15 Best Machine Learning Courses in 2024 [Free + Paid]

At a time when data is central to modern business, research, and almost everything, organizations from various industries of all shapes and sizes are looking to use their growing data stores to drive growth and innovation.

But how do they extract value from data? This is where the modern data team comes in, combining data scientists, data analysts, and specialists like machine learning (ML) engineers and other AI professionals.

According to McKinsey Global Institute, AI and machine learning technologies could contribute up to $13 trillion to the global economy by 2030, highlighting their continued impact on the modern world. And with ML engineers earning average salaries over $130K, there’s never been a better time to start studying this transformative area of modern data.

If you’re a data-driven professional, there’s no doubt that you’ve already experienced the power of machine learning, but perhaps you’ve yet to dip your toes into the water. Or perhaps you’re new to the world of data and curious to find out, what is machine learning?

Regardless of your background, one of the best ways to gain machine learning skills in 2023 is to enroll in a machine learning course. To help you on your journey,  we’ve found the 15 best machine learning courses online in 2023, with a range of free and paid options for all skill levels.

Looking for a fresh role in Machine Learning for 2023?

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Choosing the Best Machine Learning Courses

With many online machine learning courses available, choosing the right one can be daunting. It's essential to assess your individual goals and learning styles to find a course that strikes the perfect balance between theory and practical application. 

After carefully researching more than 20 ML courses, we narrowed down our findings based on the following criteria:

• Course Duration: We considered the length of the courses, ensuring we cater to various learning preferences
• Flexibility: We prioritized courses with flexible learning options to accommodate the busy schedules of working professionals.
• Instructor Expertise: We valued courses led by experienced instructors with proven track records in the field of machine learning.
• Course Reviews: We analyzed feedback from past students to gauge the quality of the courses.
• Industry-Recognized Certification: We favor courses that provide industry-recognized machine learning certifications upon completion, as they can boost your credibility.
• Hands-on Projects: We placed emphasis on courses that incorporate hands-on projects, as they provide opportunities for practical application.

The 15 Best Machine Learning Courses in 2023

1. [Coursera] Machine Learning Specialization

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Why we chose this course

Our findings show that this 3-course specialization is designed to help students master fundamental AI concepts and develop practical machine learning skills. If you're looking for a top-notch Machine Learning and AI education, you can't go wrong with the highly esteemed Stanford University, as this may be the best machine learning course for beginners.

Taught by renowned AI expert Andrew Ng, this updated and expanded version of his pioneering course covers key topics, including supervised learning, unsupervised learning, and best practices for AI and machine learning development.

Students will work on hands-on projects using popular Python machine-learning libraries, such as NumPy and scikit-learn. You’ll also learn to train a neural network with TensorFlow, build decision trees and random forests, and build a deep reinforcement learning model.

With a strong focus on real-world applications, this course has garnered a 4.9-star rating from over 10,000 students and has already enrolled more than 183,000 learners. To cap it all off, you’ll earn a certificate endorsed by Stanford and DeepLearning.AI for your resume.

Pros

• Taught by AI expert Andrew Ng
• Comprehensive curriculum including supervised and unsupervised learning
• Includes hands-on projects and real-world applications
• High student satisfaction rate with a 4.9-star rating
• One of the best machine learning courses for beginners

Cons

• None

2. [Udemy] Machine Learning A-Z: AI, Python & R + ChatGPT Bonus

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Why we chose this course

Our research indicates that this comprehensive course is perfect for those who want to create machine learning algorithms in Python and R.

Designed by two data science experts, this course includes 42 hours of on-demand video, 382 lectures, and various practical exercises based on real-life case studies. Updated in 2023, it even features a bonus downloadable resource on how to use ChatGPT to boost your machine learning skills.

The curriculum includes a wide range of topics, such as regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, and dimensionality reduction. Moreover, the course offers full lifetime access and a certificate of completion - ensuring you have all the resources you need to start out in the field. 

Pros

• Comprehensive coverage of machine learning topics
• Practical exercises based on real-life case studies
• Python and R code templates included
• Suitable for beginners with just high school mathematics knowledge

Cons

• Covers theory at a very basic level

3. [Udacity] Machine Learning Nanodegree

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Why we chose this course

Our findings show that this Nanodegree program, co-created with Kaggle, will teach you how to master supervised, unsupervised, reinforcement, and deep learning fundamentals.

Over a 6-month period, you'll complete several engaging projects in various domains, such as predicting housing prices, finding donors for a charity, and creating customer segments.

Upon completion, you’ll get personalized feedback on your projects, helping you build a job-ready project portfolio. You'll also gain access to course materials and a verified Nanodegree credential to showcase your hard-earned skills.

Pros

• Co-created with Kaggle, ensuring industry relevance
• Comprehensive curriculum covering various aspects of machine learning
• Multiple hands-on projects to build a strong portfolio
• Personalized feedback on projects and access to expert instructors
• Verified Nanodegree credential upon completion

Cons

• None

4. [FreeCodeCamp] Machine Learning for Everybody

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Why we chose this course

This free video course is perfect for those just starting their machine learning journey. Our findings show that this course is easily accessible and tailored for absolute beginners, as it covers the essentials of machine learning while teaching you how to use TensorFlow to implement various machine learning algorithms.

If you’re looking for the best machine learning courses for beginners that are also free, this option has a comprehensive curriculum that covers important topics such as k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and neural networks.

As part of FreeCodeCamp's mission to help people learn to code for free, this course has garnered overwhelmingly positive reviews for its clear explanations and effective teaching style, ensuring that you'll have a great learning experience.

Pros

• Accessible to absolute beginners with no prior machine learning experience
• Covers a wide range of machine learning topics and TensorFlow implementation
• Created by an MIT graduate and supported by a Google grant
• Positive user reviews on teaching style and concept explanations
• Free and easily accessible on YouTube

Cons

• Lack of personalized support or instructor interaction due to the YouTube format

5. [Coursera] Machine Learning Specialization

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Why we chose this course

Our research indicates that this 4-course specialization from the University of Washington, taught by leading researchers, provides you with a comprehensive, hands-on introduction to machine learning.

This program includes a range of ML classes to ensure that you'll gain practical experience in prediction, classification, clustering, and information retrieval through real-world case studies.

Aimed at an intermediate-level learner, the curriculum can be completed in approximately 7 months at a pace of 3 hours per week, ensuring a manageable yet in-depth learning experience. 

It covers vital topics such as price predictions, sentiment analysis, and similarity analysis. By the end of this program, you'll walk away with valuable applied machine learning and Python programming experience under your belt.

Pros

• Provides an in-depth exploration of major ML areas
• Contains Real-world case studies and hands-on experience
• Teaches optimization algorithms and techniques like MapReduce
• Each module contains optional content for students who want to delve deeper

Cons

• Students are required to download and install a library of content materials

6. [Udemy] Machine Learning, Data Science and Deep Learning with Python

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Why we chose this course

Our analysis of this hands-on course revealed that it delves into a wide range of machine learning, AI, and data mining techniques used by major tech giants like Google and Amazon. 

This course features over 100 lectures spanning 15 hours of video content and includes hands-on Python code examples for an engaging learning experience.

After carefully reviewing feedback from past students, we found that this course focuses on the practical understanding and application of machine learning techniques without overwhelming you with deeply mathematical explanations.

To ensure you're well-rounded in your knowledge, this course also covers advanced topics like generative models, variational auto-encoders (VAE's), and generative adversarial networks (GAN's). 

Pros

• Covers advanced topics, including generative models, VAE’s, and GAN’s
• Emphasizes real-world applications of techniques
• Includes hands-on Python code examples
• Taught by an experienced machine learning professional 

Cons

• Content is very fast-paced 

7. [Educative] A Practical Guide to Machine Learning with Python

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Why we chose this course

This beginner course teaches you the fundamental principles and techniques of machine learning, with a focus on coding basic machine learning models, but without video lessons. This is the educative approach, and for many students, it’s a fantastic way to learn this essential skill.

Our findings show that It covers a variety of common algorithms, such as linear regression, logistic regression, SVM, KNN, and decision trees, while also providing an overview of different learning paradigms like supervised and unsupervised learning.

We value courses that emphasize hands-on learning, and this one offers live code environments within the browser, enabling you to practice as you learn. To help you track your progress and showcase your skills, the course also includes built-in assessments and completion certificates, ensuring you get the most out of your learning experience.

Pros

• Designed for beginners with a basic knowledge of machine learning
• Provides live code environments in the browser
• Twice as fast as video courses, ensuring an efficient learning experience
• Built-in assessments allow students to track their progress

Cons

• Entirely text-based, which may not be suitable for visual learners 

8. [SpringBoard] Machine Learning Bootcamp

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Why we chose this course

This comprehensive 6-month online program will equip you with the skills and guidance needed to become a machine learning engineer. Developed in partnership with UC San Diego, the curriculum covers in-demand topics like battle-tested machine learning models, deep learning, computer vision, and deploying ML systems to production.

Our analysis of this course revealed that you can expect to receive a well-rounded learning experience with access to expert-curated content, real-world projects, and support from industry mentors and career coaches.

The capstone project at the end of the course is a fantastic opportunity for you to build a unique, portfolio-ready showcase of your newly acquired skills.

Pros

• Covers essential ML topics, including data, algorithms, and engineering
• Includes over 15 projects and a capstone project for your portfolio
• Students can choose from 4 optional specializations, including deep learning
• Access to 1:1 mentorship, unlimited mentor calls, and a supportive online community
• Graduates receive a UC San Diego Extended Studies certificate

Cons

• None

9. [DataCamp] Machine Learning with scikit-learn

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Why we chose this course

This advanced-level course is perfect for learning how to build and fine-tune predictive models using Python and Scikit-learn, a popular and user-friendly machine learning library.

As you progress through the course, you'll have the opportunity to apply supervised learning techniques to real-world datasets across various problem domains, such as healthcare, politics, and email filtering.

Our findings show that this course is thoughtfully divided into four sections, covering classification, regression, model fine-tuning, and preprocessing with pipelines.

Although it’s fast-paced, this course will provide you with valuable hands-on experience in building predictive models, tuning parameters, and evaluating model performance on unseen data.

Pros

• Focus on Scikit-learn, a popular and user-friendly machine learning library
• Covers a wide range of supervised learning techniques and applications
• Hands-on approach with real-world datasets
• In-depth explanation of model fine-tuning and preprocessing

Cons

• Does not cover unsupervised or reinforcement learning

10. [Edureka] Machine Learning Full Course

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Why we chose this course

Our team found this free course is perfect for beginners looking to master machine learning algorithms. Our research indicates that it covers a wide range of topics, such as supervised, unsupervised, and reinforcement learning techniques, along with practical demonstrations using Jupyter Notebook.

The instructor thoughtfully delves into various algorithms like Linear and Logistic Regression, Decision Trees, Random Forest, KNN, and Naive Bayes.

Overall, this course will provide you with the essential knowledge to start your journey to becoming a machine learning specialist.

Pros

• Completely free course, accessible to learners of all levels
• Comprehensive curriculum covering various machine learning algorithms
• Includes practical demonstrations using Jupyter Notebook
• Provides insights into ML Engineer job trends, salaries, and interview questions

Cons

• Lack of personalized support or instructor interaction due to the YouTube format

11. [Google] Machine Learning Crash Course

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Why we chose this course

Where better to learn machine learning than at Google? This fast-paced, practical machine learning expert course has 25 lessons, 30 hands-on exercises, insightful video lectures from Google researchers, real-world case studies, and interactive visualizations.

As indicated by our tests, this course covers essential machine learning engineering concepts such as reducing loss, feature crosses, regularization, classification, neural networks, and embeddings.

It also provides valuable answers to key questions about best practices, loss measurement, gradient descent, model effectiveness, data representation, and building deep neural networks. 

Overall, this free Google course is a fantastic opportunity to dive into the world of machine learning engineering.

Pros

• Fast-paced and practical introduction to machine learning
• Lectures from Google researchers and real-world case studies
• Hands-on practice exercises and interactive visualizations
• Comprehensive coverage of machine learning concepts, engineering, and real-world applications

Cons

• Students may need to enroll in a secondary course or conduct self-research through data science books to gain a well-rounded understanding 

12. [LinkedIn Learning] Machine Learning with Python: Foundations

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Why we chose this course

Our analysis of this short course shows it to be an approachable and beginner-friendly way to gain a comprehensive understanding of machine learning while also receiving step-by-step guidance on how to get started using Python.

You'll learn about the different ways that machines can learn and how to collect, understand, and prepare data for machine learning.

What's great about this online course is that it also provides guided examples, which help you build, evaluate, and interpret the results of machine learning models in Python. Perfect for anyone looking to dive into the world of machine learning with a hands-on approach.

Pros

• Beginner-friendly and approachable course content
• Provides step-by-step guidance on using Python for machine learning
• Covers data collection, preparation, and model evaluation

Cons

• No hands-on projects

13. [edX] Machine Learning with Python: A Practical Introduction

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Why we chose this course

Based on our observations, this 5-week self-paced course delves into both supervised and unsupervised learning methods.

You'll explore a variety of algorithms, such as linear and non-linear regression, K-Nearest Neighbor, decision trees, logistic regression, and support vector machines. The course also covers clustering, dimensionality reduction, and recommender systems.

What makes this course stand out is that it's taught by a Senior Data Scientist at IBM, and it's part of the Professional Certificate in Python Data Science and IBM Data Science programs. By successfully completing the course, you'll not only gain valuable knowledge but also earn a verifiable digital skill badge to showcase your expertise.

Pros

• Comprehensive coverage of supervised and unsupervised learning methods.
• Taught by an experienced Senior Data Scientist at IBM
• Self-paced, allowing learners to progress at their own speed
• Offers a verifiable digital skill badge upon successful completion

Cons

• The course moves very quickly, especially for those with little Python knowledge

14. [Simplilearn] Machine Learning With Python Full Course 2022

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Why we chose this course

This intermediate-level course is perfect for gaining a strong foundation and work-ready skills for aspiring machine learning and artificial intelligence professionals.

Our analysis of this course shows that it covers an impressive range of topics, such as supervised and unsupervised learning, time series modeling, linear and logistic regression, kernel SVM, KMeans clustering, Naive Bayes, decision tree, random forest classifiers, boosting, and bagging techniques.

What we particularly appreciate about this program is that it offers hands-on experience in data preprocessing, time series, text mining, and other essential machine learning concepts. This makes it an ideal choice for anyone looking to kick-start their career in this exciting field.

Pros

• Comprehensive coverage of machine learning topics
• Provides hands-on experience in crucial machine learning concepts
• Includes downloadable data sets 

Cons

• Lack of personalized support or instructor interaction due to the YouTube format

15. [Codecademy] Intro to Machine Learning

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Why we chose this course

Our findings show that this intermediate-level course provides a great level of depth on the technical aspects of machine learning, which is perfect for data analysts seeking to upgrade their skills or professionals who are looking to analyze datasets using machine learning.

The course covers key concepts such as linear regression, logistic regression, decision trees, clustering, and supervised learning algorithms. Moreover, it introduces artificial intelligence decision-making using the Minimax algorithm.

What really stands out about this online course is that it includes eight hands-on projects and nine quizzes to solidify your understanding. Though it requires a comfortable knowledge of Python, the course is taught on an interactive platform that offers AI-driven recommendations for personalized learning, ensuring you get the most out of the experience.

Pros

• Explores the two types of supervised learning algorithms
• Offers eight hands-on projects and nine quizzes for practical application
• Interactive platform with AI-driven recommendations for personalized learning

Cons

• None

Why Should You Study Machine Learning in 2024?

In the era of big data, companies of all sizes across a wide range of industries are looking to uncover hidden insights from their ever-growing troves of valuable data.

This is where machine learning shines, as it can help with data-driven decision-making and provide predictive analytics about future trends.

In fact, interest in ML has boomed in the last five years, even outpacing its closely-related cousins of data science, deep learning, and artificial intelligence, as shown by the Stack Overflow trends below. 

Huge relative growth in Machine Learning interest, Source: Stack Overflow

It’s no wonder that there’s such high demand for data-driven professionals with ML skills. If you’re on the fence about studying ML, perhaps some of the following reasons will convince you that it’s an ideal skill to add to your resume in 2023.

1. Relevance in the job market: Machine Learning Specialists and Engineers have been on LinkedIn’s emerging jobs list from 2016-2022 with a 74% annual growth rate. By learning machine learning, you're positioning yourself at the forefront of the job market and increasing your chances of landing in-demand roles.
2. Creating innovative solutions: Machine learning has the potential to revolutionize industries by automating processes, enhancing decision-making, and unlocking new insights.
3. Lucrative salaries: As of 2023, machine learning engineers can earn average salaries in excess of $150K, while machine learning specialists can earn over $95K
4. Job growth projections: The U.S. Bureau of Labor Statistics projects a 22% growth rate in computer and information research scientist roles, including machine learning positions, from 2021 to 2031.

Why Do I Need To Know AI in 2024?

One thing is for sure: we're all slowly becoming acquainted with the various forms of AI in our professional and personal lives. But do you need to be an AI wizard to excel in 2024? Interesting question: let's dive deeper.

In the constantly evolving landscape of technology, the integration of artificial intelligence has become increasingly significant, and there is no doubt about that. While AI is a distinct field, its impact is becoming hard to ignore across various industries.

Professionals are now tasked with the responsibility of leveraging AI to extract actionable insights and drive innovation. This involves not just the manipulation of structured and unstructured data but also the application of advanced analytical techniques. AI, particularly in the form of machine learning, plays a pivotal role in this process.

Machine learning enables professionals to create predictive models that can learn from data, thereby enhancing the accuracy and effectiveness of their analyses. Understanding AI principles, especially machine learning algorithms, can immensely benefit anyone looking to stay competitive.

This knowledge allows them to automate complex processes, optimize predictive models, and implement solutions that are both innovative and efficient. While not every role will demand deep expertise in AI, a foundational understanding is becoming increasingly important in the field.

If you're curious to learn more about the transformative power of generative AI across various industries, I'd highly recommend attending DataCamp's free digital conference, RADAR: AI Edition in June 2024.

This event is a fantastic opportunity to discover how businesses and individuals can unlock their full potential with AI. You'll hear from industry leaders like Megan Finck, the Global Head of Talent Acquisition (Data & AI) at Boeing, and Sadie St Lawrence, the CEO of Women in Data. Nnamdi Iregbulem, Partner at Lightspeed Venture Partners, and Eric Seigel, Founder of Machine Learning Week and bestselling author, will also share their insights.

Additionally, Carolann Diskin, Senior Technical Program Manager at Dropbox, and Julien Simon, Chief Evangelist at Hugging Face, will discuss the future of AI and its applications. Don't miss this chance to learn from experts about the latest advancements and strategies in AI.

Conclusion

In today's fast-paced technological landscape, machine learning has emerged as a critical skill for data-driven professionals and newcomers alike who want to stay ahead of the curve while leading innovation in the ever-evolving field of modern data.

To help you on your journey, we’ve found the 15 best machine learning courses online in 2023, with a range of free and paid options for various skill levels and backgrounds. So whether you’re a data scientist, Python pro, or beginner that wants to break into the field, there’s something for you on our list.

When selecting the ideal ML course for you, remember that it's important to consider your goals and learning preferences while also striking a balance between theory and practical skills. Investing in your machine learning education will set you on your way to a rewarding and successful career in this exciting field.

Good luck, and happy learning!

Want to continue your machine learning journey? Check out:

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Frequently Asked Questions

1. What Are the Best Machine Learning Courses?

The best machine learning course online for you depends on your background, learning style, and goals. For beginners and experienced learners alike, we’d recommend Coursera’s machine learning specialization which is offered by Stanford’s world-renowned AI department. But overall, any of the options in our list are contenders for the best course for machine learning.

2. Is Six Months Enough for Machine Learning?

Six months can be enough for machine learning if you dedicate consistent time and effort. While becoming an expert may take longer, within six months, you can gain a strong foundation in machine learning concepts, tools, and techniques. As one of the most comprehensive machine learning programs on our list, Udacity’s machine learning Nanodegree can be completed in 6 months.

3. What Are the Three Types of Machine Learning?

The three types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model with labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning trains an agent through trial and error based on rewards. 

4. Is Python Enough for Machine Learning?

Python is a great starting point for machine learning, as it offers many libraries and frameworks like TensorFlow, Scikit-learn, and PyTorch. Exploring other languages, such as R, Java, or Julia, can also be beneficial to gain a strong understanding of machine learning.

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