How to learn Machine Learning for free?

How to learn Machine Learning for free?

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Machine Learning is one of the transforming technologies that has had a ripple effect across the industry domain. ML forms the underlying platform for several new developments. Hence, it has also triggered the demand for ML experts. Acquiring Machine Learning skills can have catalytic impact on your professional growth. However, if you are new to the tech domain and want to learn Machine Learning for free, then in this blog, we will take you through the 3 best options to start your ML learning journey.

Why pursue a career in the ML domain?

  1. A lucrative career opportunity in terms of career growth and salary
  2. ML has become integral for several new developments
  3. The average salary of a Machine Learning expert in India varies between ₹ 3.7 Lakhs to ₹ 28.4 Lakhs (based on experience).
  4. There are a growing number of institutes offering Machine Learning Courses for free
  5. Even professionals from different niches can transition into the ML domain

How to Learn Machine Learning?

Any individual who wishes to excel in the Machine Learning domain can follow these basic steps.

Start With the Basics

To start your Machine Learning journey, it is significant to have familiarity with the fundamental concepts of Machine Learning. Expertise in algebra, calculus and statistics is a must. This makes it easier for you to understand the algorithm and the different techniques used in Machine Learning.

Programming Language

As a Machine Learning professional, having knowledge of coding in different programming languages can be an added advantage. You can choose between Python or R programming languages.

Explore Online Resources

While there are several e-learning platforms providing Machine Learning courses, there are many that offer these programmes for free. So for the beginner who wants to gain insight into Machine Learning concepts, enrolling for these online programs can be a good start.

Practice with Datasets

In addition to mastering Machine Learning concepts, practising what you have learnt is significant. You can take the data sets available and start preparing the projects on the same. This will eventually help in building up your portfolio.

Join Communities

Networking is also important in making your transition to Machine Learning easier. Being a part of an online community is like Reddit or GitHub where you can get a sneak peek into the latest development in the Machine Learning domain.

How to learn Machine Learning for free?

Choosing the Right Learning Platform

While we have discussed the important steps that you need to take to start your Machine Learning journey, it is equally significant to register on a credible platform. Here we will be highlighting 3 such platforms where you can access Machine Learning courses:

Unsupervised Learning Course by Coursera in Collaboration with IBM

It is one of the most recognized Machine Learning courses focusing on Unsupervised Learning. As a part of this course, you will learn how to draw insights from the data sets. Moreover, you will also learn the use of clustering and dimensionality reduction algorithms. This course is useful for Data Scientists who are keen to expand their expertise in ML.

Skill Level – Data Scientists

Course Content

  • Introduction to Unsupervised Learning and K Means
  • Distance Metrics & Computational Hurdles
  • Selecting a Clustering Algorithm
  • Dimensionality Reduction
  • Nonlinear and Distance-Based Dimensionality Reduction
  • Matrix Factorization
  • Final Project

An Intuitive Machine Learning Program by Pick.AI

Anyone planning to build a career in the tech domain and wanting to explore more about Machine Learning concepts can enrol on this Machine Learning course for free. The course is provided by Pickl.AI powered by TransOrg Analytics. This e-learning module has been curated to give you a comprehensive overview of the concepts of Machine Learning and its application. The course is suitable any individual who wishes to learn more about Machine Learning and allied concepts.

Skill Level – Anyone who wants to learn Machine Learning

Course Content

  • Feature Selection
  • How does a model learn?
  • Measuring Performance
  • Model Performance
  • Bias-Variance
  • Visualisation
  • Feature Engg Demo
  • Feature Scaling

Introduction to Machine Learning with R by Simplilearn

This is a new introduction to the Machine Learning domain, but the course has received accolades from the learners. As a part of this course, you will learn about programming languages like R, SVM, decision trees, random forests and other concepts of ML.

Skill Level – Beginner

Course Content

  • Introduction to Unsupervised Learning and K Means
  • Selecting a clustering algorithm
  • Dimensionality Reduction

This brief discussion on Machine Learning courses, its growth and how you can excel in this domain would have thrown light on how this technology is expanding. If you want to explore more about the prospects of Machine Learning, connect with Pickl.AI.


What is Machine Learning Course Fees?

There are several online and offline platforms offering Machine Learning courses. The fees for the same may vary based on the curriculum and the duration of the programme. However, if You Are a beginner in this domain and want to acquire fundamental expertise in Machine Learning concepts, then you can join the Intuitive Learning Program on Machine Learning Pickl.AI.

Is ML Coding Hard?

Having coding expertise for Machine Learning is a debatable topic. However, a basic understanding of programming can help you optimise the algorithm. Thus, simplifying your learning curve.

What Are The Job Responsibilities of an ML Engineer?

Some of the key job responsibilities of an ML Engineer include the following:

  1. Transforming Data Science prototype
  2. Designing ML system implementation on of ML algorithm
  3. Choosing the right datasets and representation models
  4. Running ML testing


  • Aishwarya Kurre

    Written by:

    I work as a Data Science Ops at and am an avid learner. Having experience in the field of data science, I believe that I have enough knowledge of data science. I also wrote a research paper and took a great interest in writing blogs, which improved my skills in data science. My research in data science pushes me to write unique content in this field. I enjoy reading books related to data science.