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The 9 Best Online Machine Learning Courses (2024 Reviews & Rankings)

With the best online machine learning courses, you can gain valuable, cutting-edge skills and knowledge to help you build a thriving career in this growing field.

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By Fatima Mansoor

best online machine learning courses

Machine learning is a branch of artificial intelligence that deals with the design and development of algorithms that allow computers to learn from data. As machine learning is increasingly being used in a wide range of applications, including image recognition, natural language processing, and predictive analytics, there is a growing demand for machine learning experts.

The good news is you can learn machine learning on your own time from the comfort of home, thanks to online courses from top platforms.

Of course, not all machine learning course programs online are created equal, and with dozens upon dozens of courses to choose from, it can be tough to know where to start. That’s why I’ve put together a list of the best online machine learning courses, to help you find the right one for your needs.

I’ve ranked the top paid and free machine learning classes online based on content, instructor, and price (click here to learn about our entire Editorial Process & Methodology for product reviews).

What are the Best Online Machine Learning Courses?

Here are my picks for the top machine learning classes you can take online in 2024…

1. Mathematical Foundations of Machine Learning (Udemy)

Mathematical Foundations of Machine Learning (Udemy) Mathematical Foundations of Machine Learning (Udemy)

Gain a solid foundation in linear algebra and calculus, the essential mathematical disciplines that form the basis of machine learning and data science.

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Regularly $109.99 for full lifetime access with a 30-day money-back guarantee (often goes on sale)


16.5 hours on-demand video

This course takes you from the concepts of mathematics and integrates them into the basics of machine learning. So, in the introductory lessons, you will learn about linear algebra, vectors, and matrices. The next lessons cover complex concepts like Eigenvectors, limits, and differentiation.

This mathematical start to machine learning is how machine learning is taught in universities to build a solid foundation for what lies in the advanced levels of this subject. You will enjoy learning about the mathematical concepts from the best in the industry.

You should take this course if you want to build the basics before deep diving into machine learning. This course is also for you if you already have some background of machine learning, but want to get your basics strong to get the hang of the more complex concepts that you need to process in advanced machine learning courses.

What You Will Learn

  • Data structures for linear algebra
  • Integral calculus
  • Limits
  • Matrices
  • Eigenvectors
  • Hands-on exercises to solidify mathematical machine learning concepts.

2. Complete AI & Machine Learning, Data Science Bootcamp (Udemy)

Complete A.I. & Machine Learning, Data Science Bootcamp (Udemy) Complete A.I. & Machine Learning, Data Science Bootcamp (Udemy)

Master data science, data analysis, and machine learning with this comprehensive course that covers Python, TensorFlow, Pandas, and more, equipping you with the skills to excel in artificial intelligence.

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Regularly $119.99 for full lifetime access with a 30-day money-back guarantee (often goes on sale)


Almost 44 hours of lectures divided into 21 sections and 382 lectures

This course takes you from a beginner level and allows you to deep-dive into the complex concepts in Machine Learning. Through this course, you will learn about the basics of machine learning and Python. You will then get hands-on machine learning experience as you navigate through Python, learning Pandas, NumPy, and Matplotlib. Finally, by the end, you will learn how to build machine learning models through Scikit-learn.

This course is for you if you want to go from a zero to a mastery level in machine learning. It covers all the basics of Python and allows you to build your own machine-learning models by the end of the course.

With a whopping 44 hours of content, you will go through the different concepts of machine learning, with detailed content for each concept. Every part of this course is well-designed, and nothing is overdone. So, every minute of this course will build your knowledge and model-creating skills.

What You Will Learn

  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • How to use modern tools that big tech companies like Google, Apple, Amazon and Meta use.
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Study real-life cases and projects to understand how things are done in the real world
  • Implement Machine Learning algorithms
  • How to explore large datasets and wrangle data using Pandas

3. Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)

Machine Learning A-Z (Python & R in Data Science Course) | Udemy Machine Learning A-Z (Python & R in Data Science Course) | Udemy

This course offers you the opportunity to learn how to develop machine learning algorithms in Python and R from two data science experts, complete with ready-to-use code templates.

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Regularly $139.99, but often goes on sale


44 hours on-demand video Throughout this course, you will work in Python, R, R studio, and their libraries. It is an intense course starting from the basis and refining you to an intermediate level. At the start of this course, you are taught how to install R and R studio. As you progress, you will work on advanced libraries such as different regressions, K-Nearest neighbors, Support Vector Machine, Kernel SVM, and Naïve Bayes. It’s further extended to classification, clustering, deep learning, dimension reduction, and model selection. This course is designed for individuals who are interested in machine learning and have no experience in coding. But it requires a basic understanding of mathematics.

What You Will Learn

  • Learn the use of Python and R to master machine learning
  • Make precise forecasts
  • Make a strong analysis
  • Creation of robust machine learning models
  • Have a strong understanding of a variety of Machine Learning models
  • Handle specialized areas such as reinforcement learning, natural language processing, and deep learning
  • Understanding of advance approaches such as Dimensionality Reduction
  • Structuring of different models and understanding to integrate them

4. Machine Learning For Everyone (Datacamp)


Plans start at $13/month billed annually


2 hours on-demand video

In this course, you will learn non-technical aspects of machine learning. It is designed for beginners so that they can understand the terminology and the process of how things work with the help of machine learning.

There are no prerequisites for this course, nor does it require any programming knowledge.

Think of this as a true “101 level” course that will serve as an intro to this new topic and help you get on the path to learning much more about it.

What You Will Learn

  • What is machine learning?
  • The relation between data science and Artificial Intelligence is established.
  • Decodes key machine learning terms.
  • Introduces model-building machine learning procedure.
  • Introduction to neural networks
  • Further use of two deep learning applications: computer vision and natural language processing.

5. Introduction To Deep Learning In Python (Datacamp)


Plans start at $13/month billed annually


4 hours of on-demand video

Python is a new and advanced programming language for building websites and software, automating tasks, and analyzing data. Developers have started to use Python for machine learning; therefore, new frameworks and libraries have been established.

Deep learning is a branch of machine learning and artificial intelligence that mimics how people acquire information. It covers statistical data and pattern classification and incorporates deep learning as a key component.

In this course, you will use Keras 2.0 (created with the goal of allowing for quick experimentation) with new libraries for deep learning in Python. This course intends to provide you with practical experience to maximize your learning.

What You Will Learn

  • Introduction to deep learning
  • Comparison between neural network models and classical regression models
  • Forward propagation algorithm
  • Multi-layer neural network
  • Understanding model error
  • Relation between change in weight and accuracy of the mode
  • Understanding gradient descent and calculating scope
  • Creation of Keras model
  • Classification of the model
  • Making predictions from the model
  • Altering of optimization parameters
  • Addition of layers to a network
  • Creation of your own digital recognition model

6. Machine Learning Fundamentals with Python (Datacamp)


Plans start at $13/month billed annually


20 hours.

Machine learning is changing the world around us, and this online course will help you earn how you can use data to make powerful predictions and suggestions for users with machine learning.

This Skill Track is extensively designed to develop your beginner to intermediate-level skills with the Python programming language.

It includes 5 courses, each designed to teach a new skill to polish your skills.

The course curriculum is designed to teach you the use of scikit-learn (a user-friendly library in Python), SciPy, logistic regression, case studies, and Keras 2.0.

What You Will Learn

  • Exploratory data analysis
  • Numerical and Visual EDA
  • Linear Regression
  • 5-Fold cross-validation and K Fold CV comparison
  • Plotting and area under ROC curve
  • Creation of temporary variables and handling of missing data
  • Exploration of datasets using clustering
  • T-SNE and hierarchical clustering for visualization
  • Reducing the size of your data and decorating it
  • Finding interpretable characteristics
  • Using SVM and logistic regression
  • Loss Function and Logistic regression
  • Vector Support Machines
  • Learning from case study: exploring raw data, creating and improving model
  • Learning about deep learning and neural networks
  • Creation of models with Keras

7. Data Engineering for Everyone (Datacamp)


Plans start at $13/month billed annually


2 hours on-demand video

The “engineering” element is essential to comprehend when studying what data engineering is. Engineers create and construct things. Data engineers develop and construct processes that modify and transmit data in a highly useable manner by the time it reaches Data Scientists or other end users. The construction of processes collects data from a variety of sources and stores in a single warehouse.

This course is designed for beginners to understand the dynamics of data engineering.

Throughout the course, you will work on Spotflix’s data (a fictional company) to understand your role; after your tasks, you will be better positioned to understand what is done by data engineers.

What You Will Learn

  • Introduction to data engineering how is it different from data science
  • Introduction to data structures and SQL databases
  • Difference between data warehouse and data lakes
  • Organization of the data and extracting meaning from data
  • Scheduling data
  • Parallel computing, Parallel universe, and cloud computing

Want to learn more about Datacamp? Check out our Datacamp review.

8. Machine Learning by Andrew Ng (Coursera)


Free to audit, but a professional certificate of completion comes at a fee


61 hours

did you know that the art of enabling machines to operate without any supervised learning is known as machine learning?

Self-driving vehicles, realistic voice recognition, successful online search, and a much-enhanced knowledge of the human genome have all been made possible by machine learning in the last decade.

This course is explained in an easy way from the basics so that no one needs to worry about it when signing up for this course. It is a beginner-level course, which requires some knowledge of linear algebra.

What You Will Learn

  • Introduction to the concepts of Machine learning
  • Linear Algebra; Linear and Multiple regression
  • Understanding of Octave/Matlab and logistic regression
  • Introduction to Neural networks
  • Creation of Neural networks – backpropagation algorithm
  • How to apply Machine Learning
  • Fundamentals of Support Vector Machine and Unsupervised Learning
  • Learn how to detect anomalies using Gaussian distribution
  • Integrate ML algorithms with a huge dataset

9. Machine Learning Crash Course with TensorFlow APIs (Google)


Free to use


15 hours

The Google AI Education designs this course. It is a free platform where different courses are taught through the help of videos, articles, and exercises. This is a crash course designed for machine learning but aided by TensorFlow’s open-source software library. This course aims to provide you with real-world scenarios and ensures practical learning.

This course doesn’t provide any certificate if you are looking for one. Google AI Education intends to empower people with knowledge.

Remember, this is not a beginner-level course! You need to understand Python for coding and have a firm grip on variables, linear equations, graphs of functions, histograms, and statistical inferences. In addition to this, you need to be confident with Bash Terminal or cloud console.

Before enrolling yourself in this course, you should learn Introduction to Machine Learning Problem Framing, NumPy Ultraquick Tutorial, and pandas Ultraquick Tutorial.

What You Will Learn

  • Introduction to key terminology
  • Use of Linear regression and Logistic regression
  • Learning of gradient descent and optimization of learning rate
  • Classification – ROC curve and AUC
  • Regularization – L1 and L2, Lambda
  • Development of Neural Networks – single and multi-class
  • Training set, splitting, and validation
  • Embedding and Machine learning engineering

Final Thoughts

As anyone in the tech industry will tell you, machine learning is one of the hottest trends right now. And it’s only going to become more popular in the years to come.

That’s why taking an online machine learning course is a great way to stay ahead of the curve and prepare for the job market of the future.

Machine learning is a complex field, but with a good course, you can learn everything from basic techniques to advanced concepts, and start applying them to real-world problems.

And as machine learning gets more widespread, employers will be looking for candidates with experience in the technology. So if you’re looking to stay ahead of the curve and make yourself more marketable, taking an online machine learning class is a great way to do it.

Have any questions about this guide to the best machine learning course programs? Let us know by commenting below.

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