Best Machine Learning Courses

Best Machine Learning Courses

Best Machine Learning Courses in 2022

1. Deep Learning Specialization – offered by Deep Learning.AI on Coursera


  • A well-rounded specialization for those seeking to learn about fundamental concepts and practical skills related to machine learning and deep learning
  • Able to complete one or all of the courses in the specialization depending on your preferences
  • Specialization created by top-rated instructors on Coursera


  • Certificate of completion requires paid course access
  • Does require some prior experience including intermediate Python skills; won't suit beginners
  • Suggested study of eight hours per week for five months won't suit those looking for short courses that can fit easily around busy schedules

This foundation course for deep learning (a subfield of machine learning) offers a five-month course that will prepare you for developing AI technology. Upon completion, you will gain a certificate that can be shared with potential employers and other professionals.

It comprises five separate courses, each of which will help you to understand the complexities and capabilities of machine learning:

1. Neural Networks and Deep Learning – The first section of the course will enable you to understand the concept of neural networks and deep learning. By the end, you will:

  • Understand the trends which have driven demand for deep learning
  • Be able to build and apply deep neural networks
  • Be able to identify the key parameters within a neural network
  • Be able to apply deep learning to your own applications

2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization – In this section, you will develop a deeper understanding of the processes which drive performance and lead to positive results. By the end, you will:

  • Be able to use standard neural network techniques
  • Be able to implement a variety of algorithms for optimization purposes
  • Be able to implement neural networks in TensorFlow

3. Structuring Machine Learning Projects – During the third section of the course, you will gain hands-on experience, learning how to build a machine learning project. You will also be able to practice your decision-making skills as project leader.

This section can be taken as a standalone course for those who already have prior knowledge of machine learning. By the end, you will:

  • Be able to accurately diagnose issues within a machine learning program
  • Be able to create successful strategies designed to reduce errors
  • Be able to apply end-to-end learning

4. Convolutional Neural Networks – This section focuses on understanding how machine learning has evolved as well as exploring the applications of this technology. By the end, you will:

  • Be able to build a convolutional neural network
  • Be able to apply convolutional networks to visual recognition tasks
  • Be able to use neural style transfer to create art
  • Be able to apply algorithms to a variety of image and video data

5. Sequence Models – The final section of the course is designed to familiarize you with sequence models and their applications. By the end, you will:

  • Be able to build and train recurrent neural networks (RNNs) as well as commonly used variants
  • Be able to apply RNNs to character-level language models, gaining experience with language processing and word embedding
  • Be able to use HuggingFace tokenizers and transformer models to complete activities such as question-answering

Visit Deep Learning Specialization – offered by Deep Learning.AI on Coursera

2. Introduction to Machine Learning for Coders – University of San Francisco


  • Well suited to individuals who write code daily
  • Will appeal to those seeking a code-first approach over a math-first approach


  • Does require prior coding experience; not suitable for beginners
  • Suggested eight hours of study each week may not suit those with busy schedules

This course is designed as a 12-week online learning course.

Individuals who choose this course should ideally have at least a year of coding experience as well as a high-school-level understanding of math. If you are not at this level, then you may need to do extra study ahead of undertaking this course.

This course is made up of 12 two-hour lessons based on recordings from the Master of Science in Data Science course at the University of San Francisco.

It is suggested that you spend eight hours each week completing the course material.

The lessons:

  1. Introduction to Random Forests – The first session will teach you how to use a Jupyter Notebook to build and download models as well as how to download data.
  2. Random Forest Deep Dive – This session covers metrics, loss functions and overfitting as well as validation, tests and tricks to make forests faster and more accurate.
  3. Performance, Validation and Model Interpretation – Learn to read larger datasets, profiling and how to speed up code as well as a deeper look at validation sets.
  4. Feature Importance, Tree Interpreter – This session is a more in-depth look at feature importance and interpretation techniques.
  5. Extrapolation and RF from Scratch – A deeper look at the tree interpreter and the use of waterfall charts to analyze data. This session also covers the issue of extrapolation and how to deal with it when it occurs.
  6. Data Products and Live Coding – This session looks at how to create data products based on machine learning models, the importance of model interpretation in the creation of data products and how to use live coding to deal with the issue of extrapolation.
  7. RF from Scratch and Gradient Descent – A final look at the subjects covered in previous sessions before moving on to the topic of gradient descent.
  8. Gradient Descent and Logistic Regression – An introduction to gradient-based approaches such as deep learning. This session teaches how to use Pytorch to implement logistic regression.
  9. Regularization, Learning Rates and NLP – This session builds on the information regarding logistic regression from the previous lesson and adds the feature of regularization. It also discusses the importance of learning rates and looks at natural language processing (NLP).
  10. More NLP and Columnar Data – This session develops the NLP model which was started in the previous lesson combining naive Bayes and logistic regression. You will also be introduced to tabular and relational data as well as continuous vs categorical variables and embedding matrices.
  11. Embeddings – A deep dive into embeddings and the ways that they can be used.
  12. Complete Rossmann, Ethical Issues – The final session combines previous learning to create a completed model for the Rossmann dataset. The lesson also looks at potential ethical issues, why they are important and how to handle them.

Visit Introduction to Machine Learning for Coders – University of San Francisco

3. Practical Deep Learning for Coders –


  • Study at your own pace
  • Course content does not require a technical or mathematical background (though some coding experience is needed)


  • Does require some prior experience, namely a year of coding (ideally in Python) and high school level math

This course is designed to be accessible to anyone who wants to understand deep learning and coding.

Students don’t need to have advanced mathematical skills or extensive knowledge, although a year of coding experience is recommended. Lessons can be done at your own pace.

Through eight lessons you will learn:

  • How to train models in four key areas which will achieve results
  • How deep learning models work and why
  • The latest techniques in deep learning
  • How to use gradient descent and complete a training loop
  • The potential ethical issues which may occur with deep learning, how to approach these and how to ensure that deep learning programs aren’t misused.

Visit Practical Deep Learning for Coders –

4. Machine Learning – offered by Stanford University on Coursera


  • Study at your own pace
  • Course led by top-rated Coursera instructor
  • Suits those seeking a basic understanding of machine learning without a lot of prior experience (though will require an understanding of linear algebra)


  • Certificate of completion requires paid course access
  • Around five to eight hours of weekly study for 11 weeks requires commitment and may not suit those with busy schedules
  • Some dated content and may not be that beneficial to highly experienced machine learning professionals

This 11-week online course by Stanford University offers a broad overview of everything you will need to know about machine learning.

Students will usually take 61 hours to complete the course although deadlines are flexible and can be altered to fit with your schedule.

Throughout the sessions, you will learn about building algorithms, medical informatics, database mining and much more.

Once you complete the course, you will have the opportunity to obtain a certificate that can be shared with prospective employers.

The course structure is as follows:

  • Week 1 – An introduction to machine learning, linear regression with one variable, and a refresher on linear algebra concepts
  • Week 2 – Linear regression with one variable, Octave/Matlab tutorial
  • Week 3 – Logistic regression, regularization
  • Week 4 – Neural networks: representation
  • Week 5 – Neural networks: learning
  • Week 6 – Advice for applying machine learning, machine learning system design
  • Week 7 – Support vector machines
  • Week 8 – Unsupervised learning, dimensionality reduction
  • Week 9 – Anomaly detection, recommender systems
  • Week 10 – Large scale machine learning
  • Week 11 – Application example: photo OCR

Visit Machine Learning – offered by Stanford University on Coursera

5. Machine Learning Specialization – offered by the University of Washington on Coursera


  • Suits those with some prior programming experience who want to build on their skills
  • Case study approach provides hands-on learning
  • Suggested study pace of three hours per week accommodates busy schedules
  • Study at your own pace
  • Option to take one or all of the courses within the specialization depending on your preferences


  • Installing required software can be challenging
  • Certificate of completion requires paid course access

This online course is designed for those who already have some experience of computer programming, but wish to increase their knowledge.

On average, learners will complete the four sections of the course in around seven months, but this does depend on your own learning pace.

By implementing a case study approach, individuals have the opportunity to put the skills they are learning into practice.

This means that you can learn the principles of machine learning in a hands-on format.

Learners have the opportunity to obtain a certificate upon completion which they can share with potential employers.

The course includes:

  1. Machine learning foundations – introduction to principles and techniques
  2. Machine learning: regression – case study
  3. Machine learning: classification – case study
  4. Machine learning: clustering and retrieval – case study

Visit Machine Learning Specialization – offered by the University of Washington on Coursera

Best Machine Learning Courses in 2021
Best Machine Learning Courses in 2022

What Is Machine Learning?

Machine learning is a form of data analysis. It uses artificial intelligence to create programs and algorithms which learn from data to predict future patterns and trends.

Over time, the program becomes more familiar with the data, thereby increasing the accuracy of its predictions. Using this process, it is essentially learning by itself or with very little human interaction.

There are essentially three main types of machine learning algorithms:

  • Unsupervised – Uses clusters of data to analyze complex information
  • Supervised – Identifies patterns and data points to make predictions
  • Reinforcement – Actions can be taken depending on various data points. Some of these actions will be automated, while others will require manual input.

We experience the effects of machine learning in our everyday lives without really noticing it. Just a few examples of machine learning algorithms are:

  • Fraud detection within banking – Your bank will notice if there is unusual behavior on your account and potentially flag a fraud warning. This is thanks to machine learning algorithms that analyze your typical behavior patterns.

  • Social media – Algorithms are key to being noticed on social media. The way that hashtags and content are analyzed will decide how prominently your posts appear and the level of reach that you have.

  • Recommendations on your streaming service – By looking at the genres of programming that you most frequently watch, algorithms can suggest other shows which you might enjoy.

  • Spam filters – The programming which decides whether the emails going into your inbox are spam or genuine is a form of machine learning technology.

Why Study Machine Learning?

The ability to create software that performs machine learning functions is becoming more and more vital in today’s world.

Artificial intelligence is a rapidly growing area of software development and as such, the demand for developers is constantly increasing.

Data analysis and development are core areas within most areas of business, and this demand is only likely to increase in the future.

To study machine learning, it is generally recommended that you have some knowledge of programming, as well as calculus, algebra and statistics.

This is because it is considered an advanced discipline within programming and data analysis.

However, it is not required for all courses and some can be taken alongside refresher courses for the areas in which you may not have as much experience.

How Long Does It Take to Study Machine Learning?

The length of time it takes to complete your machine learning course will vary hugely depending on which course you take.

Some provide an introduction to the world of machine learning and may only take a few hours to complete.

Others may involve more detailed information, but be self-paced, meaning that you can complete them as quickly or as slowly as you want. There are also courses that follow structured timings and may take up to two years.

It is important to note that once you have finished your machine learning course, there will be opportunities in the future to carry on learning.

As this area of industry is developing so quickly, there are always new things to learn. Much of this will be on-the-job learning.

How to Choose a Machine Learning Course

With so many options available, it can be hard to know which course will suit you best. When deciding on a course, there are a few things to consider:

What Do You Want to Learn?

Machine learning and data analysis are hugely varied areas of industry. It is a good idea to consider which areas you would like to work in as this will help to guide your decision-making process on courses.

It may be that you only need basic knowledge, in which case an introductory course could be best. If you are considering specializing within a specific area, then it is always best to find a course that covers your chosen specialization.

Is a Free Course Suitable for Your Needs?

There are free courses available that can teach you the basics of machine learning.

However, if you are looking to begin a career in this area, it is important to make sure that any course you take will be recognized by employers.

If your preferred free course isn’t recognized, it may be worth considering paying for one that is.

How Long Can You Study For?

If you are considering taking a machine learning course alongside your job, you will have less time available compared with someone who can study full-time.

It is always best to choose a course that fits within the time you have available.

Choosing a course that will leave you overstretched can lead to poor performance and you may not be able to complete the course to the best of your ability.

Do You Have Experience in Math, Programming or Data Analysis?

A large proportion of machine learning and data analysis is math and programming. If you already have experience within these areas, then you may be able to jump straight into a more specialized course.

If there are areas of your knowledge that are weaker, it may be worth considering an introductory machine learning course alongside a refresher course that covers your weakest areas.

If you don’t have experience in math, programming or data analysis, you should consider courses in these areas before beginning a machine learning course.

Final Thoughts

Machine learning is an essential skill for anyone working within the world of programming and analytics. The demand for individuals who understand this sector is increasing and is unlikely to cease.

Making sure that you choose the right course for your machine learning needs will help you to achieve your career goals in the future.

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