We’ve looked into many online Machine Learning courses and come with the list of the best online course to learn Machine Learning. This list has online Machine Learning courses that are suitable for beginners, intermediate, and advanced learners.

Let’s look get started with the list of the best online course to learn Machine learning.

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## Best Online Course to Learn Machine Learning

Here we’ve listed the Machine Learning course at every level – beginners, intermediate, and advanced course. So let’s get started.

## Best Machine Learning Course for Beginners

Here we’ve listed the best Machine Learning course for beginners. Enroll in any of the below courses to begin your journey in Machine Learning.

### 1. Machine Learning for All – Coursera

We all know Machine Learning is the popular on-demand skill out there. Hence more and more people are subscribing to the Machine Learning classes. This Machine Learning for All course is designed by the University of London and available for enrolling in the Coursera platform.

Since Coursera is the most trusted platform to get on-demand job-ready skills, it has many ML courses. And among them, this is the best online course to learn Machine Learning. However, it is not easy to learn Machine Learning as it involves advanced mathematics and programming skills to master it.

In the Machine Learning for All course, you will understand the basic idea behind these complexities that everyone is talking about. You will get all the theory video lessons on ML and also hands-on, user-friendly labs. This course will take you to the first step into the Machine Learning career.

**Key takeaways of this course:**

- A beginner Machine Learning course that anyone can sign up.
- You will gain practical experience in training a Machine Learning model.
- Learn how the representation of data affects the models in Machine learning.
- You will do your ML project by collecting data, training a model, and finally testing it.

Machine Learning for All course covers a syllabus for four weeks with a total duration of 10 hours to complete the course. This is one of the best beginner course in Machine Learning.

### 2. Mathematics for Machine Learning Course – Coursera

This is the prerequisite course for every Machine Learning course, and it is one of the best online course to learn Machine Learning prerequisites. The two requirements to learn Machine Learning is knowing Python and basic math.

Mathematics for Machine Learning course is the required course that you can sign up to and learn all the math topics needed for Machine Learning. It covers Linear Algebra, Multivariate Calculus, and basic math to derive PCA (Principal Component Analysis).

This course consists of 3 courses where first and second doesn’t need any background knowledge, but basic knowledge on Python would be helpful. For course 3, it is recommended that you know Python so that you can finish the assignments with ease. If you don’t know where to start learning the Python programming language, you can refer to this guide.

**Key takeaways of this course:**

- Work with vectors and matrices in linear algebra and how it relates to data.
- Learn the foundation of calculus and how vectors and matrices can be implemented.
- In the third course, you will use both linear algebra and calculus and work with Principal Component Analysis.
- It also includes graded assignments to help you learn quickly.

The average rating of this course is 4.5 out of 5, voted by over 9k learners. This course can be completed in about two months. Then you can sign up for one of the Machine Learning course.

### 3. Machine Learning with Python – Coursera

Machine Learning with Python course is developed by IBM, which guides you on the basics of Machine Learning using Python. Here, Python is the obvious choice because of its easy syntax and simple program to learn.

IBM Machine Learning with Python course is the best online course to learn Machine Learning for beginners. This course has a Machine Learning syllabus for beginners that covers for six weeks. It also comes with a final capstone project where your peer will evaluate your final project.

The advantage of Machine Learning with Python course is that after completing, you’ll be getting a certificate from Coursera and a digital badge from IBM. You can showcase both the credentials with your employer and in a professional network.

**Key takeaways of this course:**

- You will see the real-world examples of Machine Learning and where it is used.
- Understand the purpose of ML and how it is used.
- Learn the basics terms of ML, such as model evaluation, supervised vs unsupervised learning, and ML algorithms.
- Clear all your doubts by reaching out to your instructor.
- Earn new Machine Learning skills to add to your resume.

This course is intended for beginners where they learn the foundations of Machine Learning concepts. Over 6k students have rated this course with an average rating of 4.7 out of 5. The approximate duration to complete this course is 18 hours.

### 4. Introduction to Machine Learning by Duke University – Coursera

You can get started with the Machine Learning course with Introduction to Machine Learning that teaches from the ground level of Machine Learning. It will show you everything about the latest ML models, such as – convolutional neural networks, multilayer perceptrons, natural language processing, and logistic regression.

You will solve complex problems by learning to demonstrate the above models. This course also consists of a few of the practice exercises in Machine Learning, where it will provide hands-on labs to implement the data science models on data sets. Also, you will learn to apply the ML algorithms using TensorFlow. TensorFlow is the popular ML library developed by Google.

After going through the introduction of Machine Learning classes, you will then proceed to learn the basic model building, image analysis with CNN, natural language processing, and reinforcement learning.

**Key takeaways of this course:**

- Study the basic concepts of ML models.
- Learn to use the popular Machine Learning library, TensorFlow.
- Understand the mathematical basis of learning deep networking.
- Learn to fine-tune the Convolutional Neural Networks in this ML tutorials.
- Learn everything about Q learning in this popular ML course.

Machine Learning course by Duke University has a curriculum of 5 weeks with a total duration of 16 hours. Hence, you can complete this course in a week or two. Over 200 learners have awarded it an average rating of 4.6 out of 5 in ML.

### 5. Machine Learning for Business Professionals – Coursera

Google Cloud developed Machine Learning for Business Professionals course. This course is aimed at business professionals who have heard the hype behind Machine Learning but don’t know where to start. This course can be taken even by non-technical business professionals.

There is a common misconception among many people that to learn ML, one should know many technical aspects. But Google instructor has cleared that misconception where you do not need to have the professional background to take this course.

In this course, you’ll learn to formulate the ML solutions to solve real-world problems and build the ML model from scratch. This course has Qwiklabs to teach you the practical of Machine Learning.

**Key takeaways of this course:**

- One of the best ML course for beginners.
- You will be able to train, evaluate, and deploy the ML models.
- Understand if you’ve sufficient data for Machine Learning.
- Discover the use cases of the ML.
- You will be successful at Machine Learning.
- Build a Machine Learning model from scratch.

This course covers over 16 hours of training, which you can complete by 2- 3 weeks. The average rating of this course is 4.6 out of 5 by over 300 users.

### 6. Machine Learning by Stanford University – Coursera

Machine Learning course by Stanford University is one of the popular courses in Coursera, and you can enroll in this Machine Learning course for free. This is one of the best beginners course in Machine Learning, where you’ll learn the most effective techniques of Machine Learning.

In this class, you’ll not only learn the theory but also gain the practical knowledge to apply powerful techniques of Machine Learning. This course provides an introductory class on many-core topics of ML. Such as supervised learning, unsupervised learning, and study the best practice to follow in Machine Learning.

You’ll also learn to apply Machine Learning algorithms and build many tools in web search, perception, control computer vision, etc.

**Key takeaways of this course:**

- Learn all the key terminologies and core concepts.
- Understand the difference between parametric and non-parametric algorithms.
- This course offers real-world case studies in Machine Learning.
- Access to community of learners in Machine Learning and interact with them to learn effectively.
- Learn the best practice to follow in ML.

This course is the detailed beginner’s course in Machine Learning with over 11 weeks of curriculum. It has over 50 hours of study materials. This course has been rated 4.9 out of 5 stars by over 100k students.

### 7. Introduction to Machine Learning – edX

This is the introductory Machine Learning tutorial offered by ITMO University. You will learn the modern ML methods and choose the right approach when necessary. After you learn the basic terminologies of Machine Learning, you’ll proceed to determine the required math topics for ML. No other ML course offers to teach you mathematics.

Here you’ll learn the various methods involved in clustering, classification, and regression methods. The only prerequisite for this course is that you know basic math and basic calculus. If you don’t know the basic math or need refreshing training, visit Khan Academy.

Anyone who knows basic math can get started with this Machine Learning course in edX. Also, check what you’ll be learning in this course with the below key takeaways.

**Key takeaways of this course:**

- Grasp the foundational knowledge of ML.
- It also teaches you the essential mathematics for ML.
- Learn different types of classification methods – Naïve Bayes, Logistic regression, and K-nearest neighbors.
- Understand the different types of clustering methods.
- You’ll also learn to work with data in Microsoft Azure.

The syllabus of the Introduction to Machine Learning course is for five weeks. Hence you can complete this course in 5 weeks. The ideal time to study is about 2-4 hours per week, which should be sufficient to learn and practice.

### 8. Machine Learning with Python: A Practical Introduction – edX

This course is a practical approach to Machine Learning with Python. We know that ML is an extremely beneficial skill set to predict the future. This ML course will provide all the tools you need to start with supervised and unsupervised learning.

Since Python is the easiest programming language, it is an obvious choice for beginners to start learning. By taking this course, you’ll learn how society is benefited from many real-world examples of ML. We thought this was the coolest feature, where you get to learn in such an effective way.

This course is designed by IBM and one of the easiest Machine Learning course. Once you’re done with the theory part, you will start the practical hands-on labs. You’ll learn the many algorithms – Clustering, Regression, Classification, and Dimensional Reduction.

**Key takeaways of this course:**

- You’ll learn the key difference between supervised and unsupervised learning.
- Understand the different methods of supervised learning – classification and regression.
- Learn the different methods of unsupervised learning – Clustering and Dimensionality Reduction.
- Learn how you can compare ML and statistical modeling and how they relate.
- Understand how the ML affects our society.

Over 50k students have enrolled in this course. You can complete this course in about five weeks by dedicating 4-6 hours per week.

### 9. Machine Learning for Data Science and Analytics – edX

We all know that Machine Learning is one of the most sought after skill set by all the industry. This is a part of the Data Science course that introduces to Machine Learning. If you are currently pursuing the data science field and want to upgrade your career, then this would be the right course to get started.

This course will introduce ML concepts and algorithms to Data Scientist. By taking this course, you will understand the principles of ML and extract solutions using predictive analytics. Also, learn how algorithms play a vital role in Big Data analysis.

You can enroll in this course if you’re aware of the high school math level and some experience in the programming language.

**Key takeaways of this course:**

- Learn how ML is related to data analysis and statistics.
- Understand how algorithms are used by ML to search patterns in data.
- Learn to prepare data and deal with the missing data.
- Learn to create custom data solutions for various industries.
- Using topic modeling, uncover hidden gems in large collections of documents.

You can complete this course in 5 weeks by dedicating 7-10 hours a week. More than 130K students have enrolled in this course and learning it actively.

## Best Machine Learning Course for Intermediate Students

Here is the list of the best online course to learn Machine Learning for intermediate learners. This ML course is for those who already know its concepts, terminologies, Python, and basic math.

### 10. Machine Learning by University of Washington – Coursera

This particular Machine Learning course by the University of Washington is one of the best online course to learn Machine Learning. This course is available for enrolling from Coursera. This specialization course consists of 4 courses.

To enroll for Machine Learning by the University of Washington, you should have background knowledge of Python programming language and basic math. This course is suitable for anyone who is interested in pursuing a career as a Data Scientist or in Machine Learning.

This course covers various topics of Machine Learning, starting from the Machine Learning foundation, which follows the case study approach. You’ll also gain the applied experience of ML in Prediction, Classification, Clustering, and Information Retrieval.

**Key takeaways of this course:**

- Case study approach which will teach the foundation of Machine Learning in a better way.
- Learn to describe the input and output of the regression model.
- You will learn to apply clustering, classification, regression, retrieval, and deep learning.
- Using Machine Learning, you will learn to build an end-to-end application.
- It comes with a hands-on interactive lab to learn ML effectively.

Over 18k students have rated this course at 4.7 out of 5. This is a comprehensive and detailed course, and it would take about eight months to complete the course.

### 11. Applied Machine Learning in Python – Coursera

The University of Michigan has designed the Applied Machine Learning in Python course. This course will introduce you to the field of applied Machine Learning, which will focus more on the methods and techniques on the statistics. It will teach you how ML is different than the statistics and guide you to use scikit-learn through their tutorial.

Applied Machine Learning in Python will teach you Machine Learning with the introduction to the scikit-learn library. This course should only be taken if you’ve taken this Introduction to Data Science in Python course or have similar knowledge.

By the end of this course, you will be able to tell the difference between classification (supervised) and clustering (unsupervised) technique. This is one of the best online course to learn Machine Learning, which falls under the intermediate level of Machine Learning course.

**Key takeaways of this course:**

- Provide an introduction to the scikit-learn python library and how to implement it in Machine Learning.
- Learn to control model complexity by following the best techniques like regularization to avoid overfitting.
- Learn to optimize the performance of your ML models.
- Understand how you can detect data leakage in ML and how you can avoid it.

This course has a total duration of 24 hours of content and covers the syllabus for four weeks. It has been rated to be 4.6 out of 5 by over 800 students.

### 12. Data Science: Statistics and Machine Learning Specialization – Coursera

Johns Hopkins University developed this Machine Learning course. This course is the continuation of another course, Data Science: Foundations using R. If you’re currently pursuing or already have similar knowledge in Data Science, then this is the best online course to Learn Machine Learning.

It will cover many topics of Machine Learning, which will help you to succeed in the field of Data Science. Such as regression models, statistical inference, and development of data products. This specialization course comes with five courses.

At the end of the course, there is a capstone project where you’ll apply all the skills that you learn in this Machine Learning course. And after completing, you will earn the course certificate.

**Key takeaways of this course:**

- Learn different modes of statistical inference, such as data-oriented strategies, data modeling, and randomization in analyses.
- Understand the basic concepts of training and test sets, overfitting, and error rates.
- Covers the full process of building prediction functions.
- This course focus on the statistical fundamentals of creating a data product.

This course is 100% online and would take about six months to complete the course. It is rated at 4.4 out of 5 by over 12k students.

### 13. Machine Learning: Algorithms in the Real World Specialization – Coursera

This Machine Learning specialization course is for those who want to learn and apply it to data analysis and automation. It can be engineering, finance, medicine, business, or any other domain. It will teach you to define, train, and manage the ML application.

However, this course should be taken if you’ve some knowledge of Python, analytics, math, and statistics. If not, please take the beginner courses in Machine Learning that we’ve have highlighted in the earlier section.

This course comes with four courses, which will teach you everything about building a machine learning project. It also has some of the hands-on labs to guide you effectively.

**Key takeaways of this course:**

- You’ll be able to define the ML problem with more clarity.
- Able to prepare data for Machine Learning applications.
- Scale your business by applying ML techniques.
- You will also demonstrate the accuracy of your model and improve it further.
- Foresee the common problems in the applied Machine Learning.

This course has been rated 4.7 out of 5 by over 200 students. It will take around two months to complete this course.

### 14. Machine Learning with TensorFlow on GCP – Coursera

Machine Learning with TensorFlow on the GCP course starts from the video lesson from the basics of ML. If you have some programming knowledge or at the least have an interest in ML, then this course is right for you. It teaches everything that you need to know.

This course consists of 5 courses from introduction classes to launching your own Machine Learning models. To get the most out of this course, it is advised that you know Python programming language or at least the basic concepts.

The instructor of this course will also teach you TensorFlow, the most popular library for Machine Learning. It also features Qwiklabs that provide interactive labs to learn the ML more effectively. Not only you learn TensorFlow and Machine Learning, but you deploy them on Google Cloud. This adds various skills set to your resume.

**Key takeaways of this course:**

- Learn all kinds of Machine Learning and scale up your career in Machine Learning.
- You will understand to use the TensorFlow to build ML models.
- Able to build Machine Learning models on Google Cloud Platform.
- Train the TensorFlow model in the Google Cloud infrastructure.
- Learn to optimize the ML model on GCP.

According to Coursera, the course duration is one month. Over 12k students have rated this course at 4.5 out of 5.

### 15. Principles of Machine Learning: R Edition – edX

Microsoft has developed the intermediate level of Machine Learning course, Principles of Machine Learning: R Edition. As the name suggests, this follows the learning path of using R and Azure Notebooks.

Here you’ll learn to build Machine Learning models using the R to derive insights. This is the self-paced study that has a prerequisite of R and basic math. This course offers combined teaching of both practical and hands-on labs.

Microsoft’s Principles of Machine Learning: R Edition also has some of the Data Science topics that involve building, validating, and deploying ML models using R.

**Key takeaways of this course:**

- Introduction video lessons on Machine Learning using R.
- Learn about data cleaning, exploration, and preparation.
- Learn both the supervised and unsupervised techniques in this special edition ML course.
- You’ll also learn to improve the performance of ML models.

This Microsoft course has been enrolled by over 16k students and would take about six weeks to complete the course.

### 16. Machine Learning for Trading Specialization – Coursera

Machine Learning for Trading Specialization is developed by Google Cloud and comes with three courses in this specialization certificate. This Machine Learning course is intended for finance professionals, hedge fund traders, analysts, day traders, and anyone who is interested in effective trading strategies.

You’ll learn to create many trading strategies using Python. Hence, it is recommended that you know the fundamentals of Python. Along with Python, you should know mathematics in standard deviation, Gaussian distributions, higher moments, probability, linear regressions. We have a detailed guide on some of the best online course to learn linear regressions.

Choose this course if you’re looking to implement excellent trading strategies that will make some money.

**Key takeaways of this course:**

- Understand the basic concepts of trading, including trend, returns, stop-loss, and volatility.
- Build ML models using Keras and TensorFlow.
- You will use Google Cloud to build the basic ML models.
- Utilize Reinforcement Learning techniques to build trading strategies.
- Offers a flexible schedule in Coursera to learn Machine Learning courses.

The average rating of this course is 3.8 out of 5 and rated by over 400 students. The approximate duration to complete this course is one month.

## Best Machine Learning Course for Advanced Students

Below is the list of advanced Machine Learning courses that you should only take if you’ve already taken one or more of the above courses or have similar knowledge. Here you’ll see the list of the best online course to learn Machine Learning for advanced learners.

### 17. Advanced Machine Learning with TensorFlow on GCP – Coursera

This is the advanced Machine Learning course developed by Google. To sign up and succeed in this course, you must have taken the above course (Machine Learning with TensorFlow on GCP) or have equivalent knowledge. This advanced ML course comes with five courses.

Here you’ll be learning end-to-end techniques of Machine Learnings with their workshop. It comes with production-ready tasks that are designed to teach you ML concepts in an effective way. As the earlier course used TensorFlow, this advanced ML course also uses TensorFlow to build ML models on Google Cloud Platform.

You will learn many critical advanced topics in ML that you can implement in your daily work. This will boost your career to the next level for sure.

**Key takeaways of this course:**

- Upscale your current Machine Learning career to the next level.
- Learn the advanced techniques of TensorFlow.
- Understand how you can create the dataset to build an ML model using TensorFlow.
- Know how to leverage the hybrid machine learning model.
- Understand how the encoder-decoder network will solve many tasks.

Advanced Machine Learning with TensorFlow will take about two months to complete the course. It has been rated 4.5 out of 5 with over 2.5k ratings. This is one of the best online course to learn Machine Learning.

### 18. Advanced Machine Learning Specialization – Coursera

This is the most advanced and detailed course in Machine Learning. It has over seven courses in this specialization certificate. Many top Kaggle Machine Learning experts and CERN scientist will share their knowledge to solve real-world problems. They will also provide more insight by helping you fill gaps between theory and practice.

The seven courses include an introduction to deep learning, reinforcement learning, natural language understanding, computer vision, and Bayesian methods. Since it covers many advanced topics, it is definitely the most advanced course in the Machine Learning that we’ve come across.

The course starts by revising some of the popular concepts that you should know before proceeding with this course. The prerequisites of this course are Python, calculus, linear algebra, probability theory, and basic ML decision trees.

**Key takeaways of this course:**

- Learn the big blocks of neural networks such as connected layers, convolutional, and recurrent layers.
- From various sources, learn to pre-process the data and generate new features.
- Learn to apply Bayesian methods, which allow you to compress the models a by a hundredfold.
- You’ll also learn computer vision, which is a breakthrough in self-driving cars.
- Also, learn some of the critical topics covered in Natural Language processing.

This advanced Machine Learning course will take about 8 to 10 months to complete the course. Since ML is still new for many people and has gained momentum in recent years, not many people have learned the advanced topics in ML. This is also one of the reasons where this course has not been rated by any students.

### 19. Machine Learning by Columbia University – Edx

The Machine Learning course by Columbia University is part of the MicroMasters Program in edX. This is the most comprehensive Machine Learning course, that is the reason we’ve listed it in the advanced section of the ML course.

This course covers many basic methods to perform a task and optimize algorithms. Most of the learnings is a practical approach and less theory. The prerequisite of this course is linear algebra, calculus, probability, statistical concepts, and data manipulation.

The major topics that are covered here are probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning. You should consider taking this course only if you know all the prerequire of this ML course.

**Key takeaways of this course:**

- Understand the viewpoints of Probabilistic versus non-probabilistic.
- Learn the techniques for regression and classification.
- Also, you’ll see unsupervised learning techniques for data modeling and analysis.
- You’ll also learn some of the AI topics.

A comprehensive and significant course which will take about three months to complete the course: and over 141K students have enrolled in this course so far.

### 20. Predictive Analytics using Machine Learning – edX

This course gives you an overview of ML-based approaches. Some of them are tree-based techniques, predictive modeling, support vector machines, and neural networks using Python. The University of Edinburgh developed this course. An excellent course in the advanced ML course.

Predictive Analytics using Machine Learning provides 2 case studies; they are:

- Forecasting customer behavior after a marketing campaign
- Flight delay and cancellation predictions.

You should enroll in this course if you have experience in procedural programming language and have a strong background in statistics and mathematics. You’ll learn the sampling techniques, such as bagging and boosting. This will improve robustness, predictive power, and random forests.

**Key takeaways of this course:**

- You will learn about decision trees used in Machine Learning.
- Learn the difference between ML and statistical models.
- You will practice building tree-based models.
- Understand the common pitfalls of the neural network.
- Learn to apply ML models in the business.
- This advanced Machine Learning course will take six weeks to complete.

### 21. Advanced Machine Learning Fundamentals – edX

This advanced Machine Learning Fundamentals course is provided by The University of California, San Diego. This course is part of the Data Science MicroMasters program. It features many topics related to Machine Learning in terms of Data Science. Along with supervised and unsupervised learning, you’ll also learn the theory behind those algorithms.

It teaches you the Machine Learning with real-world case studies. Using the case studies, you’ll be able to understand how to classify images, partition people based on a different personality, identify main topics in a document, and capture the semantic structure of words.

The assignment in this advanced Machine Learning course features it in the Python programming using Jupyter notebooks. Since the course is an advanced level, it would help if you are familiar with Python language.

**Key takeaways of this course:**

- Advance your Data Science Career with this ML course.
- Build various projects using Python language based on ML models.
- Learn to represent data adequately so that you can use it in Machine Learning models.
- Be able to build ML models using Python.
- Understand the best practice of building Machine Learning models.

This course is detailed and comprehensive, that it takes about ten weeks to complete the course. More than 70k students have enrolled in this course.

### 22. Advanced Machine Learning by ITMO University – edX

In this Advanced Machine Learning by ITMO University, you’ll learn to particular techniques to analyze a vast amount of data. This involves some advanced methods of Machine Learning. Some advanced topics of ML that you discover here are multiclass logistic regression, factor analysis, support vector machines, resampling and decision trees, and reinforced machine learning.

To enroll in this course, you need the essential knowledge of at least the beginner’s experience of Machine Learning. Here you’ll learn to build decision trees, which is an intuitive concept for making decisions.

It features many examples and various software applications to teach the course more effectively. You’ll also learn how you can work with your data in Microsoft Azure.

**Key takeaways of this course:**

- Consider different methods in analyzing.
- Learn about Multiclass logistic regression and use them when there are more two possible outcomes.
- You will consider a more intuitive and straightforward classifier called the optimal margin classifier.
- Learn to use reinforcement learning in Machine Learning to maximize the cumulative feedback of an object’s actions.

It covers all the advanced topics in ML in about five weeks. Hene, make sure that you’ve about 2-4 hours per week to take time and study.

## Summary

We hope that you loved our recommendation on the best online course to learn Machine Learning. The list has all the Machine Learning courses for every level – beginners, intermediate, and advanced level.

If you come across any Machine Learning course that is worth mentioning, please let us know by leaving a comment, and we’ll make sure we update our content. And make it relevant for new visitors.

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