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Machine Learning Free Online Course with Certification [2023]

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12th Jun, 2023
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Machine Learning Free Online Course with Certification [2023]

Want to learn machine learning but don’t know where to start? Don’t worry, and we’ve got your back. We are launching upStart, an exclusive ecosystem from upGrad, which allows you to study through free courses. You can enroll in upStart’s machine learning course for free and start your learning journey today. The only investment you’d only have to make is 30 minutes per day for a few weeks to complete the course. 

This article will discuss why learning Machine Learning is crucial, why our free machine learning course is suitable for you, and what topics it covers. Let’s begin. 

Why Learn Machine Learning?

Machine Learning has become a buzzword in the tech world. Companies are actively looking for ML experts because it offers multiple advantages to them. Accenture Research and Frontier Economics performed a study that found that the more AI is integrated into economic processes, the better its potential for economic growth. They found that AI can boost the profitability of businesses by an average of 38% by 2035. (Source)

Businesses recognize the potential Machine Learning and AI have. That’s why the demand for ML experts is continually on the rise. Machine Learning finds applications in many industries, including marketing, eCommerce, finance, entertainment, and many more. Learning this skill will undoubtedly help you become a sought-after professional and get a lucrative salary. Learn more about machine learning career in India. 

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The Growing Demand for Machine Learning Skills

Machine learning has emerged as a game changer across sectors in today’s digital era. It’s capacity to analyze massive volumes of data and generate important insights has revolutionized company operations. As a result, the need for experts with machine learning expertise is increasing dramatically.

Finance, healthcare, e-commerce, marketing, and other fields have discovered uses for machine learning. Machine learning algorithms are being used by businesses to improve customer experience, optimize operations, and make data-driven choices. As automation and artificial intelligence become more widely used, the demand for professional machine learning practitioners grows.

Machine learning specialists are in charge of creating and deploying algorithms that can learn and improve based on data. They clean and preprocess data, use relevant methods, and train models to create accurate predictions or classifications. These abilities are in great demand as firms seek to harness the power of data in order to achieve a competitive advantage.

Why Choose Introduction to Machine Learning Concepts from upGrad?

You might wonder why you should select upGrad’s Introduction to Machine Learning Concepts to study Machine Learning. Here are some advantages of our machine learning course for free that will help you decide in this regard:

1 to 1 Industry Mentorship

You get to study exclusively from a leading Machine Learning expert. It would allow you to remove any doubts regarding the subject matter. 

Cutting Edge Content

All the courses are products of seasoned industry experts to ensure that you get to understand all the concepts as quickly as possible. 

Weekly Live Lectures

Every week, you’ll get live lectures from your instructor. Live interactions help in streamlining the learning experience. 

Free Certificate

After you complete the machine learning course for free, you’ll get a certificate that will enhance your resume. 

Must Read: Machine Learning Project Ideas for Beginners

What Will You Learn?

Machine Learning seems like one of the most complicated subjects in the tech sector, so our free Machine Learning course simplifies this highly challenging subject for you. It covers the fundamentals of Machine Learning and helps you understand them in detail. After completing this project, you’d know most of the basic Machine Learning concepts. 

Our Machine Learning online course free lasts for six weeks, and covers multiple topics. You’d only have to invest 30 minutes per day for six weeks to become an expert at Machine Learning basics. The course allows you to remove a lot of doubts and confusion. Here is the course’s structure:

  • Linear Regression
  • Logistic Regression
  • Clustering
  • Recommender Systems

This structure ensures that you get to learn these concepts step by step. Let’s explore each one of these sections in detail:

Linear Regression

Probably one of the most popular and well-understood algorithms, Linear regression is the first Machine Learning algorithm you’ll learn about in our course. Linear Regression allows you to determine the strength of the relationship between multiple independent variables and a dependent variable.

It’s the most straightforward Machine Learning algorithm and so, understanding its theory and application is crucial to learn about more ML algorithms. 

You’d get to understand how this algorithm works, what it is, and where we use it in Machine Learning. Apart from Linear Regression, this section will teach you how to use some of the most popular ML tools, such as Python and Jupyter.

Python is a prominent programming language. In a ranking of multiple programming languages according to popularity from RedMonk, Python ranked at the second place. Knowing how to use this programming language is vital because it finds many applications in Machine Learning.

You’d use it to write ML models, perform predictive analysis, and many other tasks. Linear Regression has many real-life applications in various sectors, including agriculture, business, etc. 

Logistic Regression

In this section, you’d learn about Logistic Regression, a technique to perform binary classification factors. It calculates the relationship between one or multiple independent variables and the categorical dependent variable through a logistic function.

It is a supervised learning algorithm and helps predict the probability of a specific variable. The target variable’s nature here is dichotomous. This means the variable can only have two possible classes. In simple language, the target variable is binary in nature and can either have 0 (stands for no) or 1 (stands for yes) in data.

Logistic Regression is among the simplest Machine Learning algorithms, but it has various applications in different industries. For example, it is used for detecting cancer and predicting diabetes in the medical field. In the tech sector, you can use it to detect spam.

However, before you apply Logistic Regression for such sophisticated applications, you must understand its basic concepts. This section of our free machine learning course will help you with the same. 


In this section, you’ll learn about Clustering. It is a prominent Machine Learning method that allows you to group elements into different groups (called clusters) when you don’t have any pre-defined factors or labels to classify them. It is an unsupervised method, which means you’d need to draw references from data sources that don’t have labeled responses.

Clustering divides data points into several groups such that the data points present in one group are more similar to the others present in it. Also, the data points present in one group would be different from those present in another one. 

Clustering is quite essential as it helps you determine the intrinsic grouping among your unlabeled data. You can use it to find unusual data points (also called outliers) to clean your data. On the other hand, you can use it to find suitable groups (also called useful classes) in your available data.

Clustering has many applications in various industries. Marketing, insurance, geology, management, and multiple other sectors utilize this powerful Machine Learning method. Some of its applications include anomaly detection, market segmentation, image segmentation, statistical data analysis, and social network analysis. Still, to understand its application correctly, you’d need to know the basics of Clustering first. This section of our free machine learning course will help you with the same. 

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Recommender Systems

This is the final segment of our machine learning online course free. Here, you’d get to know what a recommender system is and how it works; you’ll understand what ML algorithms recommendation engines use and how eCommerce platforms utilize this technology.

Recommender systems are one of the most popular Machine Learning applications. Amazon, Flipkart, Netflix, Facebook, and many other digital service providers use this technology to enhance user experience. 

A recommender system is created to suggest products (or services) to the user according to their past interaction with the platform. They suggest things that the user is more likely to purchase or interact with than others. Recommender systems consider many factors while offering something to a user, such as a user’s past purchases, the product (or service) the user is currently viewing, the product present in the user’s wishlist, etc.

They are highly complicated, and to understand them properly, and you should know the basic concepts. You will learn about the same in this section of our free machine learning course. 

Career Opportunities in Machine Learning

Aspiring professionals now have many job options because of the increased need for machine learning expertise. Let’s look at some of the most fascinating jobs in machine learning:

Machine Learning Engineers: These individuals plan, develop, and build machine learning systems. They are experts in programming, algorithms, and model deployment, and they are in charge of creating strong and scalable machine-learning solutions.

Data Scientist: Data scientists analyze massive datasets to solve complicated business challenges. They collect insights and drive decision-making processes using statistical approaches and machine learning algorithms.

AI researcher: AI researchers do cutting-edge research to advance the field of artificial intelligence and machine learning. They investigate new algorithms, create novel models, and push the frontiers of what is possible.

Data Engineers create and manage the infrastructure needed to store, process, and analyze large datasets. They collaborate closely with data scientists and machine learning engineers to assure data availability and dependability.

Corporate Intelligence Analyst: BI analysts analyze corporate data, discover patterns, and deliver important insights to stakeholders using machine learning techniques. They are critical in facilitating data-driven decision-making inside organizations.

Machine Learning Consultant: Machine learning consultants assist firms in identifying possibilities for implementing machine learning and developing personalized solutions. They have a thorough awareness of technical and business elements, allowing organizations to exploit machine learning effectively.

To start a career in machine learning, you must first master the relevant skills and information. Many colleges and online platforms provide specialized machine-learning courses and best machine learning certifications. Furthermore, having hands-on experience through projects and internships can considerably increase your chances of landing a machine-learning position.

As the need for machine learning specialists grows, so will the income opportunities. Machine learning professionals fetch lucrative wages because of their specialized expertise and the value they provide to organizations. Furthermore, the profession provides several prospects for advancement and continual learning, making it an intriguing career choice.

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How to Start

To join our machine learning online course free, follow these simple steps:

All the courses present on our upStart page are available for free and don’t require any monetary investment. These courses help you kickstart your learning journey and get acquainted with the fundamentals of such complicated subjects. 

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Sign up here to join our free machine learning course today. 

If you have any questions or suggestions, please let us know through the comments. We’d love to hear from you. 

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Our Popular Machine Learning Course

Frequently Asked Questions (FAQs)

1Which IT jobs are in trend apart from Machine learning?

Machine learning is a big fuss these days. But there are many other fields which are also in demand. There are software developers, who create applications for androids, iOS, and computer devices. There are quality analysts, who test software, identify problems, and provide solutions, and information security analysts, who are responsible for developing and implementing security strategies. Other professionals can be data scientists, who analyse substantial amounts of data and help in decision-making, and UI designers, who ensure the smooth and logical functionality of the software.

2What are the major areas of Machine Learning?

Machine learning is the art of learning from past cases, especially in Artificial Intelligence. The major areas are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning provides training through datasets that produce an inferred function. Unsupervised learning is comparatively tricky mainly because of the type of data used, which in this case is ungrouped. No ideal solution is provided, and the machine is made to learn independently. The third and final type is reinforcement learning. In this, the learning is automatically guided by the ideal behaviour.

3What is the difference between Linear and Logistics Regression?

Both linear and logistics regression are used to establish a relationship between an independent variable and dependent variables. In linear regression, prediction is made regarding the continuous dependent variable, while in logistic regression, the categorical dependent variable is predicted. The former is used to solve regression problems, while the latter is for categorical variables. The output for the former one is a continuous value, like price, age, etc.; on the other hand, for the latter one, it is a categorical value, like 0 or 1, yes or no, etc. In linear regression, there may or may not be collinearity between independent variables, but in logistics regression, there cannot be collinearity between independent variables.

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