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Prashant Kathuria

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Prashant Kathuria is currently working as a Senior Data Scientist at upGrad. He describes himself as a data freak and others working with him will agree. Working in Data since more than 3 years in Product companies has taught him that data of today is gold of tomorrow. You will find him brainstoring about new things, or reading about upcoming technologies in his free time.

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Top 15 Deep Learning Interview Questions & Answers
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Although still evolving, Deep Learning has emerged as a breakthrough technology in the field of Data Science. From Google’s DeepMind to self-driving cars, Deep Learning innovations have left the whole world in awe. Companies and organizations around the globe are adopting Deep Learning tech to enhance business possibilities. The result – demand for skilled professionals in Deep Learning and Machine Learning is increasing at an unprecedented pace. In fact, Data Science is so hot in the market right now, that if you can build a career in Data Science, you are good to go! Read on to know more about What is cnn, deep learning, and neural network. Additionally, discover deep learning interview questions to excel in your interview. As you know, to land a successful job in Deep Learning, you must first nail the interview – one of the toughest challenges in the job-hunting process.  Hence, we’ve decided to make it a little easier for you to get a headstart and compiled a list of ten most commonly asked Deep Learning interview questions! Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Top 15 Deep Learning Interview Questions and Answers What is Deep Learning? Deep Learning is the subset of Machine Learning that uses Artificial Neural Nets to allow machines to simulate decision making like humans. Neural Nets are inspired by the neuron structure of the human brain. Deep Learning has found numerous applications in areas like feature detection, computer vision, speech recognition, and natural language processing. What is Perceptron? To understand this, you must first understand how a biological neuron works. A neuron consists of a cell body, an axon, and dendrites.  While dendrites receive signals from other neurons, the cell body sums up all the inputs received, and the axon transmits the information compiled by the cell body as signals to other cells.  Just like this, Perceptron in a neural net receives multiple inputs, applies various transformations and functions to those inputs, and finally combines the information to produce an output. It is a linear model used for binary classification.  What is the function of Weights and Bias? To activate a node within a neural network, we have to use the following formula: output = activation_function(dot_product(weights, inputs)+ bias) Here, weights determine the slope of the classifier line, whereas bias enables the activation function to shift the slope either to the left or right. Generally, bias is treated as a weight input having the value x0. What is the role of an Activation Function? An activation function is used to interject non-linearity into a neural network to help it learn complex tasks. It triggers or activates a neuron by calculating the sum of the weights and adding further bias to it. Without an activation function, a neural network will only be able to perform a linear function, that is, the linear combination of its input data. What is Gradient Descent? Gradient Descent is an optimization algorithm that is used to minimize the cost function of a particular parameter by continually moving in the direction of steepest descent as determined by the negative of the gradient. What is a Cost Function? A cost function (also referred to as “loss”) is a measure of the accuracy of the neural network in relation to a specific training sample and expected output. It determines how well a neural network performs as a whole. With neural networks, the goal always remains the same – to minimize the cost function or errors.  What is Backpropagation? Backpropagation is a training algorithm used in multilayer neural networks to enhance the performance of the network. The method requires to move the error from one end of the network to all the weights contained inside the network, thereby facilitating efficient computation of the gradient and minimizing the error. Here’s how it works: First, the training data is moved forward propagation to produce the output. Use the target value and output value to calculate the error derivative in relation to the output activation. Backpropagate the data for all the hidden layers and update the parameters (weights and biases). Continue this until the error is reduced to a minimum. Now you can feed inputs into your model, and it can predict outputs more accurately. What is Data Normalization? Why is it important? Data normalization is a preprocessing step during backpropagation. It aims to eliminate or minimize data redundancy. Data normalization helps rescale values to fit within a specific range to obtain better convergence for backpropagation – the mean of each data point is subtracted and divided by its standard deviation. How do you initialize weights in a neural network? Basically, there are two ways for weight initialization –  Initialize the weights to zero (0): By doing this, your model becomes just like a linear model, which means that all the neurons and all the layers will perform the same function, thereby hampering the productivity of the deep net. Initialize the weights randomly: In this method, you assigning the weights randomly by initializing them very close to 0. Since different neurons perform different computations, this method ensures better accuracy. What are Hyperparameters? Hyperparameters are variables whose values are set before the training process. They determine both the structure of a network and how it should be trained.  There are many hyperparameters used in neural networks like Activation Function, Learning Rate, Number of Hidden Layers, Network Weight Initialization, Batch Size, and Momentum, to name a few. Here are some cnn interview questions: What is a CNN? What are its different layers? CNN or Convolutional Neural Network is a kind of deep neural networks primarily used for analyzing visual representations. These networks use a host of multilayer perceptrons that require minimal preprocessing. While neural networks use a vector as an input, in a CNN, the input is multi-channeled images.  The different layers of CNN are as follows: Convolutional Layer – This layer performs a convolutional operation to create many smaller picture windows to parse the data. ReLU Layer – This layer introduces non-linearity to the network. It changes all the negative pixels to zero. Pooling Layer – This layer performs a down-sampling operation to reduce the dimensionality of each feature map. Fully Connected Layer – This layer recognizes and classifies all the objects present in the sample image. What Is CNN Pooling, and How Does It Operate? Pooling is used to scale down a CNN’s spatial dimensions. The dimensionality is decreased by down-sampling processes, and a pooled feature map is produced by overlaying a filter matrix over the input matrix. What does CNN mean when it refers to valid padding and the same padding? When padding is not necessary, it is utilised as valid padding. After convolution, the output matrix will be (n – f + 1) X (n – f + 1).  The same padding is used here, covering the output matrix in padding elements. It will have similarities with the input matrix’s dimensions. Here are some neural network interview questions: What is a Neural Network? Neural networks are simplified versions of our brain’s neurons, that simulate how people learn. Three network layers make up the most popular neural networks: A base layer A hidden layer (the most crucial layer where feature extraction occurs and modifications are made to train more quickly and perform better) A layer of output There are “nodes,” or neurons, on each sheet that carry out different functions. Deep learning algorithms like CNN, RNN, GAN, and others employ neural networks. What benefits do neural networks offer? These are some benefits of neural networks: Neural networks are quite flexible and may be applied to much more complicated challenges as well as classification and regression issues.  Additionally, neural networks are very scalable. Any number of layers, each with a unique set of neurons, is possible. It has been demonstrated that neural networks produce the greatest results when there are a lot of data points. With non-linear data, including pictures, text, and other types, they work well. Any information that may be converted into a numerical value can be subject to their use. Once taught, neural network modes produce results quite quickly. They save time as a result. What is the meaning of the term weight initialization in neural networks? Weight initialization is one of the key components of neural networking. A network can not evolve if the initialization of the weights is poor. A good weight initialization, on the other hand, contributes to faster convergence and a lower total error. Biases may be started out from zero. The weights should generally be set so that they are near zero but not too low. So, that’s 15 fundamental Deep Learning questions your interviewer will probably ask you during your DL interview. You must prepare the above interview questions on deep learning properly to excel in your interview. However, just reading up on interview questions isn’t enough to crack a job interview – you must possess in-depth knowledge of the field. The best course of action would be to sign up for a Deep Learning and Machine Learning certification program. These programs are designed to teach you the a-z of both ML and DL.
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by Prashant Kathuria

21 Sep 2023

How Does Machine Learning Work – An Easy Guide
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Netflix and Amazon have gotten pretty great at their game – they always seem to know what content or product you’d love to see / purchase. Don’t you just love to see everything already curated to your taste and preference? While most of us know the secret sauce behind the nifty Recommendation Engine of Netflix and Amazon (Machine Learning, of course!), how many of us are familiar with the inner mechanisms of Machine Learning? Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification To put it straight – How does Machine Learning work? In essence, Machine Learning is a data analytics technique (a subset of AI) that aims to “learn” from experience and enable machines to perform tasks that require intelligence. Machine Learning algorithms apply computational methods to extract information and learn directly from data without being explicitly programmed for it (not having to depend on a predetermined equation). Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI The Anatomy of Machine Learning systems All ML systems can be disintegrated into three parts: Model – the component that deals with the identifications, that is, predictions. Parameters – refers to the factors used by the model to reach its decisions (predictions). Learner – the component that adjusts the parameters (and as a whole, the model) by considering the differences in predictions compared to the actual outcome. Types of Machine Learning Now that you are familiar with the core components of ML systems, it’s time to take a look at the different ways they “learn.” Supervised Learning In Supervised Learning, a model is explicitly trained on how to map the input to the output. A supervised learning algorithm takes a recognized set of input data along with known responses (output) to that data and trains the model to generate reasonable predictions in response to new input data. Supervised learning uses two approaches to develop predictive models – Classification – As the name suggests, this technique classifies input data into different categories by labelling them. It is used to predict discrete responses (for instance, if a cancerous cell is benign or malignant). Medical imaging, speech recognition, and credit scoring are three popular use cases of classification. Regression – This technique is used to predict continuous responses by identifying the patterns in the input data. For instance, fluctuations in temperature or weather. Regression is used to forecast the weather, electricity load, and algorithmic trading. Unsupervised Learning Unsupervised Learning approach uses unlabeled data and seeks to unravel the hidden patterns within it. Thus, the technique draws inferences from datasets consisting of input data devoid of labelled responses. Clustering – One of the most common unsupervised learning methods, clustering is an exploratory data analysis technique that categorizes data into “clusters” without any known information about the cluster credentials. Object recognition and gene sequence analysis are two examples of clustering. Dimensionality Reduction – Dimensionality Reduction cleanses the input data of all the redundant information and retains only the essential parts. Thus, the data not only becomes clean, but it also reduces in size, thereby taking up less storage space. How Machine Learning Works Reinforcement Learning Reinforcement Learning aims to build self-sustained and self-learning models that can learn and improve through trial and error. In the learning (training) process, if the algorithm can successfully perform specific actions, reward signals are triggered. The reward signals function like guiding lights for the algorithms. There are two reward signals: A Positive signal is triggered to encourage and continue a particular sequence of action. A Negative signal is a penalty for a particular wrong action. It demands the correction of mistake before proceeding further in the training process. Reinforcement Learning is widely used in video games. It is also the mechanism behind self-driving cars.  Inside the ‘learning’ function of ML algorithms Behind the functionings of ML algorithms and how they learn through experience, there are three common principles. Learning a Function The first step in the learning process is where ML algorithms learn about the target function (f) that best maps the input variable (X) to the output variable (Y). So, Y = f(X). Here, the form of the target function (f) is unknown, hence the predictive modelling. In this general learning phase, the ML algorithm learns how to make future predictions (Y) based on the new input variables (X). Naturally, the process isn’t free of error. Here error (e) exists independent of the input data (X). So, Y = f(X) + e Since the error (e) might not have enough attributes to characterize the mapping scenario from X to Y best, it is called irreducible error – irrespective of how good the algorithm gets at estimating the target function (f), you cannot reduce the error (e). Making predictions and learning how to improve them In the earlier point, we understood how an ML algorithm learns a target function (f). And we already know that our only and only goal here is to find the best possible way to map Y from X. In other words, we need to find the most accurate way to map the input to the output. There will be errors (e), yes, but the algorithm has to keep trying to understand how far off it is from the desired output (Y) and how to reach it. In this process, it will continually adjust the parameters or the input values (X) to best match the output (Y). This will continue until it reaches a high-degree of semblance and accuracy with the desired output model. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau How to Learn Machine Learning – Step by Step The ‘Gradient Descent’ learning approach It may be true that we have been successful in creating ‘intelligent’ machines, but their pace of learning differs – machines tend to take it slow. They believe in the “gradient descent” learning process – you don’t take the leap at once, but you take baby steps and slowly descend from the top (the metaphor here is that of climbing down a mountain). While descending a mountain, you don’t jump or run or hurl yourself down in one go; instead, you take measured and calculated steps to get down to the bottom safely and avoid mishaps. ML algorithms use this approach – they keep adjusting themselves to the changing parameters (picture the rough and unexplored terrain of a mountain again) to get the desired outcome finally. Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Uses of Machine Learning When it comes to machine learning algorithms, the most common question that follows, ‘How do machine learning algorithms work’ is usually – how can it help us? Machine learning algorithms help build intelligent systems that learn from historical data to generate accurate results. When we know how does machine learning work, we can learn to use it in different ways to enhance services, generate valuable insights, and solve business concerns. Most industries worldwide, for example, marketing, healthcare, finance, defence, and more, regularly use machine learning to improve their services.  Here are some areas which commonly use machine learning.   Facial recognition It uses machine learning, especially for security purposes such as locating missing persons, identifying criminals, and so on. Facial recognition is also used for disease diagnosis, intelligent marketing, and tracking attendance in educational institutions. Sales and Marketing  Lead scoring algorithms use machine learning to evaluate factors like website visits, email opens, downloads, and clicks to assign a score to each lead. Companies can also use regression techniques to predict pricing models, and sentiment analysis is valuable for assessing consumer reactions to newly launched products and marketing efforts.  Computer vision uses machine learning to help brands identify their products in online images and videos and recognize relevant text that may have been missed. Customer support chatbots are also becoming smarter and more responsive. Finances Financial services often use machine learning algorithms to monitor user activity and detect fraud by evaluating whether the activity being carried out is suspicious or not. Financial services also detect money laundering similarly, which is an important security use case.  Trading commonly makes use of ML algorithms to make better trading decisions by analyzing massive amounts of data simultaneously. Credit scores are also evaluated using ML. Automatic Speech Recognition ASR is used to convert spoken words into digital text. This technology has various applications, including user authentication and task execution based on voice commands. The system is trained with speech patterns and vocabulary to improve accuracy. You will often see ASR used in medical assistance, defence and aviation, IT, telecommunication services, law, and other industries.  Healthcare If you are wondering how does supervised machine learning work in healthcare, you should know supervised ML has various uses in this industry. It can be used to diagnose, predict, and treat diseases, especially those that are difficult to diagnose. It is used to enhance medical imaging and diagnostics. Machine learning is vital in discovering early-stage drugs and developing them with accuracy, which acts as a major boost to clinical trials that are time-consuming and expensive. It also includes next-generation sequencing and precision medicine.  A recent example of machine learning in healthcare is its use to predict critical epidemic outbreaks. It is also used to organize large amounts of medical records.  Media and OTT OTT platforms commonly use machine learning to understand user preferences and recommend relevant movies, songs, web series, and more. Online shopping websites like Amazon and Flipkart do the same. These recommendation systems help platforms personalize their services to meet their customers’ preferences, ultimately increasing customer satisfaction and encouraging continued usage. To conclude… The fundamental goal of all Machine Learning algorithms is to develop a predictive model that best generalizes to specific input data. Since ML algorithms and systems train themselves through different kinds of inputs/variables/parameters, it is imperative to have a vast pool of data. This is to allow the ML algorithms to interact with different kinds of data to learn their behaviour and produce the desired outcomes. We hope that with this post we could demystify the workings of Machine Learning for you!
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by Prashant Kathuria

10 Jun 2023

12+ Machine Learning Applications Enhancing Healthcare Sector 2024
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The ever increasing population of the world has put tremendous pressure on the healthcare sector to provide quality treatment and healthcare services. Now, more than ever, people are demanding smart healthcare services, applications, and wearables that will help them to lead better lives and prolong their lifespan. By 2025, Artificial Intelligence in the healthcare sector is projected to increase from $2.1 billion (as of December 2018) to $36.1 billion at a CAGR of 50.2%. The healthcare sector has always been one of the greatest proponents of innovative technology, and Artificial Intelligence and Machine Learning are no exceptions. Just as AI and ML permeated rapidly into the business and e-commerce sectors, they also found numerous use cases within the healthcare industry. In fact, Machine Learning (a subset of AI) has come to play a pivotal role in the realm of healthcare – from improving the delivery system of healthcare services, cutting down costs, and handling patient data to the development of new treatment procedures and drugs, remote monitoring and so much more. This need for a ‘better’ healthcare service is increasingly creating the scope for artificial intelligence (AI) and machine learning (ML) applications to enter the healthcare and pharma world. With no dearth of data in the healthcare sector, the time is ripe to harness the potential of this data with AI and ML applications. Today, AI, ML, and deep learning are affecting every imaginable domain, and healthcare, too, doesn’t remain untouched. Also, the fact that the healthcare sector’s data burden is increasing by the minute (owing to the ever-growing population and higher incidence of diseases) is making it all the more essential to incorporate Machine Learning into its canvas. With Machine Learning, there are endless possibilities. Through its cutting-edge applications, ML is helping transform the healthcare industry for the better. Research firm Frost & Sullivan maintains that by 2021, AI will generate nearly $6.7 billion in revenue in the global healthcare industry. According to McKinsey, big data and machine learning in the healthcare sector have the potential to generate up to $100 billion annually! With the continual innovations in data science and ML, the healthcare sector now holds the potential to leverage revolutionary tools to provide better care. Get Machine Learning Certification online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Here are 12 popular machine learning applications that are making it big in the healthcare industry: 1. Pattern Imaging Analytics Today, healthcare organizations around the world are particularly interested in enhancing imaging analytics and pathology with the help of machine learning tools and algorithms. Machine learning applications can aid radiologists to identify the subtle changes in scans, thereby helping them detect and diagnose health issues at the early stages. One such pathbreaking advancement is Google’s ML algorithm to identify cancerous tumours in mammograms. Also, very recently, at Indiana University-Purdue University Indianapolis, researchers have made a significant breakthrough by developing a machine learning algorithm to predict (with 90% accuracy) the relapse rate for myelogenous leukemia (AML). Other than these breakthroughs, researchers at Stanford have also developed a deep learning algorithm to identify and diagnose skin cancer. There are various techniques for image recognition in machine learning algorithms. Those techniques are- Statistical Pattern Recognition Neural Pattern Recognition Syntactic Pattern Recognition Template Matching Fuzzy Model Hybrid Model These techniques are unique in their own way and serve different purposes. For example, a statistical pattern recognises the historical data, and as it implies, this allows the machine to learn from the previously existing examples. After collecting, studying, and studying the data, it derives new laws that the machine learns to apply to the new data. Neural Pattern Recognition, as the name implies, uses the process of neural networks.  Artificial Neural Networks (ANN) are based on the neural network of a human brain. This is a very advanced technique to analyse patterns in varied types of data such as textual, visual, etc.  Syntactic Pattern Recognition, the alternate name for this type of technique, is structural pattern recognition. It is ideal for solving problems that are complex in nature. There is the involvement of recognizing sub-patterns. Also, there is a huge application of machine learning in healthcare where Pattern Imaging Analytics plays an important role. This brings a lot more accuracy to the decision-making in the healthcare industry.  Machine Learning Engineers: Myths vs. Realities 2. Personalized Treatment & Behavioral Modification Between 2012-2017, the penetration rate of Electronic Health Records in healthcare rose from 40% to 67%. This naturally means more access to individual patient health data. By compiling this personal medical data of individual patients with ML applications and algorithms, health care providers (HCPs) can detect and assess health issues better. Based on supervised learning, medical professionals can predict the risks and threats to a patient’s health according to the symptoms and genetic information in his medical history. FYI: Free nlp online course! This is precisely what IBM Watson Oncology is doing. It is helping physicians to design better treatment plans based on an optimized selection of treatment choices by utilizing the patient’s medical history. Behavioral modification is a crucial aspect of preventive medicine. ML technologies are helping take behavioral modification up a notch to help influence positive behavioral reinforcements in patients. For example, Somatix a B2B2C-based data analytics company has launched an ML-based app that passively monitors and recognizes an array of physical and emotional states. This helps physicians understand what kind of behavioral and lifestyle changes are required for a healthy body and mind. Healthcare startups and organizations have also started to apply ML applications to foster behavioral modifications. Somatix, a data-analytics B2B2C software platform, is a fine example. Its ML application uses “recognition of hand-to-mouth gestures” to help individuals understand and assess their Behavioral, thus allowing them to open up to make life-affirming decisions. Machine learning healthcare applies behavior modification to provide remedies for serious conditions such as Obsessive Compulsive Disorder (OCD), Traumas and Phobias, Separation Anxiety, etc. Apart from positive reinforcement, there are other methods used for behaviour behavior modification, such as negative reinforcement, aversion therapy, etc. Also, there are two ways in which behavior modification works classical conditioning and operant conditioning. This allows for better coping for depression, anxiety, bipolar disorder, etc. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses 3. Drug Discovery & Manufacturing Machine learning applications have found their way into the field of drug discovery, especially in the preliminary stage, right from the initial screening of a drug’s compounds to its estimated success rate based on biological factors. This is primarily based on next-generation sequencing. Machine Learning is being used by pharma companies in the drug discovery and manufacturing process. However, at present, this is limited to using unsupervised ML that can identify patterns in raw data. The focus here is to develop precision medicine powered by unsupervised learning, which allows physicians to identify mechanisms for “multifactorial” diseases. The MIT Clinical Machine Learning Group is one of the leading players in the game. Its precision medicine research aims to develop such algorithms that can help to understand the disease processes better and accordingly chalk out effective treatment for health issues like Type 2 diabetes.  Apart from this, R&D technologies, including next-generation sequencing and precision medicine, are also being used to find alternative paths for the treatment of multifactorial diseases. Microsoft’s Project Hanover uses ML-based technologies for developing precision medicine. Even Google has joined the drug discovery bandwagon. According to the UK Royal Society, machine learning can be of great help in optimizing the bio-manufacturing of pharmaceuticals. Pharmaceutical manufacturers can harness the data from the manufacturing processes to reduce the overall time required to develop drugs, thereby also reducing the cost of manufacturing. Drug discovery has a lot many uses in machine learning applications in the healthcare industry. It allows medical professionals to bring precision, accuracy,  and better delivery within the timelines. Mechanisms like clustering, classification, and regression analysis. Technologies like nanofluidics, automation, imaging software, etc, play a vital role in drug discovery. Also, AI is not limited to providing gene sequencing in the process but also predicting how well the chances are for a drug to work and what are the expected side effects.  Deep learning in healthcare also has a major role to play.  It speeds up the process of drug discovery and comes as a savior in finding the drug to stop the spread of infectious diseases.  It was highly useful for identifying the drugs for Coronavirus. It mapped the potential drugs that could work against the infection to curb the spread. 4. Identifying Diseases and Diagnosis Machine Learning, along with Deep Learning, has helped make a remarkable breakthrough in the diagnosis process. Thanks to these advanced technologies, today, doctors can diagnose even such diseases that were previously beyond diagnosis – be it a tumour/or cancer in the initial stages to genetic diseases. For instance, IBM Watson Genomics integrates cognitive computing with genome-based tumour sequencing to further the diagnosis process so that treatment can be started head-on. Then there’s Microsoft’s InnerEye initiative launched in 2010 that aims to develop breakthrough diagnostic tools for better image analysis. It allows the practitioners to study the medical history and find correlations to build a robust diagnostic model. The data is of varied types, such as the data of diseases, genes, etc. This brings relief to both the medical professionals as well as the patients as this reduces the timeline to find the problem, serves the purpose of taking lesser, the diagnosis is accurate and most importantly reduces the number of visits that are required for a patient. Machine learning for healthcare also works to reduce the chances of misdiagnosis and for the early prediction of diseases. And the potential research shows how machine learning is helpful in curing dangerous diseases like cancer.   Artificial Intelligence: Taking or Rather Taken Over 5. Robotic Surgery Thanks to robotic surgery, today, doctors can successfully operate even in the most complicated situations, and with precision. Case in point – the Da Vinci robot. This robot allows surgeons to control and manipulate robotic limbs to perform surgeries with precision and fewer tremors in tight spaces of the human body. Robotic surgery is also widely used in hair transplantation procedures as it involves fine detailing and delineation. Today robotics is spearheading the field of surgery. Robotics powered by AI and ML algorithms enhance the precision of surgical tools by incorporating real-time surgery metrics, data from successful surgical experiences, and data from pre-op medical records within the surgical procedure. According to Accenture, robotics has reduced the length of stay in surgery by almost 21%.   Mazor Robotics uses AI to enhance customization and keep invasiveness at a minimum in surgical procedures involving body parts with complex anatomies, such as the spine. Also robotic surgery also allows the practitioners to perform the surgeries with sharp precision in complex areas. They are also famously known for non-invasive surgery and are usually done with smaller incisions. They are commonly done for kidney transplants, coronary artery bypass, hip replacements, etc. Robotic surgery allows the practitioners to perform the surgery with lesser pain during and after the surgery. Also, there is a lesser scope of blood flow in robotic surgery procedures. It is expected to be the future of medicine. 6. Personalized Treatment By leveraging patient medical history, ML technologies can help develop customized treatments and medicines that can target specific diseases in individual patients. This, when combined with predictive analytics, reaps further benefits. So, instead of choosing from a given set of diagnoses or estimating the risk to the patient based on his/her symptomatic history, doctors can rely on the predictive abilities of ML to diagnose their patients. IBM Watson Oncology is a prime example of delivering personalized treatment to cancer patients based on their medical history. There are various benefits to personalized treatment, such as better specific diagnosis and reducing the trial and error-based approach. The inclusion of multi-modal data from the patient opens the chances of giving patient-centric medication. And most importantly it reduces the risk to health and reduces the cost that is borne by the patients.  The application of machine learning on genomic datasets facilitates giving better-personalized treatment. Understanding health on a much deeper level also grows as the large volume of data can be understood very well. The capability of analyzing the hidden patterns helps to predict the diseases that can be prevented, reducing the risk to human health. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 7. Clinical Trial Research Machine learning applications present a vast scope for improving clinical trial research. By applying smart predictive analytics to candidates for clinical trials, medical professionals could assess a more comprehensive range of data, which would, of course, reduce the costs and time needed for conducting medical experiments. McKinsey maintains that there is an array of ML applications that can further enhance clinical trial efficiency, such as helping to find the optimum sample sizes for increased efficacy and reducing the chance of data errors by using EHRs. Machine Learning is fast-growing to become a staple in the clinical trial and research process. Why? Clinical trials and research involve a lot of time, effort, and money. Sometimes the process can stretch for years. ML-based predictive analytics help brings down the time and money investment in clinical trials-but would also deliver accurate results. Furthermore, ML technologies can be used to identify potential clinical trial candidates, access their medical history records, monitor the candidates throughout the trial process, select best testing samples, reduce data-based errors, and much more. ML tools can also facilitate remote monitoring by accessing real-time medical data of patients. By feeding the health statistics of patients in the Cloud, ML applications can allow HCPs to predict any potential threats that might compromise the health of the patients. 8. Predicting Epidemic Outbreaks Healthcare organizations are applying ML and AI algorithms to monitor and predict the possible epidemic outbreaks that can take over various parts of the world. By collecting data from satellites, real-time updates on social media, and other vital information from the web, these digital tools can predict epidemic outbreaks. This can be a boon particularly for third-world countries that lack proper healthcare infrastructure. While these are just a few use cases of Machine Learning today, in the future, we can look forward to much more enhanced and pioneering ML applications in healthcare. Since ML is still evolving, we’re in for many more such surprises that will transform human lives, prevent diseases, and help improve healthcare services by leaps and bounds. For instance, Support vector machines and artificial neural networks have helped predict the outbreak of malaria by considering factors such as temperature, average monthly rainfall, etc. ProMED-mail- is a web-based program that allows health organizations to monitor diseases and predict disease outbreaks in real-time. Using automated classification and visualization, HealthMap actively relies on ProMED to track and alert countries about the possible epidemic outbreaks. How Big Data and Machine Learning are Uniting Against Cancer 9. Crowdsourced Data Collection Today, the healthcare sector is extremely invested in crowdsourcing medical data from multiple sources (mobile apps, healthcare platforms, etc.), but of course, with the consent of people. Based on this pool of live health data, doctors and healthcare providers can deliver speedy and necessary treatment to patients (no time wasted in fulfilling formal paperwork). Recently, IBM collaborated with Medtronic to collect and interpret diabetes and insulin data in real-time based on crowdsourced data. Then again, Apple’s Research Kit grants users access to interactive apps that use ML-based facial recognition to treat Asperger’s and Parkinson’s disease. The crowdsourcing data collection helps in improvising the techniques that are used by machine learning and improves the quality of diagnosis given to the patients using AI.  This reduces human intervention and brings better time delivery and reduces the risk of error by gathering the data in real-time which is the opposite of procuring the data through the traditional way. 10. Improved Radiotherapy Machine Learning has proved to be immensely helpful in the field of Radiology. In medical image analysis, there is a multitude of discrete variables that can get triggered at any random moment. ML-based algorithms are beneficial here. Since ML algorithms learn from the many disparate data samples, they can better diagnose and identify the desired variables. For instance, ML is used in medical image analysis to classify objects like lesions into different categories – normal, abnormal, lesion or non-lesion, benign, malignant, and so on. Researchers in UCLH are using Google’s DeepMind Health to develop such algorithms that can detect the difference between healthy cells and cancerous cells, and consequently enhance the radiation treatment for cancerous cells. 11. Maintaining Healthcare Records It is a known fact that regularly updating and maintaining healthcare records and patient medical history is an exhaustive and expensive process. ML technologies are helping solve this issue by reducing the time, effort, and money input in the record-keeping process. Document classification methods using VMs (vector machines) and ML-based OCR recognition techniques like Google’s Cloud Vision API help sort and classify healthcare data. Then there are also smart health records that help connect doctors, healthcare practitioners, and patients to improve research, care delivery, and public health. Today, we stand on the cusp of a medical revolution, all thanks to machine learning and artificial intelligence. However, using technology alone will not improve healthcare. There also needs to be curious and dedicated minds who can give meaning to such brilliant technological innovations as machine learning and AI. Check out Advanced Certification Program in Machine Learning & Cloud with IIT Madras, the best engineering school in the country, to create a program that teaches you not only machine learning but also the effective deployment of it using the cloud infrastructure. Our aim with this program is to open the doors of the most selective institute in the country and give learners access to amazing faculty & resources in order to master a skill that is in high & growing. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Understanding the importance of people in the healthcare sector, Kevin Pho states: “Technology is great. But people and processes improve care. The best predictions are merely suggestions until they’re put into action. In healthcare, that’s the hard part. Success requires talking to people and spending time learning context and workflows — no matter how badly vendors or investors would like to believe otherwise.”
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by Prashant Kathuria

07 Sep 2021

4 Key Benefits of Machine Learning in Cloud: Everything You Need to Know
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For quite a long time now, machine learning has been out of reach for most enterprises. The hardcore machine learning that adds real value to the organization i.e. However, even as we are speaking, technology is advancing. And this advancement has trickled into the domain of machine learning as well to make it widely and properly available for a variety of enterprises. And if you examine the long-term effects, this is nothing less than disruption and revolution. But, how will businesses actually be affected? Let’s dig a little deeper into it today. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification What is machine learning? A quick recap for those who know and a quick intro for those who don’t. Machine learning is a subset/ part of the entire, vast field of artificial intelligence. It is concerned with the development of self-learning algorithms. These algorithms are trained through labeled or unlabeled data sets and examples, then employed to make predictions against new patterns of data. As one can guess, machine learning was and is a huge leap in the realm of artificial intelligence. Instead of using static programs to make decisions, the data presented to the algorithm at that moment is used to make decisions. This is similar to how humans make decisions. Have an inkling of what you are looking for through past experiences (the ‘training data’ in case of the algorithm) and using that plus the data at the moment, arrive at a decision. Although a lot of developments have been made, a lot of work is still left to be done. Scientists and researchers envisage a future where no human intervention and additional programming will be needed for the algorithm to arrive at an answer.  Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Challenges to the entry of machine learning capabilities Here are the biggest ones: The specialized skill and expertise required which is in short supply and not easily available Deployment costs. The computational special-purpose hardware requirements add up to greater costs for development, infrastructure, and workforce. Even with open-source machine learning frameworks like CNTK, MXNet, and TensorFlow, run into problems when scaling up due to the requirement of more computers. How machine learning in the cloud will revolutionize businesses There are 4 major ways in which machine learning in the cloud will act as a boon for businesses. These are: Cost efficiency The cloud has a pay-per-use model. This eliminates the need for companies to invest in heavy working and expensive machine learning systems that they won’t be using always and every day. And for most of the enterprises, this is true since they use machine learning as a tool and not as the modus operandi.  When AI or machine learning workloads would increase, the cloud’s pay-per-se model would come in handy and help companies cut down on costs. The power of GPUs can be leveraged without investing in cost-heavy equipment. Machine learning on the cloud enables cheap data storage, further adding to the cost-efficiency of this system. No special expertise required According to Tech Pro research, only 28% of companies have experience with AI or machine learning. Demand for machine learning is increasing and the future scope of machine learning is bright. 42% said that their IT team is not skilled enough to implement and support AI and machine learning. This suggests a crucial knowledge and expertise gap. But, the cloud helps in bridging it. Using the cloud means that companies do not have to worry about having a data science proficient team. With Google Cloud Platform, Microsoft Azure, and AWS, artificial intelligence features can be implemented without requiring any deep or hardcore knowledge. The SDKs and APIs are already provided so machine learning functionalities can be directly embedded.  Easy to scale up If a company is experimenting with machine learning and its capabilities, it does not make sense to go full-on, full out in the first go only. Using machine learning on the cloud, enterprises can first test and deploy smaller projects on the cloud and then scale up as need and demand increases. The pay-per-use model further makes it easy to access more sophisticated capabilities without the need to bring in new advanced hardware. How to be a part of this revolution As businesses take to machine learning and the cloud together, they’ll be needing professionals who are fluent in operating both and can provide maximum value to the organization. Traditional university courses do not provide the curriculum in classrooms to ready eager students for it. But, at upGrad, we provide the best of both worlds- an online, easily accessible platform plus an integrated, classroom environment. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau If you are interested to learn about cloud computing and Machine learning, upGrad in collaboration with IIIT- Bangalore, has launched the Master of Science in Machine Learning & AI. The course will equip you with the necessary skills for this role: maths, data wrangling, statistics, programming, cloud-related skills, as well as ready you for getting the job of your dreams. If this feels like something you’d be interested in learning, then head to the course page now.
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by Prashant Kathuria

25 Sep 2019

What makes a Good Machine Learning Engineer – Qualities & Skills
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The inclusion of Machine Learning (ML) in mainstream technological applications has made this branch of Data Science, one of the hottest career options right now. As the interest in ML is increasing by the day, it is giving rise to a growing number of job opportunities in the field, with Machine Learning Engineer being one of the most promising jobs.  Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification However, since Machine Learning is still an emerging field, the real challenge lies in finding the right talent for ML jobs. The only problem – there aren’t enough talented and skilled professionals to fill these vacancies. This is where Machine Learning courses come in handy. By enrolling in programs that are specially designed for ML, you will not only learn about ML and the related concepts but also nurture industry skills simultaneously. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. When companies hire Machine Learning Engineers, they look for certain qualities and skills that make an excellent ML Engineer. And guess what? That’s our topic of discussion today! What qualities make up a good Machine Learning Engineer? Before we get into a detailed discussion about the skills and qualities of an ML Engineer, you must first understand the job role. The job of an ML Engineer is neither purely academic-based nor purely research-oriented – it’s a mix of both. Also, while the best ML Engineers need not have a research or academic background, they must have both Software Engineering background and Data Science Experience. Now, let’s discuss the qualities of a skilled ML Engineer. 1. A strong propensity for programming. A Machine Learning Engineer is an expert programmer. ML Engineers usually have a Computer Science/Software Engineering background. Hence, they possess an in-depth understanding of Computer Science concepts like Data Structures, Computer Architectures, Algorithms, Computability & Complexity, among other things. Needless to say, ML Engineers have a flair in at least two programming languages and have coding knowledge at their fingertips. 2. A strong foundation in Mathematics and Statistics. ML Engineers must be well-versed with Mathematical and Statistical concepts including Linear Algebra, Multivariate Calculus, Mean, Median, Variance, Derivatives, Integrals, Standard Deviations, Distributions, etc. Apart from this, they must also know the basic concepts of probability like Bayes rule, Gaussian Mixture Models, Markov Decision Processes, Hidden Markov Models, etc.  Mathematics, Statistics, and Probability lie at the heart of many ML algorithms, and hence, it is crucial to have a strong foundation in these. 3. An intuitive and creative bent of mind. While there is no shortage of Software Engineering/CS graduates, there is definitely a shortage of individuals who are driven by curiosity and the will to learn. A good ML Engineer is an intuitive and creative professional. Only then can they use their Mathematical, Statistical, and Analytical skills to find solutions to complex real-world problems. The goal is to develop innovative ways to look at a problem and create numerous possibilities around it.  4. The innate ability to understand data and derive insights from it. The ability to understand data and derive valuable insights from it is integral to developing ML algorithms and applications. An ML Engineer must be able to decode and unravel the hidden patterns within raw data, analyze it, and interpret it to find actionable business solutions. 5. A keen sense of business knowledge. To develop successful ML applications and projects that actually address different business issues, one must know the business domain inside-out. Every business has unique needs and hence, having a keen sense of knowledge about the business domain is essential to develop specific ML applications and projects best-suited for it. Also, customer satisfaction is a pivotal aspect of a business. Hence, a good ML Engineer will always develop models/applications, keeping in mind the unique needs of the customers or the clientele.  6. The ability to deliver on time.  When you have a highly demanding and versatile job role as that of an ML Engineer, proper time-management is crucial. An ML Engineer has a lot to do within a stipulated time – analyze and interpret data; build ML models; use the right ML algorithms to train models; perform A/B testing, and so much more. Getting so much done within the allotted time and successfully delivering the project to the clients is a highly appreciated quality. 7. The ability to communicate clearly. ML Engineers often work with Data Scientists, Data Analysts, and other technical staff. To work in a team, one must possess excellent communication (both written and verbal) skills. Not everyone can communicate or present their ideas clearly for the teammates to see. ML Engineers must bear this quality to be able to explain their findings and models for a clearer understanding of other team members.  8. A strong passion and drive for work. An employee who is driven by a strong passion for the work he/she does is truly a valuable asset for a company. This is a defining quality that sets them apart from a pool of qualified candidates. Recruiters often look for candidates who bear an immense passion for AI and ML and are ever-ready to seek answers.  These are some of the most valued and demanded quality traits of a Machine Learning Engineer. If you have the right educational background and possess the qualities we’ve mentioned above, you are golden – take our word for it!  Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau
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by Prashant Kathuria

30 Aug 2019

6 Machine Learning Skill Sets That Can Land You in a Perfect Job
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Would you be surprised if we told you that over 50,000 job vacancies in Data Science and Machine Learning remain unfulfilled in India? Considering the fact that Machine Learning is one of the hottest career fields right now, this may seem shocking, but it is the hard truth. Do you know the reason behind the demand-supply paradox of professionals in Data Science and ML? Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses It is solely because there aren’t enough skilled and talented candidates ready to take on the booming job opportunities in these emerging fields. Gartner maintains that among the 10 lakh registered firms in India, as high as 75% have already invested or are ready to invest in Machine Learning. Clearly, job opportunities in Machine learning are bound to increase exponentially in the near future. The need of the hour is “upskilling” to fit the requirements of ML job profiles. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Skills required to land Machine Learning jobs 1. Fundamental knowledge of Computer Science and Programming To build a successful career in ML, you must first you need to have an in-depth understanding of the fundamental concepts of Computer Science including Data Structures (stacks, queues, trees, graphs, multi-dimensional arrays, etc.); Computer Architectures (memory, cache, bandwidth, distributed processing, etc.); Algorithms ( dynamic programming, searching, sorting, etc.), and Computability & Complexity (big-O notation, P vs NP, NP-complete problems, approximate algorithms, etc.), to name a few. Once you understand these, you must learn how to employ and implement them while writing code. As for choosing a programming language, you can begin with Python. It is great for beginners and is the lingua franca of Machine Learning. You can hone your programming skills by taking part in online coding competitions and hackathons. Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. 2. A strong rapport with Probability and Statistics Statistics and probability concepts form the core of numerous ML algorithms. Naturally, it is imperative to have a strong knowledge and understanding of statistical concepts including Mean, Median, Variance, Derivatives, Integrals, Standard Deviations, etc.; Distributions (uniform, normal, binomial, etc.), and the various analysis methods (ANOVA, hypothesis testing, etc.) that are essential both for developing data models and validating them. Apart from statistical flair, you must also understand the fundamentals of probability like Bayes rule, likelihood, independence, Bayes Nets, Gaussian Mixture Models, Markov Decision Processes, Hidden Markov Models, and so on. 3. Experience in Data Modeling and Evaluation One of the primary goals of Machine Learning is to analyze vast amounts of unstructured data. To do this, you must know the art of Data Modelling. Data Modeling is the technique of estimating the underlying data structure of a particular dataset to unravel and identify the hidden patterns within (clusters, correlations, eigenvectors, etc.) and also predict the properties of instances never seen before (classification, regression, anomaly detection, etc.). During the Data Modelling process, you will be required to choose appropriate accuracy/error measures (for instance, log-loss for classification, sum-of-squared-errors for regression, etc.) and evaluation strategies (training-testing split, sequential vs randomized cross-validation, etc.). So, before you start applying algorithms, you need to gain a thorough understanding of the basic concepts involved in in the Data Modelling. 4. Possess Software Engineering skills Whether you are a Data Scientist or a Machine Learning Engineer, you need to possess the typical Software Engineering skills and knowledge base. If you have a Software Engineering background, great! If you don’t, you need to learn about the best practices in Software Engineering, including system design, modularity, version control, code analysis, requirements analysis, testing, documentation, among other things. The following step would be to learn how these concepts function together in the development of system interfaces. Understanding the nitty-gritty of system design is essential to prevent the occurrence of bottlenecks in the process. 5. Learn how to apply ML Algorithms and Libraries There are a host of libraries/packages and APIs that contain the standard implementations of ML algorithms such as Scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc. However, the secret to making the most out of them is to know how to apply them effectively on suitable models (neural nets, decision trees, nearest neighbour, support vector machine, etc.). Not just that, you must also be familiar with the learning procedures (linear regression, gradient descent, genetic algorithms, boosting, etc.) that fit the data at hand. The best way to get familiar with ML algorithms, libraries, and how to apply them correctly is to take up online challenges in Data Science and Machine Learning. 6. Get familiar with Advanced Signal Processing techniques Feature extraction is one of the core essences of Machine Learning. Depending upon the problem at hand, you have to perform feature extraction using appropriate advance signal processing algorithms like wavelets, shearlets, curvelets, contourlets, bandlets, etc. Simultaneously, you must also learn about the various analysis techniques such as Time-Frequency analysis, Fourier Analysis, Convolution, etc. 7. Never stop upskilling and learning As you know, Machine Learning is still an evolving discipline, with time new ML concepts, algorithms, and technologies will develop. To keep pace with the changing times, you must continuously upskill and develop new skill sets. This would involve staying updated with the latest tech and Data Science trends, working with new tools and theories, reading scientific journals, staying active in various online communities, and much more. Long story short, you should always have the urge to learn new things. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau To conclude The applications of Machine Learning have already begun to intertwine in our lives in ways that we couldn’t imagine before. Healthcare, education, finance, business – you name it, Machine Learning is everywhere. As long as the world continues to churn data, Machine Learning will reign, and with time, help us find answers to the most complicated real-world scenarios. The change has begun – it’s time you brace yourself for the new future with Data Science and Machine Learning. So, begin today and start acquiring these Machine Learning skills!
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by Prashant Kathuria

18 Aug 2019

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