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In Career Last updated: May 2, 2023
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If data is something that intrigues you, then stepping into Machine Learning is no doubt a rewarding career because today’s world runs on data, resulting in the growing demand for Data Scientists and Machine Learning experts.

Indeed says, an average base salary of a Machine Learning Engineer in the United States is $152,466, and if you work for big brands like eBay, Snap Inc, or Cruise, then it can go over $200,000 per year.

According to the Future of Jobs Report published by the World Economic Forum, machine learning is expected to be one of the world’s most in-demand skills through 2025.

Wondering where to learn Machine Learning? I know the scroll doesn’t end when you search for a roadmap to learn Machine Learning or resources to learn Data Science on Google.

However, taking up a well-organized course to learn any skill is crucial for mastering it effectively, and Machine Learning is no different. So, I’ve curated a list of the best machine Learning courses to learn from experts.

How to Get the Most Out of Your Online Course?


If you choose to learn online, then these tips are worth following.

Self-Motivated: Learning online demands great self-discipline to see the course through to the end. Since online courses lack the accountability of traditional classes, I suggest you stay responsible for your progress to get going with the learning.

You can achieve this by sharing your progress with others, such as posting your achievements on social media or telling your friends about your moves in the course.

Join the Discussion: Talk to your course peers about your learnings and ask them about their mistakes while doing the course and suggestions if they are ahead in the course. This will help you avoid common learning pitfalls and master the material faster.

Ask Doubts: Some of the online courses come with doubt-clearing sessions, and others provide you with an instructor’s email to contact them for queries. Be an active learner and take help whether you are stuck on an assignment to solve or a concept to crack.

Time Management: Setting short-term goals is a way to go to reach your destination. So, set some weekly goals and decide on the exact amount of coursework to complete each day. This way, you can track your progress and finish the course on time.

Develop the necessary industry-ready skills and knowledge with one of the top online courses in machine learning. Let’s check out these courses now!

Machine Learning Specialization

YouTube video

Build a solid foundation of AI fundamentals and explore practical Machine Learning skills with this ML specialization offered by Stanford on Coursera.

Taught by Andrew Ng, Founder of DeepLearning.AI and co-founder of Coursera. On top of these, he is a Professor at Stanford University. I guess his bio alone can convince you to enroll in this course.

This specialization is a 3-course program, beginning with Supervised Machine Learning, that teaches you basic and derived supervised learning algorithms, paving the way for a solid foundation in supervised learning.

Building upon that, the next one is about Advanced Algorithms focused on building neural networks and multi-class models. And finally, the last course – Unsupervised Machine Learning, delves into clustering and helps you build recommender systems.

What you’ll learn?

  • Regression
  • Classification
  • Advanced ML algorithms
  • Artificial Neural Network
  • Recommender Systems
  • Tensorflow

Machine Learning with Python

machine learning with python

Machine learning with Python from IBM will teach you various Machine Learning algorithms and their implementations in Python.

This course is part of major IBM certification programs on data science, including IBM Data Science Professional and IBM AI Professional. Saheed Aghabozorgi, Sr Data Scientist (expert in developing advanced analytic methods) at IBM, and Joseph Santarcangelo, Data Scientist at IBM, are the instructors for this course.

With an overall rating of 4.7 out of 5 from over 13k learners, this best Machine Learning course is a go-to choice for many data enthusiasts and students.

Thanks to the last module of the course! you’ll get a chance to gain hands-on experience through the project included in it.

What you’ll learn?

  • Machine Learning
  • Python
  • SciPy and scikit-learn
  • Regression
  • Classification
  • Hierarchical Clustering

Introduction to Machine Learning

udacity ml course

Introduction to Machine Learning covers everything a beginner or an intermediatory data professional should know.

This introductory course is a part of the Data Analyst nanodegree by Udacity. So take up this free and best Machine Learning course to decide whether the nano degree is worth your time and investment.

This course is a complete bundle that guides you in the end-to-end Machine Learning lifecycle, including investigating data, extracting relevant features, choosing the best ML algorithm, and testing the model performance.

The good part is that the course doesn’t just throw theories at you and expect you to absorb them like a sponge but rather shows you practical use cases for intuitive learning.

What you’ll learn?

Machine Learning in Production

coursera ML course

Intro to Machine Learning in Production is the first course in the MLops specialization, in which each course is focused on each aspect of deploying ML models into production.

Understanding Machine Learning and Data Science is important, but efficiently scaling your work to production will give you a competitive edge. If you are someone who loves data and deployment, then this course might be the one you are searching for.

The course is more focused on ML deployment systems and creating strategic models that run seamlessly in production. Also, you’ll see how to build and run integrated ML systems in production at minimum cost and maximum efficiency.

Remember Andrew Ng? author of the ML specialization course in this list. Well, you’ll be pleased to know that the same data expert taught this course as well.

What you’ll learn?

  • ML lifecycle and deployment
  • Model selection and training strategies
  • Model evaluation
  • Concept drift
  • Model baseline
  • Deployment challenges
  • Project scoping and Design

Python for Data Science and ML

best machine learning course

Udemy is the most popular and affordable e-learning platform, with over 50 million learners worldwide.

When you search for the best Machine Learning course on Udemy, then Python for Data Science and ML Bootcamp definitely tops the results.

This is a 25-hour course created by Jose Portilla, Head of Data Science for Pierian Training. Interestingly, Some Salesforce, Starbucks, and McKinsey folks are his students.

The course introduces you to Python programming and then takes you into data analysis and visualizations using Python and now steps into the core Machine Learning algorithms, implementing each on a practical use case.

What you’ll learn?

  • Python Programming
  • Pandas for data analysis
  • Seaborn for visualizations
  • Implementing ML algorithms
  • NLP
  • Neural networks
  • Intro to big data

Machine Learning Crash Course

YouTube video

Math fundamentals and Python syntax are enough to kick off this excellent crash course on Machine Learning from Google developers.

You don’t see a single instructor showing up in every module of the course. Instead, a team of 2-3 Google experts delivers the content, allowing them to teach their areas of expertise in this vast field of ML.

The course is a 15-hour package of 25 lessons, 30+ assignments, and real-world case studies with interactive visuals. So, In this course, you will use Machine Learning by applying it in real-time various case studies and hands-on practice assignments.

This Google Developers learning platform not only offers you advanced courses for solving a variety of Machine Learning problems but also includes specialized courses for decision trees, clustering, recommendation systems, image classification, etc.,

What you’ll learn?

  • ML concepts
  • ML algorithms
  • Real-world case studies
  • ML engineering techniques

Machine Learning CS229

best machine learning course

Machine Learning CS229 is a 2-3 months intensive academic program from the Stanford School of Engineering that costs you between $4k to $6k.

Since it’s a live course, you will not only be taught regular ML concepts but also about recent research on Machine Learning and the latest real-world implementations.

As of this article, Tengyu Ma, assistant professor of computer science and statistics at Stanford, and Christopher Ré, associate professor in the Stanford AI Lab, are the instructors.

Pre-requisite standards are a little higher for this course. You will need a bachelor’s degree with a GPA of over 3. Also, the ability to program in Python and a basic understanding of Numpy and Pandas are preferable. Moreover, knowledge of Calculus, Algebra, and Probability is required to quickly grasp the depth of concepts explained.

What you’ll learn?

  • Supervised learning
  • Clustering
  • Statistical pattern recognition
  • Dimensionality reduction
  • Neural Networks
  • Real-world ML applications

Machine Learning Foundations

Best ml course online

Machine Learning Foundations is a seven-module course from the University of Washington that begins with a strong intro to ML and how it’s transforming the world, then enters into core technicalities with regression, continues with clustering, and ends with a dedicated module on Deep learning.

Emily Fox, Amazon Professor of Machine Learning at the University of Washington, is the lead instructor and will be present throughout this course.

By the end of this course, you’ll learn how to extract house-level features, sentiment analysis based on customer reviews, recommendations for products, an efficient search of images, and many more by building a real-world house prediction machine learning system. You can apply these learnings to a wide range of ML problems to solve them with ease.

But, installing and working with Graphlab was challenging for many learners. Also, the Python version used in this course is outdated now, causing compatibility issues.

What you’ll learn?

  • Python basics
  • Machine learning concepts
  • Deep learning
  • Clustering
  • Recommendation systems

Data Science: Machine Learning

edx ML course

Data Science course from Harvard teaches you Machine Learning by walking you through every phase of building a movie recommendation system. This course is a part of Harvard’s professional Data Science certification program.

You will see about training data, building predictive relationships, overtraining cases, cross-validation, and much more. This helps you build intuition to create recommendation systems for e-commerce platforms, OTT streaming platforms, New websites, etc.,

This training will cost you around $100 with unlimited access to course materials. However, it comes with a free edition where you get limited access to material and no graded assessments to test your progress.

Rafael Irizarry, Professor of Biostatistics at Harvard University, taught this course.

What you’ll learn?

  • Machine learning algorithms
  • Principal component analysis
  • Regularization
  • Movie recommendation system
  • Cross-validation

Final Words

Mastering machine learning is challenging but achievable with the list of the best machine learning courses mentioned in this article. Whether you are a beginner who wants to build fundamentals in ML or an ML engineer looking to level up your skills, this list has got you covered.

However, if you are serious about building a career in ML, do not put a stop to when the course is done. Take your course knowledge and implement it in projects. Moreover, keep yourself updated with the technology by delving into research papers.

You may also check these PyTorch resources to level up your data skills.

  • Srujana Maddula
    Srujana is a freelance tech writer with the four-year degree in Computer Science. Writing about various topics, including data science, cloud computing, development, programming, security, and many others comes naturally to her. She… read more
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