10 Best Machine Learning Courses Online (Free & Paid)

Machine learning is a powerful subfield of Artificial Intelligence, focused on the development of algorithms capable of consuming and learning from data. Over the years, machine learning tools have grown increasingly valuable to almost every industry. The right solutions can assist with everything from business analytics tasks to financial forecasting.

Today’s machine learning training courses are emerging to educate new professionals with the skills they need to become machine learning experts. We’ve reviewed the most popular courses offered by reputable vendors to bring you our picks for the best machine learning courses you can take online.

10 Top Online Machine Learning Courses

1. Machine Learning Course

Part of a comprehensive “Machine Learning Specialization”, the Supervised Machine Learning course promises learners a complete insight into the power of machine learning models. Created in collaboration with Stanford Online and DeepLearning.AI, this educational experience is a beginner-friendly introduction to the fundamentals of machine learning.

Who is this for?

As a broad introduction to the capabilities of machine learning algorithms, this course is suitable for anyone with an interest in ML technology. Whether you’re planning on building your own algorithms or simply want to use machine learning to address real-world business problems, this could be the course for you.

If you’re looking to break into AI, these beginner-friendly lessons are great for those with minimal knowledge in the machine learning space.

However, it’s worth having a basic knowledge of coding (loops, functions, and if/else statements), and a strong capacity for high school math.

What you’ll learn?

The first course in the Machine Learning Specialization takes students through the steps involved in building models in Python with libraries like sci-kit-learn and NumPy. If you’re looking for the best machine learning courses to introduce you to building and training supervised ML models, this could be the solution for you.

Learners will create their own machine learning tools for binary classification tasks and prediction, covering topics like linear and logistic regression. You’ll also learn all about the difference between supervised and unsupervised learning. Lessons covered include:

• Managing regression with input variables
• Classification in supervised learning with regularization
• Implementing linear regression code
• Improving your model’s training and performance

How much does it cost?

This beginner course is available for free from Coursera; however, you will need to pay for your certificate. Financial aid is available for those who might struggle to pay for the certificate, and you can audit the course without the qualification at the end for free.

2. Data Science & Machine Learning Bootcamp

Designed for students who want to learn machine learning concepts like regression, classification, and the use of Neural Networks, the Data Science & Machine Learning Bootcamp is a comprehensive machine learning certification course.

With this course, you’ll get a full introduction to the practical aspects of building machine learning algorithms, and discover how to use common tools like Tensorflow.

Who is this for?

Ideal for those who learn best by doing, this course teaches you all the fundamentals of machine learning programming, by helping you build tools from scratch. You’ll discover how to solve real-life problems using data for businesses and clients and develop key data science skills.

The course is easy to follow for beginners, with no need for any prior programming experience. There are animated explanation videos, real-world projects to build, and countless useful videos. You don’t need any paid software to get involved either.

What you’ll learn?

The complete Data Science & Machine Learning Bootcamp offers everything beginners need to know about Python programming and data science. You’ll start with an introduction to machine learning, and create your own neural networks for deep learning projects. You’ll also get a complete insight into tools like Matplotlib and NumPy.

Throughout 207 lectures and 13 educational sections, this machine learning course will give you a behind-the-scenes look at what you can do with ML algorithms. Topics covered include:

• Python programming for machine learning and data science
• Linear progression for predictive models
• How to use neural networks and pre-trained models
• Leveraging Tensorflow for handwritten digits
• Building your own projects for your ML portfolio

How much does it cost?

This course is available for a price of $99.99 and lasts a total of 41 hours. You’ll also get access to a certificate at the end of the course, and countless resources as part of the price. Udemy’s courses also come with a 30-day full money-back guarantee.

3. Machine Learning Crash Course

The Machine Learning Crash Course was designed by Google to introduce you to the basics of machine learning development with Tensorflow APIs. One of the best online machine learning courses for beginners using Google’s vast technology ecosystem, this self-study guide will give you the initial tools you need to start a new career in AI.

Who is this for?

If you’re thinking of starting a career in machine learning, or you want to develop your data science skills, this could be the course for you. The comprehensive crash course comes with the opportunity to gain real-world experience using the companion Kaggle competition, and there’s access to a full library of Google AI resources.

Google recommends an “Introduction to Machine Learning Problem Framing”, and the NumPy Ultraquick tutorials to get you ready to learn. You’ll also need some experience in Python, and be comfortable with linear equations, variables, and statistical means.

What you’ll learn?

Google’s course consists of a number of video lectures, lessons, and real-world case studies, designed to give you all the insights you need into TensorFlow. Over the course of around 15 hours, you’ll encounter numerous exercises to put your new talents to the test. There are also some helpful interactive visualizations available too.

This course focuses on providing learners with the knowledge they need to address common problems with machine learning. You’ll learn how machine learning is different from regular programming, as well as:

• How to measure loss in machine learning programs
• How to develop your own deep neural network
• Representing data so programs can learn from it
• Determining whether machine learning models are effective

How much does it cost?

The course is free to take but comes with no certificate of completion at the end. You can complete each lesson in your own time.

4. Introduction to Machine Learning

Offering a behind-the-scenes guide to the computer science and statistics concepts fundamental to machine learning, the “Introduction to Machine Learning” course is great for would-be coders. This intermediate-level course combines rich learning content and interactive quizzes, with lessons from industry pros to help you harness machine learning in a range of scenarios.

Who is this for?

Though relatively easy to follow, the Introduction to Machine Learning course is intended for those with some existing coding skills. If you’re looking for the best machine learning course to enhance your existing Python programming knowledge, this could be the solution for you. You’ll need to know basic statistical strategies, and have a good knowledge of data science.

If you’re brand-new to coding, Udacity recommends taking some initial courses in Python programming, descriptive statistics, and inferential statistics. You won’t need any prior exposure to machine learning to take this course.

What you’ll learn?

With this course, students get an opportunity to learn by doing, with fascinating use cases and real-world problems to solve. Your final project involves mining the financial data and email inboxes of Enron to identify the people of interest in a historical American fraud case.

The introductory course will equip you with the skills you need to use machine learning techniques. You’ll also be prepared to take the full Data Analyst Nanodegree if you want to take your education to the next level. Topics covered include:

• How to use Naïve Bayes with Scikit Learn
• Leveraging Support Vector Machines and Decision trees
• Picking and implementing the right ML algorithms
• Leveraging datasets, questions, regressions, and outliers
• Clustering and feature scaling

How much does it cost?

This course is completely free to take and includes around 10 weeks of educational content, as well as a certificate of completion. Continuing to the Data Analyst nano-degree costs around $400 per month.

5. Machine Learning With Python

Diving into the basics of machine learning with Python, this course offered by IBM looks at the purpose of machine learning, and how it applies to the real world. You’ll gain an insight into common topics like supervised and unsupervised learning, how to use powerful ML algorithms, and what’s involved in “model evaluation”.

Who is this for?

The course is also part of two certification programs offered by IBM, including the IBM Data Science Professional Certificate, and the AI Engineering Professional certificate. If you’re planning on taking either of those certifications, this course forms an important starting point.

If you want to learn machine learning with IBM, you’ll need at least a reasonable knowledge of the Python programming language. Providing “intermediate” level training, this solution doesn’t require any existing machine learning knowledge, but you will need some experience in data science.

What you’ll learn?

Throughout around 23 hours of study, you’ll discover everything you need to know about using machine learning in a range of industries to solve common problems. You’ll discover the various techniques used in machine learning, and how the ML environment supports companies in a variety of verticals, from banking to healthcare.

The complete course, delivered over around 6 weeks, includes visual guidance and an end-of-lessons project where you’ll put your skills to the test. Topics covered include:

• A guide to linear, non-linear, simple, and multiple regression
• Classification techniques using algorithms like KNN, logistic regression, and SVM
• Clustering approaches like partitioned-based clustering, hierarchical, and density clustering
• How to use recommender systems and engines

How much does it cost?

You can sign up for this course at a cost of $49 per month. With around 23 hours of content to cover, many learners will be able to complete this course in a single month. The cost includes a certificate of completion.

6. Machine Learning For All

Delivered by the University of London, “Machine Learning for All” aims to make AI more accessible to everyone. The 100% online course explores how ML technology is powering a future of facial recognition, self-driving cars, and business evaluation. There’s access to user-friendly hands-on tools to help enhance your educational experience.

Who is this for?

This unique machine learning training course promises an excellent first step to a technical career in ML. You’ll also be able to get involved with AI concepts regardless of whether you have any prior technical knowledge.

No programming education is necessary to get started, which also means common models like TensorFlow, and Python isn’t covered as part of the course. With this course, you’ll discover how to use non-programming-based platforms for training your own modules with existing datasets.

What you’ll learn?

A true beginner’s introduction to machine learning, Machine Learning for all helps students to understand the true capabilities of ML models. You’ll gain access to a powerful platform, where you can train your own models without the need for a programming language. You’ll also learn how to predict and explain how data affects machine learning.

The course starts with a comprehensive explanation of machine learning and AI techniques, and where these concepts came from. Other topics covered include:

• How data representation affects machine learning outcomes
• How to test machine learning projects to ensure they work correctly
• The dangers and opportunities of ML technology
• How to collect datasets for machine learning purposes

How much does it cost?

Available at a cost of $49 per month, this course should be relatively affordable to those who can complete the 22 hours of training within 1 month. A certificate of completion is included in the fee.

7. Intro to Machine Learning

An introduction to the basics of machine learning, Kaggle’s ML course covers everything from using data science methodologies, to building your own models. Part of a series of “micro” courses covering concepts like AI, data science, and deep learning, this ML course teaches students how to solve real-world problems with machine learning.

Who is this for?

If you’re planning on starting a career in data science, or you’re interested in the capabilities of machine learning algorithms, this could be the course for you. Designed to provide a simple overview of common topics, like how to use different languages for data science, and what it means to build a model from scratch, this course is great for beginners.

Although you won’t need any background machine learning to take this course, it is helpful to have a basic understanding of the Python programming language. You’ll also be able to use this course as the foundation or additional intermediate courses in machine learning.

What you’ll learn?

The Intro to Machine Learning course provides exactly what you’d expect from a micro-course on ML concepts. You’ll start with an introduction to the core ideas addressed by machine learning, and develop an understanding of how models work.

The course takes learners on a journey through the basics of gathering and exploring data for machine learning algorithms, as well as the steps involved in building your own model. Other topics covered over the course of 3 hours include:

• How to validate and analyze machine learning models
• Underfitting and overfitting for ML model performance
• Using sophisticated machine learning algorithms
• Getting involved with machine learning competitions

How much does it cost?

The course is free to take, alongside a range of other ML and deep learning classes from the same company. You’ll receive a certificate of completion when you’re finished, and you’ll be able to enter competitions to put your skills to work.

8. Introduction to Artificial Intelligence Course

Providing a comprehensive view of the Artificial Intelligence landscape, the Introduction to AI course offers learners a full insight into the basics of AI workflows and concepts. If you’re planning on a career in AI, or want to expand your data science skills, this self-paced course offers a great kick-start introduction to the industry.

Who is this for?

This versatile course covers all aspects of artificial intelligence, with a strong focus on machine learning concepts like supervised and unsupervised learning. You don’t need any prior knowledge of ML concepts, but you should know about Python programming and statistics.

This course could be ideal for developers working on building their own AI models, as well as analytics managers, information architects, and analytics professionals.

What you’ll learn?

The Introduction to Artificial Intelligence course provides a more complete overview of AI for those with an interest in intelligent tools. You’ll start with a full insight into the world of AI, and what it means to create your own intelligent models. The course includes a host of videos and resources created by mentors well-versed in AI.

Topics covered include everything from fundamental AI workflows and concepts to the more complex ideas in deep learning. Some of the topics covered in the program include:

• The basics of machine learning and deep learning
• How to design machine learning workflows
• Which performance metrics to measure when designing algorithms
• How to identify supervised, unsupervised, and semi-supervised learning

How much does it cost?

You’ll be able to access this course free of charge for up to 90 days, and the training lasts around 2 hours. A certificate of completion is available when you finish your course.

9. Machine Learning Specialization

Offered by the University of Washington, the Machine Learning Specialization is a comprehensive educational experience focusing on techniques for building and using intelligent applications.

The hands-on course includes a series of practical case studies and hands-on challenges to ensure you can develop your knowledge in major areas of machine learning like classification, prediction, and clustering methods.

Who is this for?

You’ll need some basic knowledge of machine learning and computer programming worlds to thrive in this course. The machine learning training experience requires an existing understanding of the Python programming language.

Intended for data scientists and software developers looking to expand their skills in machine learning, this course may be challenging for beginners. However, it’s appropriate for anyone with a love of data, programming skills, and basic math skills.

What you’ll learn?

The Machine Learning specialization is built to help learners master the art of machine learning algorithm development and data science. You’ll learn how to analyze complex data sets, create adaptable systems that improve with time and design your own applications.

Learners will implement and apply a range of classification, clustering, and predictive algorithms to genuine data sets throughout the course, and put their skills to the test in a hands-on project. Some of the topics covered include:

• The foundations of machine learning and common algorithms
• Regression, and how to create a predictive model
• Clustering and retrieval methods in machine learning
• How to master Python programming and decision trees

How much does it cost?

The course is available at a price of $49 per month, with a total duration of around 7 months, equalling around $343 in total. Financial aid is available, and you can enroll in the course for free if you just want to audit the content without a certificate.

10. Data Science: Machine Learning

The Harvard Data Science: Machine learning Training course provides a hands-on learning experience to students who want to build their own ML algorithms. You’ll learn how to build your own movie recommendation system (similar to the one used by Netflix), and discover the basics of some of the most popular data science techniques.

Who is this for?

This self-paced course provides a fantastic introduction to the fundamentals of machine learning. You’ll need to be located in one of the available locations covered by EdX to apply for the course (which excludes the Crimea region of Ukraine, Cuba, and Iran).

While you don’t need a lot of significant prior knowledge in machine learning for this course, it is part of a comprehensive “Professional Certificate Program in data science”. EdX recommends taking the preceding courses in the series before you get started.

What you’ll learn?

This online machine learning course covers all of the basics of machine learning algorithms and applications. You’ll start with an introduction to the common concepts of machine learning, and how they’re applied in today’s evolving business landscape.

Students can discover how to train data, and use certain data sets to discover predictive relationships. You’ll also get a deep-dive insight into using algorithms and understanding concepts like overtraining and cross-validation. Topics covered include:

• How to avoid common overtraining issues
• The basics of modern machine learning algorithms
• The benefits of regularization for ML models
• How to build a recommendation system

How much does it cost?

This course costs $99 if you want to take the full Machine Learning educational program. Overall, the self-paced course should take around 8 weeks to complete, and includes a machine learning certificate.

Are Machine Learning Courses Worth It?

Machine learning is quickly becoming a popular concept in the business world, and a valuable tool for data scientists and programmers alike.

In a recent survey, 82% of companies revealed a need for professionals skilled in building and using machine learning technologies, but only 12% felt access to the right talent was adequately available.

Many leading companies are actively searching for machine learning specialists. For instance, 63% of the open positions on the Amazon career page ask for “machine learning”.

With the right machine learning courses, students can develop their understanding of all kinds of complex machine learning and AI concepts, from supervised and unsupervised learning to the benefits of cross-validation.  A machine learning training experience will allow you to:

• Demonstrate your understanding of machine learning languages like Python, and develop skills with common ML and AI algorithms.
• Build your own machine learning projects for business use cases, or develop a strong portfolio for future career opportunities.
• Differentiate yourself from other professionals with universally recognized certificates in machine learning.
• Develop your data science or programming skills for a new world of artificial intelligence career opportunities.
• Enhance the quality of your machine learning creations by avoiding concepts like over-training and under-training with tools like cross-validation.

Methodology

Machine learning training is very popular these days and there are a number of online training courses available. To create the list of the best machine learning courses, we’ve looked at training programs from reputable companies and universities.

After going through the course syllabus of all courses, our recommendation is to take a course from a high-tech company such as Google or IBM. These companies are actively using Machine Learning and their courses can teach you more than the theory. Courses offered by Universities are a good starting point for beginners.

Start with the free courses to get an idea of what machine learning is and how it works and if you decide that this is a career you want to follow, enroll in a paid course.

Related Courses and Certifications

To prepare for a career in machine learning, it is highly recommended to expand your skills to cover areas like Python programming and data analytics. The following guides will help you pick the right courses for you.

• Best Python Courses – a list of courses for beginners to help you build essential python programming skills.
• Google IT Automation with Python Professional Certificate – pursue with Google career certificate to get a job as a Python system administrator.
• Best Data Analytics Certifications – the best data science/data analytics certifications to pursue this year. They all include courses related to machine learning and AI.