Machine learning, a branch of artificial intelligence, is a process that involves training the computer in a way that it can think on its own using mathematical data sets. In other words, it enables the computer to mimic the human brain in terms of making decisions accurately without manual intervention.
Machine learning algorithms create a mathematical model with the help of sample historical data, referred to as training data, that aids in making predictions or judgments without being explicitly programmed. In order to create predictive models, machine learning combines computer science and statistics. Machine learning is the process of creating or employing algorithms that learn from past data. The more information we supply, the better our performance will be. With the help of machine learning, companies and industries can understand the trends and also define new customer-oriented trends.
There are many applications of machine learning even in our day-to-day life. An example of the same is the voice recognition feature in smartphones. The various smart assistants like OK Google, Alexa, or Siri are all products of programs taught to converse with humans and make quick decisions. The self-driving cars are also another great example of machine learning. The cars are tested by implementing changes in the traffic conditions and the car avoids collision by understanding the situation and taking the right decision.
Even though machine learning belongs to the broader category of artificial intelligence, as a discipline, machine learning is a core component of data science. Since data is the foundation of every business, having machines that think smart by making data-informed decisions is essential. And that’s what machine learning does.
A machine learning course helps programmers and data scientists make machines capable of thinking and taking decisions after being programmed with specific sets of algorithms. This can include detecting and deleting phishing links to even generating invoices and sending them to clients.
It further enables programmers and data scientists to define the algorithms, which are primarily variables and conditions, and program the machine in such a way that the machine can act unsupervised in the near future. Ultimately, undergoing Machine learning training can help you advance in your technology career.
Here are some benefits of opting for a machine learning course are:
Career growth: It is estimated that by 2025, the machine learning industry will generate a revenue of around $19.9 billion. Having the machine learning skills on your resume would catapult your chances of landing a high-paying technical job, even with one of the Fortune 500 companies.
Lesser competition: Since machine learning is still in its development phase, it is often being considered an advanced degree reserved only for professionals. Thus, having an undergraduate degree in machine learning will not only make you stand out from the crowd but will also expose you to an untapped market full of opportunities.
Exciting job: Machine learning and data science make students and professionals deal with various data sets. From these sets, predictions are made. Given the uncertainty of what the data might reveal, it unfolds a great level of excitement and challenge that any curious mind would love to explore.
A machine learning course is ideal for people dealing with big data and those working on cyber security features for various industries. Being an advanced subject, machine learning is primarily suitable as a master’s program for people who already have a degree in computer science and mathematics. These degrees include:
Given the growing need for automated processes, the machine learning course along with the artificial intelligence course can be opted as a minor even during the undergraduate program. This can be taken as an elective along with computer science or statistics, or computational biology.
The course can be opted for students who wish to work with advanced programming. They can start as early as pursuing machine learning in an undergraduate program. But, opting for a master’s degree is rather more beneficial in landing you a high-paying job.
As mentioned earlier, a machine learning course is ideal for people who already have advanced degrees in computer science and/or technological subjects like statistics. But, nowadays, machine learning is part of the bachelor’s program and is even taught as an engineering discipline.
If you are interested in the subject, it would be ideal for opting for machine learning courses after passing the 12th standard with science and computer applications as the core subjects.
Being an analytical subject and comparatively new in the field of statistical analysis and predictive models, there is no fixed age limit for obtaining a degree or a diploma with a machine learning course. But, the core subjects in the high school should be technical including mathematics and computer application being essential.
A machine learning course can last from 3 months to as long as 4 years, depending on the type of degree you pursue. If one hasn’t opted for computer science as an elective or is unaware of the basic programming languages, then the duration for the completion of the course can extend to 6 years. However, with diligence and hard work, getting a degree in machine learning and bridging any gaps with pathways is possible. There is also the option for crash courses offered by various companies like Google or Amazon that train the students with specific skill sets that align with the company’s job profile.
Machine learning can be taken as a full-time course. Undergraduate and postgraduate degrees are available as full-time courses. Students opting for the full-time course need to meet the credit hours required for that course within the given time frame.
It takes three years to complete the BSc. degree. This might vary from country to country, depending on how the credits are calculated. The B.Tech degree usually takes around four years to complete. But, the credits required to complete the degree remain the same.
The full-time postgraduate or Master’s degree is either a one-year or two-year long course, depending on the college and country.
The part-time machine learning course is ideal for working professionals or students. It is usually longer in duration, mostly double the tenure mentioned in the full-time course, and the credit earned is the same as the full-time course credits.
For a diploma or certification program, one-year is the usual duration. These courses are focused on developing skills that are required for immediate job seekers. Professionals with experience in data science or those with a B Tech in artificial intelligence or an M Tech in artificial intelligence degree can take up a machine learning specialisation course to further their career prospects. The AI and ML courses are also done as one-year courses.
Other than that, the PGDM or postgraduate diploma course is a year-long course that can be completed online. It is primarily a self-paced and self-learning model that many educational service providers offer. It is ideal for people looking to enhance their skills and improve their career prospects.
An online machine learning course is usually preferred by working professionals and even students who are seeking to get a degree from foreign universities like Stanford or Harvard, known to offer the best machine learning courses online. Many companies offer crash courses for bridging professional gaps. One such course is the AWS machine learning certification. The usual duration of these courses is between a few weeks to about 3 months.
Another popular course is the machine learning crash course. Many companies curate their curricula to suit the company’s job profile for specific roles that help students and professionals to get accustomed to the native artificial intelligence environment.
Machine learning is taught both as an offline and an online course. Many students prefer the offline traditional learning method. There’s no denying the fun college experience and socialising that comes with an offline course. But, the online course has its fair share of benefits. These are:
Students and professionals who opt for an online course can also get their certifications from esteemed universities and colleges. For example, IIT Madras also offers online machine learning certification. Hence, these courses can further your job prospects by many folds.
Since the classes are not restricted to a specific geographic location, you can meet many people who have enrolled in the same class. The online classes are also not limited to specific age groups, making the classroom more diverse consisting of a freshly high school graduate student to a senior programming developer with years of experience. This increases bonding and also helps develop networking skills.
While most of the classes are conducted by human teachers, some are even held by chatbots. The introduction of chatbots into education enables the machine to gauge the understanding level of the student and curate the course to suit their pace. This helps students self-learn as they try and navigate the chatbot programming. This also helps one understand the concept of big data with real-life models to help practice better.
In terms of curriculum, the online courses can also be curated by the students themselves so that they can study subjects that they have a deep understanding of. When the curriculum is diverse, you can specialise in core components with ease. You can also learn or relearn programming languages along with these courses. But, more interesting is that you get to know the intricacies of programming and how to create specific codes for specific tasks.
1. Skill Required for Machine Learning Course
To ace machine learning, it is essential to have basic knowledge of the core mathematical and computer science concepts. And one must opt in for science while in school to be considered eligible for pursuing machine learning as a degree. If any of these subjects were not opted for in a high school, opting for pathway programs will help overcome the education gap. These subjects or skills include:
In order to be eligible to get selected for a machine learning course at an undergraduate level, it is essential to opt for the science stream in high school. Computers as the primary elective is another must. Having an average score of 50% to 60% in the primary and core elective subjects is also necessary. For students interested in pursuing machine learning as a master's or postgraduate degree, it is essential for having a bachelor's degree in science, computer, or engineering with at least an average of 50% in the core subjects.
In terms of prerequisites, the subjects required to be considered eligible are:
The machine learning course admission process for 2022 is dependent on the entrance examination requirements. Many universities and colleges have cutoff criteria that require students to acquire a certain score during their school or undergraduate course. The engineering degree requires students to undergo the admission process by taking the entrance examination and securing a good rank.
When taken as an engineering subject, the student must appear for any JEE or Joint Entrance Examination conducted by various engineering colleges and universities. The CET or Common Entrance Test is also an admission test that many states require for being eligible for studying STEM subjects. Some of the popular entrance examinations are IIT-JEE, VITEEE, SUAT, and LPU NEST.
Applying for machine learning crash courses is an easy process and is often delivered on a first come first serve basis. Some of these courses and diplomas are also tailored to bridge learning gaps and further career prospects. For example, machine learning with python is offered by various online educational platforms
Being part of the artificial intelligence and data science fields, machine learning is primarily included in the following core subjects:
The syllabus for the machine learning course depends on the level of the degree. The crash courses and diploma certifications too are based on the specialization that the student wishes to attain.
The core subjects taught in the machine learning course are:
Apart from the above-mentioned subjects, basics like the ones listed below are also taught on an undergraduate level:
Machine learning is considered to be a pioneer in the fields of artificial intelligence and computer gaming. The capability of a machine to think for itself has been a breakthrough. This made machine learning an essential part of any data-related field. The core function of machine learning is the classification of data based on the models that the algorithm depicts and the capability of making predictions based on the analysis of that collected data.
Today, machine learning can be used as a specialization subject in order to increase career prospects across a variety of fields. Some of the fields that make use of machine learning are:
Artificial intelligence or AI helps make computers and electronic devices act smart. But, the basis of this development is machine learning and specific algorithms. As artificial intelligence focuses on the construction of computational systems, the function of machine learning is to make these systems work smoothly. Each variable added by the machine learning algorithm helps artificial intelligence to work more efficiently.
Being an interdisciplinary subject, data science deals with anything related to collecting and analyzing complex data. Since collection plays a vital role in data science, there is always the chance of redundant or unwanted data getting collected along with important data sets. This makes the task of analyzing much more difficult. With machine learning, data science predictive models can function better. The machine learning algorithm can act as a filter to not only prevent the collection of data but also to make real-time predictions related to business trends.
Data mining is used to extract relatable and useful data from data sets. This is a task that helps with the discovery of both known and unknown data. But, when the company needs to focus solely on known data so that predictions can be made, the application of machine learning to data mining can help with this task.
Mathematical optimization or mathematical programming refers to the utilization of specific mathematical criteria that will help make decisions better. In other words, with the use of mathematical functions like linear programming and probability, it will be easy to predict the future concerning business trends. Machine learning helps make these predictions more accurate by pitting the prediction against data history.
Computational Intelligence is a field of computer science that deals with the implementation of models that are based on biological and linguistic paradigms. Simply put, computational intelligence is more about the machine already learning for years. With machine learning, this development of computational intelligence is made more accurate.
In 2021, machine learning courses were reported to generate a revenue of USD 15.44 billion. Reports are predicting that by 2029, the market need for machine learning is set to generate a staggering revenue of about USD 209 billion.
These statistics are enough to point out that machine learning is gaining momentum and is no longer considered just to be a part of artificial intelligence but a separate field itself.
In order to become a machine learning engineer, successfully completing a machine learning course is a must. You can either opt for a machine learning and artificial course right after completing your high school education, or you can opt for a machine learning course as an advanced degree or diploma post completion of the bachelor’s degree.
When pursuing machine learning as an undergraduate discipline, it is required that you should be a student of science along with computer applications. If the computer was not the chosen elective, then a foundation or a crash course in computer science, especially a programming language, can be done. Many degrees, especially the engineering ones, would require you to sit for an entrance examination and secure proper rankings to be eligible to learn from top universities. If you are opting for a diploma in machine learning from a foreign university, you can look up online for the same and see if you meet the prerequisites.
If you wish to pursue machine learning as a master’s degree, you can do so by sitting for an admission test known as GATE or Graduate Aptitude Test in Engineering. This requirement is mainly for the engineering section. For an MSc. of course, having the minimum cut-off marks will be enough.
Machine learning is fast becoming a popular choice amongst Indian students and professionals. With the future being depicted to be powered by artificial intelligence and machine learning, jumping on the bandwagon is not a bad idea. Also, the salary for a graduate with experience in machine learning is lucrative.
Mumbai is the dreamland for many. Being a gateway to port trades and the entertainment industry, the city deals with millions of bytes of data regularly. This makes the backend processes of keeping track of everything a bit difficult, provided a machine learning expert is not hired. Some of the best colleges and universities in Mumbai are:
Home to the popular IIT institute and known as the capital of the country, Delhi is a popular choice for many students who want to have a great work and college life balance. The city is primarily focused on becoming a booming tech and business hub. This means that there will be a great demand for engineers and scientists with a background in machine learning. Some of the top universities and colleges in Delhi are:
The city of joy Kolkata is a budding tech and business city and the need for having specialization skills, especially machine learning and data analytics is highly sought after. Kolkata is a slow-paced city and while many might think that there might not be very profitable, the city is known to produce some notable scholars. Some of the popular universities and colleges in Kolkata are:
Chennai is ranked as one of the top five places in the country that has a booming technological advancement. Be it the filtered coffee or the appetizing food, Chennai offers something to everyone. In terms of the scope of machine learning and career about the same, Chennai is a notable city for having the top courses. Some of the popular universities and colleges in Chennai are:
The city of Bangalore is known for being the IT hub of the country. In fact, Bangalore is also known as the ‘Silicon Valley of India’. There are various multinational corporations that have their headquarters in Bangalore and many companies also prefer to begin their tech journey from this very city. Being the IT capital of India, Bangalore has some of the best institutes that focus on training the next generation of computer science enthusiasts. Some of the top machine learning colleges and universities in Bangalore are:
Being the capital city of Telangana, Hyderabad has been the seat of power for many Mughals and Nizams. The old city with its roads painted with history is also a top tech city that caters to the IT needs of the country. But, that would be only natural as the city was always known to be the business hub of ancient India. Some of the top colleges and universities for doing a machine learning course in Hyderabad are:
Pune is a thriving and appealing city in the state of Maharastra. Known for its pleasant weather and its traditional Maharashtrian heritage kept intact, Pune is a thriving informational technology or IT hub of the state and the technological edge provided by this industry makes the city all the more attractive to young professionals. Some of the top colleges and institutes for doing a machine learning course in Pune are:
Being the former capital of Gujrat, Ahmedabad is also known to be the Textile City of the state and the country. The city is full of layers of history and each place that you visit will have multiple stories that will take you down the roads of ancient India. But, history aside, Ahmedabad is now emerging as a leading IT and technological hub of the state. Some of the popular colleges and universities for doing a machine learning course in Ahmedabad are:
While machine learning is a relatively new field of education in India, there are many countries out there that have been offering machine learning programs for over a few decades now. In fact, the concept of machine learning was introduced as far back in history as 1959 by an IBM pioneer. Many people think that machine learning and artificial intelligence are interchangeable. But, AI and ML are different, thus, should not be confused with each other. In India, most of the courses are combined with Robotics or Artificial Intelligence while courses abroad are more focused on Machine Learning as a separate field.
Some of the top-ranking universities and colleges across the globe are:
In order to be eligible to get considered for a degree in machine learning from abroad, you need to fulfill the following prerequisites:
The spread of the novel coronavirus has left global economies grasping at strands, businesses disrupted, and the majority of people stranded. However, while the physical world slowed significantly, the digital world on the other hand boomed. Furthermore, corporations finally understood the potential of Machine Learning and recognised the possibilities of home workplaces. ML has taken the front foot as more and more brands realise the capabilities of these tools, a development that has already gotten a lot of attention in recent years.
The adoption of machine learning and artificial intelligence by organisations has to be one of the main reasons why many people have been able to transition to working from home without too much difficulty. Many firms, both small and large, have been prompted to rethink their operations as a result of this transition. With corporations already stating plans to investigate a more reliable working mechanism involving less office space and more detailed and structured online working systems, the focus on Machine Learning is only going to grow.
During the lockdown, the field of data science has grown more and more in relevance and interest. This is why getting a degree in this profession can help you improve your chances of securing a good job. There are a variety of courses available if you have always been interested in data sciences and machine learning, or if you are already working in this industry and want to advance your career. Starting an additional degree to pad your résumé and learn some cutting-edge concepts while obtaining access to industry experts can be a good move, to begin with.
As machine learning is a thriving career path abroad, especially in countries like Denmark and Germany, it is highly paying. The most common career path after completing a machine learning degree is to become a machine learning engineer.
Interested to know more about salary? Please visit the page - Machine Learning Salary in Abroad
Average Salary Hike
Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn.
Learners will apply Q-Learning to train an RL agent to play the game of numerical Tic Tac Toe.
Create a solution that will help in identifying the type of complaint ticket raised by the customers of a multinational bank
Build a machine learning model capable of detecting fraudulent transactions. Here you have to predict fraudulent credit card transactions with the help of machine learning models.
Build a neural network from scratch in Tensorflow to identify the type of skin cancer from image.
Make a Smart TV system which can control the TV with user’s hand gestures as the remote control
Build a model to using the concepts of natural language processing and recommender systems to recommend news stories to users on a popular news platform.
Learners will use the Markov Decision Process & Q-Learning to build an RL agent that learns to choose the best request so as to maximize the total profit earned by the agent that day.
You will build a custom NER to get the list of diseases and their treatment from a medical healthcare dataset.
Build a model that can help any visually impaired person in understanding image present before them.
Build a sentiment analysis based product recommendation system to recommend the similar products to the users. Sentiment analysis is used to fine tune the product recommendation system.
Predict the sales for a european pharma giant using a host of different types of variables. Apply VAR and VARMAX models to build the appropriate model
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa. CycleGAN model is used for producing T2 type MRI images given T1 type input MRI images
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa.
Create a custom object detector using the YOLO algorithm to detect the presence of face masks in the images of different people.
Machine learning or ML is a subset of Artificial intelligence. The core objective of this field is to create algorithms that will set some variable so that the machine can think for itself when they encounter those variable in other data sets. Deep learning or DL, on the other hand, is more complex as the programming aims to mimic how the human brain operates. The deep learning algorithms have artificial neural systems that function as the human neural system. Deep learning is more suitable for complex and very large data sets.
An algorithm is a set of instructions that are set finitely. In order to explain this better, imagine the set of instructions that are provided with a recipe. There are ingredients and there are set steps that are provided for cooking that specific dish. Algorithms are the same set of instructions for a computer or mathematical program. Some commonly used algorithms are:
A machine learning engineer has a job description that is similar to that of a data scientist or any other data science course career path. But, a machine learning engineer is more focused on the creation of machine learning algorithms. These algorithms could work with little to no supervision from humans.
Like any other data-related field, machine learning also provides a widely diverse career path. While the machine learning engineer is the primary career option, you can even become a data scientist or a data analyst after completing the course. The more specialization you have, the more complex your job will get and that will also positively impact your earnings.
In simple terms, artificial intelligence is the umbrella term. It is the final product that we see and use. Our Ai-powered devices are an example of artificial intelligence that constantly evolves to make smarter decisions. Backing up this artificial intelligence are machine learning and data science. Data science discerns the patterns that the user is generating and machine learning updates the program to work on the predicted outcomes.
If you know about the working of the anomaly detection program, you will have an understanding of semi-supervised algorithms. This type of algorithm is made to understand a specific set of problems and then that understanding is made to be implied on a larger scale.
Technically, you cannot become a machine learning engineer if you have no inkling about the basic concepts of computer science and programming languages. If you did not have computers as a subject in school, you can opt for crash courses to accelerate your learning. This will also help you get into any renowned institute as it depicts your learning enthusiasm.
Machine learning requires a basic understanding of the web, the data sets, and the coding languages. The best languages for these purposes are:
Being a widely-used programming language for machine learning, Python has various libraries that make the work of a machine learning engineer easier. These libraries are:
Just like the spinal cord is the backbone of the whole human body, Big Data is mainly supported by machine learning. Big Data is a set of complex and unstructured data. But, even though there is a great amount to search through, the benefit of Big Data is that you can have many aspects related to a specific data set. Machine learning makes handling and streamlining Big Data easy.
Yes, like any other discipline, it is possible to obtain a Ph.D. degree in machine learning. This is done after completing a master’s degree and after successfully passing the NET test that is required for all doctorate aspirants.
Another career option after completing a machine learning course is a computational linguist is responsible for assisting Ai-enabled speech recognition synthesis. The job description for a computational linguist is:
There are many platforms and machine learning tutorials available online, and for free. But, these are not suitable for freshers who have just begun understanding the basics of machine learning. Self-study is possible only when you have a good understanding of data science, artificial intelligence, and coding along with programming and generating predictive models.
Hence, it is advised that if you have just graduated from school, you can opt for an online course first to understand the basics. These include Bootcamp courses. This will help you also decide if machine learning is for you.
At the master’s level, there are six core subjects that the students take. These courses are:
Apart from these, there are various electives that the students can opt for. These electives are:
The R programming language is ideal for statistically driven libraries that enable machine learning to make predictive analysis more effective. While Python is known to be the most suitable programming language for machine learning, it is ideal for beginners and even experts who are only working with the basics. With R, the people who work with data experimentation and exploration find it easy.
The primary difference lies in the approach that is taken by both these fields. A statistical modeling approach is more focused on implying parameters, like logistic regression or linear regression. On the other hand, machine learning is more about nonparametric approaches like nearest trees or kernel SVM.
As machine learning is primarily about algorithms, there need to be some preferred algorithms that the machine learning field prefers over others. As a broader classification, the machine learning algorithm is divided into three parts: Supervised learning, Unsupervised learning, and Reinforcement learning.
The most preferred learning algorithms are:
A subfield of linguistics, computer science, and artificial intelligence, Natural Language Processing helps facilitate the communication between humans and computers. This is primarily used to help a computer process a large amount of data related to the natural language.
Some courses are curated by developers and machine learning experts that are offered for free and are perfect for beginners looking to understand if machine learning is their cup of tea or not. Some of these are: