pulse oximeter, and a buzzer. 3.  IoT-enabled door lock system Traditional doors need simple locks and keys. They boast multiple drawbacks, including keys getting lost and somebody you don’t want can find your keys. You can use a smart door lock system to ensure the excellent security of your place. The corresponding IoT project uses Arduino, a WiFi module, a Solenoid lock, a microcontroller, and a high-voltage transistor. Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. 4. Smart tank water-level supervision system Typically, water gets wasted due to overflowing whenever you fill up a water tank in your home or commercial places. Fortunately, there is a solution to this problem, i.e., a smart tank water-level supervision system can be used. It uses IoT architecture to develop a water monitoring system. It informs you whenever the water level attains the minimum or maximum threshold. The key components used in this IoT project are the Ultrasonic sensor, Bolt WiFi module, Arduino UNO, and buzzer. 5. IoT-enabled helmet for site worker safety It is one of the most useful IoT projects to ensure site workers’ safety. It incorporates a microcontroller and sensors that supervise outdoor conditions and assess workers’ safety. It works on aspects like equipment and workers’ supervision and tracking and site security to guarantee a safe working atmosphere. The safety helmet is implemented with an RF tracking system that sends the data across the IoT network. 6. IoT-based anti-theft system The anti-theft system provides excellent security for homes and offices. It can be deployed on-site or can be retrieved remotely. It is one of the best IoT based projects to ensure superior protection against burglars. The IoT platform allows the management to personalize its settings for every customer. Its IoT-based solution is uniquely programmed to sense any strange activity or movement in space. So, it informs homeowners whenever unwanted people enter the place. After triggering an alert, the users can view the events to check the intruder’s movement and activity. The events are displayed in video format. 7. Finding out lost items using an Arduino-based IoT system This project idea represents one of the most effective IoT based projects that notifies you when somebody tries to misplace your things. It uses Arduino Uno, WiFi Shield 2, force sensor, relay, jumper wires, resistor, electric horn, and informing light. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses 8. Monitoring and controlling traffic signals It uses an Arduino to prevent unwanted traffic jams and delays. The corresponding IoT-based system monitors the traffic level to decrease traffic congestion. It is implemented with advanced wireless technology containing microcontrollers and sensors. The sensors detect the traffic level and duration of the traffic signal. Top priority is offered to emergency vehicles like fire vehicles and ambulances. The second priority goes to various types of goods transportation vehicles. The third priority is common vehicles like bikes, cars, bicycles, etc. Moreover, precedence is defined depending on vehicle density on the road. RFID monitors the objects while tracking, and it, therefore, assists in tracking misplaced vehicles. 9. Inventory management system using IoT project An inventory management system has limitations like an absence of real-time inventory details, imprecise estimation of supply and demand, decentralized control, inadequate optimization, and loss of inventory. The IoT project using a smart inventory management system provides seamless communication, real-time monitoring, and accurate warehouse management. 10. Women’s safety using IoT project This IoT project offers safety to women by using a fingerprint-based system. It alters police when a woman is in an unsafe situation. 11. Face Recognition Bot It is a smart AI bot capable of identifying human voices and faces. It implements cutting-edge facial recognition technology. 12. Weather Reporting System It enables users to state environmental conditions on the Internet. It uses sensors to determine humidity, temperature, and air pressure. 13. Smart Cradle System This IoT-equipped smart cradle connects parents and babies via live audio and video. It allows parents to see and listen to their babies remotely. 14. Smart Energy Grid It uses a smart energy system to avoid power outages and regain the grid immediately after a blackout. When one transmission line is out of order, the IoT system automatically switches to the adjacent region’s transmission lines. 15. Smart Irrigation System Traditional irrigation systems are laborious. But an IoT-based irrigation system includes a circuit board and land moisture sensors to connect to your Ethernet or WiFi network. 16. Smart Agriculture System It can supervise the crop health and irrigation cycle. Based on the corresponding results, it remotely applies pesticides or fertilizers to simplify farm tasks. 17. Night Patrol Robot It uses an advanced surveying machine to monitor your home’s rooms 24×7. It uses infrared sensors to ensure security against intruders. 18. Flood Detection System This IoT-based project monitors and tracks probable flooding events. It collects data from infrared sensors to track water levels, humidity, and temperature. 19. Smart Parking System You can use this IoT-based parking system to eliminate trouble finding parking spaces. It suggests the most suitable places to park your vehicles. 20. Smart Gas Leakage Detector Bot It is based on an IoT-related smart bot that contains a gas sensor to sense any gas leakage in a property. You simply need to equip the IoT bot with a pipe, and it will supervise the line condition to avoid leakage. Get Started With Your Machine Learning Journey on upGrad Hoping to make a career in the rapidly innovative industry of IoT? We have your back! Check out upGrad’s Master of Science in Machine Learning and AI program, which can be your first step towards building a solid foundation for IoT skills.  This 20-month course imparts in-demand skills like NLP, Deep Learning, and Reinforcement Learning. Some of its extraordinary benefits include IIIT Bangalore and LJMU Alumni Status, learning from top AI & ML faculty and industry leaders, and four months of Master’s Thesis. It covers the learning of 20 demanding languages and tools, including Python, Keras, Tensorflow, MySQL, NLTK, sci-kit learn, AWS, docker, Flask, Kubernetes, matplotlib, etc. Enroll now to kick start your journey with upGrad Conclusion These IoT project ideas provide practical exposure to working and implementing IoT architecture. Freshers and professionals can work on these ideas to further strengthen their portfolios. Fluidity in topics like these can easily assist you in upgrading your skill set and bagging lucrative work opportunities. 

Time complexity is a measure of the amount of time needed to execute an algorithm. It is a function of the algorithm’s input size and the type of computing system used. The time complexity of an algorithm determines how long it will take to execute it. The higher the time complexity, the longer it will take for that algorithm to finish running. Algorithms with high time complexities are generally preferred over those with low time complexities if there are other considerations, such as accuracy or space complexity. In time complexity, there are two types of searches.  A binary search is a method of searching for an item in a list, array, or table by making comparisons to the central element of the data set. The time complexity of binary search is O(log n), with n being the number of elements in a data set. It takes less time to find an element in an extensive data set than in a small one. Linear search is an algorithm that sequentially checks every element of the list. It can be used to find a given item in a list or to find the position of an item in a sorted list. The time complexity used for linear search is O(n). For example, it will take ten steps to complete a linear search if you work with ten things. Let’s dive deep into learning the importance and application of time complexity. How Time Complexity Is Used in Algorithms Algorithmic complexity is an essential aspect of time complexity. It is the step or operation that a computer must go through to complete a process. You might not realize it, but many AI-driven tasks rely on time complexity. Algorithms are so ubiquitous in our lives that it’s nearly impossible to avoid them. From the GPS on your phone to the algorithm behind Facebook’s News Feed, we rely more on algorithms than ever before. Algorithmic Complexity vs. Actual Computational Times A computer algorithm is a list of instructions for solving a problem, which can be written as a series of steps to be followed to reach an answer. Algorithms are usually described by the number of steps required, and these steps can vary significantly in length, complexity, and dimensionality. Algorithms come in two types: deterministic and non-deterministic. While deterministic algorithms yield the same kind of output, non-deterministic algorithms generate different outputs for all inputs. Deterministic algorithms guarantee a correct answer based on the input provided. Non-deterministic algorithms need not always have the same result for any given input, meaning that they may not provide an answer guaranteed to be correct based on the feedback provided. The algorithmic complexity is the asymptotic upper bound for the number of operations needed to compute a solution for a given problem. The computational time for an algorithm is the time spent executing it on a given input. In general, algorithms with low algorithmic complexities have high computational times and vice versa. Understanding Merge Sort Time Complexity Merge Sort Algorithm is one of computer science’s most common sorting algorithms. A comparison sort algorithm divides the input list into smaller sublists, recursively sorting each sublist and then merging them to produce a sorted list. Merge Sort time complexity uses the divide-and-conquer strategy. It can be used on any input data size but only works well with manageable data sets because it requires time proportional to the list size to complete. It has O(n log(n)) time complexity, meaning it takes linear time on lists of any size. Merge Sort can be summarized as follows: Divide the array into two halves by picking the middle element as the pivot index Sort each half of the array in descending order Exchange elements to make their respective arrays identical if there is more than one element Recursively call merge sort on each of these sorted arrays until they are both sorted How To Use the Laws of Time Complexity for Better Decision-making The time complexity can be used to decide between different algorithms with different running times. The one with lower time complexity will outperform the other in most cases. The space complexity can also choose whether algorithms have additional space requirements. Two key concepts of time complexity should be considered when making a decision. These include: 1) the expected running time for a program, which is the average amount of time it will take to execute that program on all possible inputs, and 2) the space complexity, which is the amount of memory needed to store all information needed to run a program. How To Calculate Time Complexity The time complexity of a function is the amount of work it needs to do about the size of its input. The time complexity is calculated by using Big-O notation. This notation describes the complexity of a function as a mathematical expression involving one or more variables. The letter “O” represents the term “order” and comes after a variable in the expression that represents how many times the variable appears in an equation. For example, if we want to calculate how much work a function does concerning its input size, we would use this formula: ƒ(x)=O(x). Types of Time Complexity Constant Time Complexity – O(1) In constant time complexity, the algorithm will take the same amount of time to run regardless of how large the input size is. It is an essential property because as long as you have enough memory, you should be able to process any input size reasonably. Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Logarithmic Time Complexity – O(log n) The logarithmic time complexity is O(log n). Although the algorithm description seems lengthy, it is simple. One more operation is required to process every item added to the list. It is made more difficult to understand by the notation used. Linear Time Complexity – O(n) Linear time complexity measures an algorithm’s efficiency. One can calculate it by dividing the number of operations by the number of input items. The time complexity for an algorithm is linear if it takes a constant amount of time to process each input item. As the size of the input increases, so does the processing time. O(n log n) Time Complexity An algorithm with O(n log n) time complexity is an algorithm with a running time proportional to the logarithm’s input size. An algorithm with O(n) time complexity ensures the running time is proportional to the input size and will take more time as we increase the input size. An algorithm’s time complexity is measured by calculating how long it takes for the program to finish its work. The lower, the better. Quadratic Time Complexity – O(n2) The quadratic time complexity is also known as O(n2). In this type, the problem’s solving time will be proportional to the number of inputs’ squares. It can happen for two reasons –either because it takes more steps to find each input or because it takes more steps to process each input. This type of complexity applies to any algorithm where there is a constant difference in computation power between each step, which implies that any algorithm with quadratic time complexity will be inefficient when there are many inputs. The Importance of Choosing Appropriate Algorithms for Your Purpose In computer science, many algorithms are used for different purposes. The choice of algorithm you make depends on the problem and the resources you have available. Different algorithms have different time complexities; some are used for various issues. Some algorithms are more efficient than others, but they may not be appropriate for your particular task. We should be mindful when choosing a suitable algorithm for our purpose. If we choose the correct algorithm, it might lead to a good result. One of the most popular algorithms is the k-means clustering algorithm. It is an unsupervised Machine Learning algorithm that groups data points into clusters. Many factors go into choosing the suitable algorithm. The first factor is the time complexity of the algorithm. If your algorithm needs to be fast, you should choose a faster one. The second factor is the accuracy of the algorithm. If you need your algorithm to be as accurate as possible, you should choose a more complex and slower-running one. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses The third factor is how much data you have available. Many algorithms can work for your purposes if you have a lot of data. Still, if there is little data available, it’s essential to find an appropriate algorithm that can effectively use the little data there is. Conclusion Time complexity is an important part of Machine Learning. Algorithms have been a part of our lives for years now. From how we search for things on Google to how we shop online, algorithms are used in many ways. The growth rate of computational costs has been going strong for a while. The computational costs of machine learning algorithms have increased exponentially in the past few years. One of the reasons for the increased costs is the exponential growth in data. To keep up with these costs, companies must find better ways to train their models and more efficient methods to use their computational power. To learn more about how this works, you can opt for upGrad’s Master of Science in Machine Learning and Artificial Intelligence offered by IIIT-Bangalore and LJMU.

Machine learning can effectively analyze data with several dimensions. However, it becomes complex to develop relevant models as the number of dimensions significantly increases. You will get abnormal results when you try to analyze data in high-dimensional spaces. This situation refers to the curse of dimensionality in machine learning. It depicts the need for more computational efforts to process and analyze a machine-learning model.  Let’s first understand what dimensions mean in this context. What are Dimensions? Dimensions are features that may be dependent or independent. The concept of dimensions in context to the curse of dimensionality becomes easier to understand with the help of an example. Suppose there is a dataset with 100 features. Now let’s assume you intend to build various separate machine learning models from this dataset. The models can be model-1, model-2, …. model-100. The difference between these models is the number of features. Suppose we build model-1 with 3 features and model-2 with 5 features (both models have the same dataset). The model-2 has more information than model-1 because its number of features is comparatively higher. So, the accuracy of model-2 is more than that of model-1. With the increase in the number of features, the model’s accuracy increases. However, after a specific threshold value, the model’s accuracy will not increase, although the number of features increases. This is because a model is fed with a lot of information, making it incompetent to train with correct information. The phenomenon when a machine learning model’s accuracy decreases, although increasing the number of features after a certain threshold, is called the curse of dimensionality. Why is it challenging to analyze high-dimensional data? Humans are ineffective at finding patterns that may be spanned over many dimensions. When more dimensions are added to a machine learning model, the processing power required for the data analysis increases. Moreover, adding more dimensions increases the amount of training data needed to make purposeful data models. The curse of dimensionality in machine learning is defined as follows, As the number of dimensions or features increases, the amount of data needed to generalize the machine learning model accurately increases exponentially. The increase in dimensions makes the data sparse, and it increases the difficulty of generalizing the model. More training data is needed to generalize that model better. The higher dimensions lead to equidistant separation between points. The higher the dimensions, the more difficult it will be to sample from because the sampling loses its randomness. It becomes harder to collect observations if there are plenty of features. These dimensions make all observations in the dataset to be equidistant from all other observations. The clustering uses Euclidean distance to measure the similarity between the observations. The meaningful clusters can’t be formed if the distances are equidistant. How to solve the curse of dimensionality? The following methods can solve the curse of dimensionality. 1) Hughes Phenomenon The Hughes Phenomenon states that with the increase in the number of features, the classifier’s performance also increases until the optimal number of features is attained. The classifier’s performance degrades when more features are added according to the training sets’ size. Let’s understand the Hughes Phenomenon with an example. Suppose a dataset consists of all the binary features. We also suppose that the dimensionality is 4, meaning there are 4 features. In this case, the number of data points is 2^4 =16. If the dimensionality is 5, the number of data points will be 2^5 = 1024. These examples indicate that the number of data points exponentially increases with the dimensionality. So, the number of data points that a machine learning model needs for training is directly proportional to the dimensionality. From the Hughes Phenomenon, it is concluded that for a fixed-sized dataset, the increment in dimensionality leads to reduced performance of a machine learning model. The solution to the Hughes Phenomenon is Dimensionality Reduction. “Dimensionality Reduction” is the data conversion from a high-dimensional into a low-dimensional space. The idea behind this conversion is to let the low-dimensional representation hold some significant properties of the data. These properties will be almost identical to the data’s natural dimensions. Alternatively, it suggests decreasing the dataset’s dimensions. How does Dimensionality Reduction help solve the Curse of Dimensionality? It decreases the dataset’s dimensions and thus decreases the storage space. It significantly decreases the computation time. This is because less number of dimensions need less computing time, and ultimately the algorithms train faster than before. It improves models’ accuracy. It decreases multicollinearity. It simplifies the data visualization process. Moreover, it easily identifies a meaningful pattern in the dataset because visualization in 1D/2D/3D space is quite simpler than visualization of more dimensions. Note that Dimensionality Reduction is categorized into two types, i.e., Feature Selection and Feature Extraction. Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. 2) Deep Learning Technique Deep learning doesn’t encounter the same concerns as other machine learning algorithms when dealing with high-dimensionality applications. This fact makes Neural Network modeling quite effective. Neural Network’s resistance to the curse of dimensionality proves to be quite useful in big data. The Manifold Hypothesis is one theory that justifies how deep learning solves the curse of dimensionality in data mining. This theory implies that high dimensional data overlaps on a lower dimensional manifold that is equipped in a higher dimensional space. It implies that in the high-dimensional data, there exists a fundamental pattern in lower-level dimensions that deep learning techniques can effectively manipulate. Hence, for a high-dimensional matrix, neural networks can efficiently find low-dimensional features that don’t exist in the high-dimensional space. 3) Use of Cosine similarity: The effect of high dimensions in the curse of dimensionality in data mining can be reduced by uniquely measuring distance in a space vector. Specifically, you can use cosine similarity to substitute Euclidean distance. Cosine similarity presents less impact on data in higher dimensional spaces. Cosine similarity is extensively used in word-to-vec, TF-IDF, etc. Cosine similarity assumes that the observations are made by assuming that the points are spread randomly and uniformly. If the points are not uniformly and randomly organized, the following conditions must be considered. i) The effect of dimensionality is high when the points are densely located, and dimensionality is high. ii) The effect of dimensionality is low when the points are sparsely located, and dimensionality is high. 4) PCA One of the conventional tools capable of solving the curse of dimensionality is PCA (Principal Component Analysis). It converts the data into the most useful space. Hence, it enables the use of lesser dimensions that are quite instructive than the original data. In the pre-processing stage, the nonlinear relations between the initial data components may not be maintained because PCA is a linear tool. In other words, PCA is a linear dimensionality reduction algorithm that lets you extract a new set of variables from a huge set of variables known as Principal Components. It is important to note how Principal components are extracted. The first principal component describes the maximum variance in the dataset. The second principal component describes other variances in the dataset; it is unrelated to the first principal component. The third principal component describes the variance that is not described by the first and second principal components. In a nutshell, PCA finds the best linear combinations of the variables to ensure the spread of points or the variance across the new variable is maximum. Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses Get Started With Your Machine Learning Journey on UpGrad If you want to master Machine Learning and Artificial Intelligence skills, then you can pursue upGrad’s leading Master of Science in Machine Learning & AI course. This 18-month course covers subjects like Deep Learning, Machine Learning, Computer Vision, NLP, Cloud, Transformers, and MLOps. It is suitable for Data Professionals, Engineers, and Software and IT Professionals. After completing this course, you can work as a Machine Learning Engineer, Data Scientist, Data Engineer, and MLOps Engineer. Some of the exceptional facets of this course are 15+ case studies and assignments, IIIT Bangalore & Alumni Status, Career Bootcamp, AI-Powered Profile Builder, guidance from expert mentors, and more. Conclusion With the increase in the number of dimensions, the analysis and generalization of a machine learning model become difficult. It demands more computational efforts for its processing. The solutions discussed above can depend on the type of machine learning model and the type of application.

Artificial Intelligence is a field of science that enables computers and machines to perform various functions, including the ability to learn, reason, make recommendations, understand spoken and written languages, and act in a way that would require human intelligence. According to a study, artificial intelligence software marketing will proliferate in the coming years, estimated to reach around $126 billion by 2025. In today’s time, artificial intelligence is the most in-demand technology in the computer world. It provides various benefits like reducing human errors and providing more accuracy in any task.  Artificial intelligence can provide its service endlessly. It can help in risky situations by creating AI robots that can perform tasks on behalf of humans. Artificial intelligence can reach any place that human beings cannot reach.  Following the unconventional nature of this field, artificial intelligence and machine learning salaries in US are on a higher end compared to other tech fields.  Artificial Intelligence Salary Package in United States Artificial intelligence salary in US depends upon years of experience, profile, expertise, and diverse other factors. Here are the three levels on which AI salary is based upon.  Entry-Level AI Engineer Salary This category is for freshers who are yet to gain ample knowledge and experience in the field. Their primary focus is to understand and learn fundamental concepts. Artificial intelligence has a high demand; hence leading companies are always on the lookout for skilled interns. As this job role still has plenty of room for improvement, the artificial intelligence salary for entry-level engineers is the lowest among all. Artificial intelligence salary in US for freshers is approximately $115,000 per year.  Mid-level AI Engineer Salary Mid-level artificial intelligence engineers are skilled in this field and tend to work under the same corporation for years. They are more equipped than entry-level engineers, so their pay scale is much larger than entry-level employees. AI salary in US for mid-level engineers is approximately $146,664 per year. These clusters of people are stable yet evolving individuals.  Senior-level AI Engineer Salary  Senior-level artificial intelligence engineers are the highest paid among all. They are independent and collaborative, with enhanced skills and experience. As they are senior-level engineers, they have 4-7 years of experience in this field. Artificial intelligence salary in US for experienced engineers is approximately $205,000 per year.   Top 5 Job Roles and Salaries of Artificial Intelligence   Artificial intelligence salary in US for freshers and experienced candidates also vary depending on their job profile. Here’s a list of top AI jobs and their offered salary figures.  Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Machine Learning Engineer A machine learning engineer focuses on researching and designing self-running software that will ease machine learning initiatives. These engineers are in massive demand by companies. They are responsible for working in areas such as preventing fraud, managing risks, and customer insights. If you are interested in becoming a successful machine learning engineer, mathematics, programming, and computing are some must-have skills.  An average salary of a machine learning engineer is $107,635 per year. A master’s or doctoral degree in mathematics, Computer Science or AI is mainly preferred for this job. Though, deep knowledge of machine learning algorithms and neural networks is considered to be additionally valued. Data Scientist  The responsibility of a data scientist is to use their expertise in mathematics, computer science, and statistics in order to analyze complex data. Data scientists gather data from various sources to gain constructive inferences and make predictions depending on past and present data information. Data assessment further helps them to analyze the cause of the drawbacks in the business and, at the same time, how to overcome them in the coming future. With the support of data scientists, a business can perform positively. The average salary of a data scientist is $97,546 per year.  If you want to build your career as a data scientist, you must hold an advanced degree in Computer Science. The applicant must hone proficiency in programming languages such as Python, SQL, or Scala.  Big Data Engineer/Architect  Big data professionals are responsible for various processes, such as the development, maintenance, and evaluation of a company’s data. Big data helps companies to improve their efficiency and profitability. Hence, big data engineers are required by companies in order to collect and maintain databases. If you are keen to build your career in AI as a big data engineer, you must have proficiency in programming languages like Python and Scala. The students who have a Ph.D. in Math or Computer Science are given more preference. This field is suitable for both freshers and experienced as it has a good pay scale compared to other jobs in artificial intelligence. The average salary of a big data engineer is $112,564 per year.  AI Research Scientist  These are the professionals who are experts in the field of deep learning, machine learning, and statistics. A research scientist is responsible for designing and analyzing the information. In order to work as an AI research scientist, you must have a master’s or doctoral degree in Computer Science. The average salary of a research scientist is $82,803 per year. The field greatly values experience and offers exceptional opportunities to people with a significant experience in their candidatures.  Our AI & ML Programs in US Master of Science in Machine Learning & AI from LJMU and IIITB Executive PG Program in Machine Learning & Artificial Intelligence from IIITB To Explore all our courses, visit our page below. Machine Learning Courses Business Intelligence Developer  Business Intelligence Developers are professionals who help increase a business’s profitability by analyzing data and utilizing the obtained insights to reap valuable results. They recognize various trends by designing, testing, and maintaining data storage systems. A Business Intelligence Developer’s responsibility is to find solutions for business intelligence to meet business needs. The average salary of these experts is $98,816 per year. If you are interested in this profession, a degree in computing and engineering with experience in design, SQL queries, and data processing is essential.  Best Highest Paying Companies for AI Engineers  Here are the top companies that pay exceptionally higher artificial intelligence salary per month in US. Google Google is one of the leading global technology corporations with great demand for artificial intelligence engineers. The AI engineer salary in US for Google employees is $236,388 annually.  Facebook (Meta) Facebook is one of the leading social media platforms that recruits experienced candidates in machine learning and artificial intelligence. AI jobs salary offered by Facebook (Meta) is approximately $257,846 annually.  Apple  Apple is the biggest technological corporation worldwide. This company specializes in internet services and computer software. They recruit employees for the roles such as artificial intelligence team head, machine learning engineer, and many others. The average pay scale offered to AI engineers here is approximately $227,094 annually.     Amazon  The American e-commerce giant Amazon has expanded its service in digital streaming and artificial intelligence. Amazon offers an excellent salary to artificial intelligence engineers compared to other companies. It provides approximately $209,762 annually as its AI engineers salary in US.  Uber Global transportation leader Uber has a solid team of AI professionals as well, which receives around $314,746  as their annual pay package.  How to boost artificial intelligence salaries? AI salary in US depends on various factors, and the best way to raise your salary is to improve those factors consistently.  Improve and develop soft skills Work towards developing and improving soft skills such as critical thinking, creativity, and a positive attitude to work under pressure. You should have the ability to collaborate. This is essential for all the various artificial intelligence positions. Earn certification   Certifications can drastically influence your skill set, while adding them to your candidature can further strengthen it. So, ensure you collect ample experience and go for certification courses to pave a successful career AI career for yourself.  Advance your education with a master’s degree One of the best ways to boost AI salary is through a master’s degree. This degree will help you to gain a better and deeper understanding of artificial intelligence theories and topics.  Do your research   Ensure that you’ve done prior research on salary figures and your current market worth. This can help you to be prepared to negotiate with your recruiter for a better artificial intelligence salary per month in US.  Conclusion  Artificial intelligence is one of the top trends in the IT sector. You can build a lucrative career by honing the right skills and experience as a professional AI engineer. This job involves challenges and constant learning, making it worth all the hard work you put into upskilling. As an artificial intelligence engineer, you can achieve rewarding salaries both as a beginner and experienced.  upGrad’s Master of Science in Machine Learning and Artificial Intelligence is one such course that will help you bag your dream role as an AI engineer. Offered under the guidance of Liverpool John Moores University and IIIT-B, this course equips you with in-demand skills to emerge as an industry leader!