6 Best Machine Learning Courses in 2024 to Boost Your ML Skills

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If you want to become a machine learning engineer or pursue another AI and ML-related career path, taking and completing one or more of the best machine learning courses is critical. That’s because such courses provide a thorough understanding of ML algorithms, principles, and techniques.

The top machine learning courses also offer hands-on experiences with practical exercises and projects, enhance your career prospects by enabling you to demonstrate your expertise and commitment to employers, and ensure you stay up to date with the latest trends and advancements in the field.

In this article, we explore where you can get high-quality machine learning training and certification that can jumpstart your career.

Key Takeaways

  • The best courses for machine learning provide a comprehensive understanding of machine learning algorithms, principles, and techniques.
  • These courses enhance your career prospects by enabling you to demonstrate your expertise.
  • Some of them are free to audit; however, you’ll have to pay to receive a certificate.
  • The top machine learning courses will enable you to make meaningful contributions to the rapidly evolving field of machine learning and artificial intelligence.

6 Best Machine Learning Courses in 2024

To help get you started on your journey to that ML engineer position or other machine learning-related job, here are some of the best online machine learning courses.

6 Best Machine Learning Courses in 2024

Machine Learning Specialization

A foundational online program, the Machine Learning Specialization is a collaboration between Stanford Online and DeepLearning.AI. This Coursera machine learning course aims to teach beginners the basics of machine learning as well as how to use machine learning techniques to build real-world artificial intelligence applications. This is one of the top courses for machine learning on Coursera.

The Machine Learning Specialization is ideal if you’re just getting started with artificial intelligence and you want to gain a fundamental understanding of how machine learning models work, as well as real-world experience building Python machine learning models.


This three-course program offers a comprehensive overview of contemporary machine learning techniques, such as supervised learning, e.g., neural networks, decision trees, logistic regression, and multiple linear regression, as well as unsupervised learning, e.g., recommender systems, dimensionality reduction, and clustering.

By the end of this program, you’ll understand the key concepts of machine learning. You’ll also have the practical knowledge to use machine learning to handle difficult real-world problems.

  • This machine learning course takes two months to complete at 10 hours per week.
  • Although there are no prerequisites to take this program, you should at least have some basic knowledge of programming and high-school-level math.
  • The Machine Learning Specialization program is free to audit.
  • However, if you want to obtain a machine learning certificate, the cost is $49 per month after the seven-day trial period.

AWS Machine Learning Engineer Nanodegree

The AWS Machine Learning Engineer Nanodegree, developed in collaboration with Amazon Web Services (AWS), is an intermediate-level program designed to equip software developers and data scientists with the skills they need to build and deploy machine learning models using Amazon SageMaker.

Offered on Udacity, the program covers such topics as neural network basics, deep learning fluency, and the basics of the machine learning framework. The program includes an introduction to machine learning and explains how to develop machine learning workflows. It also covers deep learning topics, natural language processing, and computer vision.

Throughout the five-month, six-course program, you’ll also work on real-world projects to reinforce your learning. This program provides an excellent opportunity to enhance your machine learning skills and gain hands-on experience with AWS services.

  • If you complete the program successfully, you’ll receive an AWS machine learning certification.
  • The prerequisites for the AWS Machine Learning Engineer Nanodegree program include familiarity with AWS, intermediate Python skills, and proficiency with application programming interfaces.
  • The cost of the program is $249 per month. A discount is available for the first four months if you opt to pay upfront, after which plans are converted to month-to-month.

Machine Learning A-Z: AI, Python & R + ChatGPT Prize

The Machine Learning A-Z: AI, Python & R + ChatGPT Prize is an online course offered on Udemy. In this course, you’ll learn to create machine learning algorithms using Python and R. Designed by a data science expert and a machine learning expert, this course covers a wide range of topics related to machine learning.

The course is structured into several parts, including such topics as classification, clustering data preprocessing, natural language processing, regression, reinforcement learning, deep learning, and model selection. It includes practical exercises based on real-life case studies, enabling you to apply theory to real-world scenarios.

This course is geared to anyone who wants to begin their machine learning career as well as intermediate-level people who know the basics of machine learning and want to explore the different fields of machine learning.

  • You should have knowledge of high school math if you want to take the Machine Learning A-Z: AI, Python & R + ChatGPT Prize course.
  • The course consists of 42.5 hours of on-demand video. You will receive a certificate after successfully completing the program.
  • This course includes Python and R code templates that you can download and use on your own projects.
  • It also offers an extra resource that you can download that teaches you how to use ChatGPT to improve your machine learning skills.
  • The cost of the course is $119. Discounts are sometimes available.

A Practical Guide to Machine Learning with Python

A Practical Guide to Machine Learning with Python offered on Educative is an interactive course designed for developers who want to learn about the world of machine learning using Python.

The course, which focuses on teaching you how to code basic machine learning models, is geared toward beginners who have a general understanding of the concepts of machine learning.

The course covers common algorithms, such as logistic regression, linear regression, SVM, KNN, and decision trees. It follows a structured approach, providing theoretical understanding, technical insights, and hands-on implementations. You’ll learn the fundamentals of different learning models, e.g., supervised, unsupervised, etc.

The topics of the course include data processing, wrangling, and visualization, as well as feature engineering, model building, tuning, and deployment. The course offers practical and hands-on content, allowing you to develop skills that you can apply to real-world problems.

By the end of the course, you’ll have a solid understanding of the fundamental principles of machine learning, and you’ll have the ability to use machine learning tools effectively. If you’re eager to begin your journey into machine learning, this course is a great place to start.

  • To take this course, you’ll need basic software development skills and basic high school math, such as algebra or trigonometry.
  • A Practical Guide to Machine Learning with Python, which is divided into 12 sections, takes 72.5 hours to complete.
  • You’ll receive a certification of completion after you’ve finished the course.
  • The cost of the course is $16.66 per month. Discounts are available.

Machine Learning for All

Offered on Coursera, the University of London’s Machine Learning for All course aims to make machine learning accessible to everyone, even if they don’t have backgrounds in programming or math.

The course is designed to introduce both technical and non-technical learners to the world of machine learning. Even if you don’t have a background in math or programming, this course will help you understand fundamental machine learning concepts and techniques.

You’ll engage in a hands-on machine learning project using user-friendly tools developed at Goldsmiths, University of London. Specifically, you’ll work on training a computer to recognize images. Whether you’re an aspiring data scientist or simply curious about breakthroughs in AI, this course is a great starting point.

In this course, you’ll learn the fundamentals of how modern machine learning technologies work. You’ll also be able to explain and predict how data affects the results of machine learning. You’ll also learn how to use a non-programming-based platform to train a machine learning module using a dataset.

The Machine Learning for All course can help you start a technical career in machine learning; however, it also fits if you’re in a non-technical role.

  • This course takes 21 hours to complete (seven hours a week for three weeks).
  • It doesn’t require any specific prerequisites or prior technical knowledge.
  • Unlike other courses, you don’t need a math or programming background to take the Machine Learning for All course.
  • The course is free to audit. The cost to access graded assignments and earn a certificate through Coursera is $49.

Introduction to Machine Learning

The Introduction to Machine Learning course, offered as part of the Massachusetts Institute of Technology’s Open Learning Library, is available to anyone, anywhere, at any time.

All the material is free to use. However, the Open Learning Library doesn’t include discussion forums or offer certificates upon completion of a course.

This course explores the basics of machine learning, encompassing principles, algorithms, and practical applications focused on modeling and prediction. It delves into how to formulate learning tasks, covering crucial concepts such as representation, overfitting, and generalization.

Through hands-on exercises, you’ll apply these principles in both supervised and reinforcement learning contexts, with specific attention to image analysis and temporal sequence processing.

The Introduction to Machine Learning course focuses on the mathematics of machine learning and deep learning.

It’s perfect if you’re looking for a comprehensive understanding of machine learning practices. However, if you just want a quick, practical introduction to machine learning, this might not be the right course for you.

  • The course, which takes 13 weeks to complete, includes lectures, lecture notes, exercises, labs, and homework.
  • Before taking this course, you should understand computer programming (Python), calculus, and linear algebra.
  • The course is free, but it doesn’t offer a machine learning certification upon completion.

The Bottom Line

Taking a machine learning course can offer you numerous benefits, regardless of your field or background.

It can equip you with valuable skills, enhance your career prospects, and enable you to make meaningful contributions to the rapidly evolving field of machine learning and artificial intelligence.

Сhoose yours among the best machine learning courses depending on your background, career goals, and preferences.


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Linda Rosencrance
Technology journalist
Linda Rosencrance
Technology journalist

Linda Rosencrance is a freelance writer and editor based in the Boston area, with expertise ranging from AI and machine learning to cybersecurity and DevOps. She has been covering IT topics since 1999 as an investigative reporter working for several newspapers in the Boston metro area. Before joining Techopedia in 2022, her articles have appeared in TechTarget, MSDynamicsworld.com, TechBeacon, IoT World Today, Computerworld, CIO magazine, and many other publications. She also writes white papers, case studies, ebooks, and blog posts for many corporate clients, interviewing key players, including CIOs, CISOs, and other C-suite execs.