Learn machine learning in 2021 from the top courses out there. 

Branched from statistics, Machine Learning has become one of the most sought-after computer science disciplines in 2021. From supervised learning to smart robotics, it is an invaluable asset, and investment, to individuals pursuing a job in machine learning. 

We’ve put together a list of the best Machine Learning courses in 2021 to help beginners, intermediate and advanced individuals choose the ones that best fit their needs and the most influential people in the field. So let’s get started.


Top Machine Learning courses of 2021

  1. Python for Everybody Specialization- Coursera (University of Michigan)
  2. Machine Learning offered- Coursera (Stanford)
  3. Machine Learning Specialization- Coursera (University of Washington)
  4. Deep Learning Specialization- Coursera (Deeplearning.ia)
  5. Machine Learning A-Z™: Hands-On Python & R In Data Science- Udemy
  6. AI Applications for Growth- Kellogg
  7. Machine Learning: From Data to Decisions- MIT
  8. Machine Learning, Data Science and Deep Learning with Python
  9. Machine Learning Crash Course- TensorFlow APIs

Now let’s go in-depth over each Machine Learning course with information regarding each course, pricing, difficulty level, and so on.

9 Of The Best Machine Learning courses of 2021

  1. Python for Everybody Specialization- Coursera (University of Michigan)

Python for everybody course

Description: This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

Rating: 4.8 stars out of 5 based and 2.4 million students have taken it

Difficulty: Beginners without coding background

Instructors: Charles Russell Severance

Duration: Approximately 8 months to complete

Cost: 7-Day free trial and $49 USD monthly after the trial expires

  1. Mechanics of Machine Learning (Stanford Online)

Machine learning course

Description: The Machine Learning MOOC offered on Coursera covers a few of the most commonly used machine learning techniques. XCS229i explores these concepts in greater depth and complexity, in addition to several other concepts. XCS229ii will cover completely different topics than the MOOC and include an open-ended project. You may gain a better sense of comparison by examining the CS229 course syllabi linked in the Description Section above and the course lectures posted on YouTube. This course has dense math materials so being proficient in linear algebra is important. 


Difficulty: Advanced

Instructors: Andrew Ng

Kian Katanforoosh, Adjunct Lecturer of Computer Science

Anand Avati & Raphael Townshend, CS229 Head TAs

Duration: 10-weeks

Cost: $1,595

  1. Machine Learning Specialization- Coursera (University of Washington)

Machine learning specialization

Description: This Specialization from leading researchers at the University of Washington introduces you to the high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. This course is ideal for everyone with at least some understanding of matrix algebra and Python.

Rating: 4.7 stars out of 5

Difficulty: Intermediate

Instructors: Emily Fox- Amazon Professor of Machine Learning

Carlos Guestrin- Amazon Professor of Machine Learning

Duration: Approximately 7 months

Cost: 7-Day free trial and $49 USD monthly after the trial expires

  1. Deep Learning Specialization- Coursera (Deeplearning.ia)

Deep learning specialization

Description: The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. 

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

It’s suggested to have at least some knowledge behind Linear/Logistic Regression, cost functions, Gradient Descent, vectorized implementations. But if you don’t, the course will cover all of the required materials.

Rating: 4.9 stars out of 5

Difficulty: Intermediate

Instructors: Andrew Ng- Stanford University

Younes Bensouda Mourri- Curriculum developer

Kian Katanforoosh- Senior Curriculum Developer- DeepLearning.AI

Duration: Approximately 5 months

Cost: 7-Day free trial and $49 USD monthly after the trial expires

  1. AI Applications for Growth- Kellogg

AI Applications for growth

Description: Gain a comprehensive perspective on how AI is being applied in practice. Build a robust playbook that helps you frame AI initiatives, identify the most impactful business problems, map your AI Journey, and drive responsible outcomes. To help you understand Artificial Intelligence as the most important commercial opportunity of our lifetime and the potential it offers to your company, this program provides frameworks to help you build an effective AI implementation plan. By the end of this program, you will be able to do the following:

  • Understand the business applications and outcomes that can be achieved with AI.
  • Represent the voice of the business as well as the customer to data scientists and engineers.
  • Craft your AI journey, from strategy and capabilities to execution and organization.
  • Navigate the black box and ethical considerations of Artificial Intelligence to drive responsible AI initiatives.
  • Join a community of like-minded professionals who are successfully deploying AI in their organizations.

Rating: 4.9 stars out of 5

Difficulty: Intermediate

Instructors: Mohanbir Sawhney- Associate Dean for Digital Innovation; McCormick Foundation Chair of Technology; Clinical Professor of Marketing; Director of the Center for Research in Technology and Innovation

Duration: Approximately 2 months

Cost: $2,250

7. Machine Learning: From Data to Decisions- MIT

Machine learning: from data to decisions

Description: This program is not a program to learn how to code, but rather an introduction to the many ways that machine learning tools and techniques can help you make better decisions in a variety of situations. The program is organized into four key building blocks: Understanding Data; Prediction; Decision Making; and Causal Inference.

Representative industries include engineering, data science, IT, software, strategy, media, healthcare, manufacturing, and business intelligence

The program is designed for both technical and non-technical professionals. If you aspire to become a data scientist, have some experience with data science, or plan to work with a team of data scientists, this program will be beneficial for understanding the tools and techniques to help make sense of the data

Functional and cross-functional teams are encouraged to attend together, to accelerate the machine learning adoption process.

There are no prerequisites in terms of math or computational sciences, however, some experience with introductory-level statistics is helpful.

Rating: 4.9 stars out of 5

Difficulty: Beginner

Instructors: Devavrat Shah- Professor, Department of Electrical Engineering and Computer Science; Director of Statistics and Data Science Center, Massachusetts Institute of Technology

Duration: Approximately 2 months

Cost: $2,300

8. Machine Learning, Data Science and Deep Learning with Python

Machine Learning, Data Science and Deep Learning with Python

Description: If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry. 

Rating: 4.5 stars out of 5

Difficulty: Beginner

Instructors: Kirill Eremenko- Data Scientist

Hadelin de Ponteves- AI Entrepreneur

Duration: Approximately 2 months

Cost: $13.99 per month

9. Machine Learning Crash Course- TensorFlow APIs

Machine learning crash course

Description: With more than 18,000 Google engineers taking this course, and the first time it's been made free. Google’s Machine Learning Crash Course is the perfect route to understand concepts of Machine Learning and Artificial intelligence. It takes about 15 hours and uses TensorFlow, Google's open-source machine learning platform to complete. You must have an understanding of algebra and elementary statistics. If you understand calculus, you'll get a bit more out of the course, but calculus is not a requirement.

The benefits of TensorFlow greatly out-weighs the learning curve. With TensorFlow you have the ability to easily go from training on one machine to distributed, training on hundreds, even thousands of machines. TensorFlow is a full-stack machine learning solution along with distributed training. Even though TensorFlow is written in C++ and optimized for speed, the recommended way to create models is with Python. Even if you are not experienced in Python, you could still take this course. Many of the Google engineers who took this course didn't know Python but still completed the exercises. That's because you'll write only a few lines of code during the programming exercise.

Rating: N/A

Difficulty: Intermediate

Instructors: Google

Duration: Approximately 15 hours 

Cost: Free