The 14 Best Machine Learning Courses & Online Training for 2023
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By
Tim King
, Executive Editor at Solutions Review - Best Practices,
Machine learning involves studying computer algorithms that improve automatically through experience. It is a sub-field of artificial intelligence where machine learning algorithms build models based on sample (or training) data. Once a predictive model is constructed it can be used to make predictions or decisions without being specifically commanded to do so. Machine learning is now a mainstream technology with a wide variety of uses and applications. It is especially prevalent in the fields of business intelligence and data management. With this in mind, we’ve compiled this list of the best machine learning courses and online training to consider if you’re looking to grow your AI or data science skills for work or play. This is not an exhaustive list, but one that features the best machine learning courses and training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.
The Best Machine Learning Courses and Online Training
Learn the Basics of Machine Learning
Platform: Codecademy
Description: This course covers the foundational machine learning algorithms that will help you advance in your career. Whether you’re trying to analyze a dataset using machine learning, or you’re a data analyst trying to upgrade your skills, this course is the best place to start. You should be comfortable with Python, including functions, control flow, lists, and loops.
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Machine Learning (Stanford)
Platform: Coursera
Description: This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
Related paths/tracks: Machine Learning with Python (IBM), Machine Learning Specialization (University of Washington), Mathematics for Machine Learning Specialization (Imperial College London), Machine Learning with TensorFlow on Google Cloud Platform Specialization (Google Cloud)
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Machine Learning for Everyone
Platform: DataCamp
Description: In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered.
Related paths/tracks: Machine Learning for Business, Machine Learning with Tree-Based Models in Python, Machine Learning with caret in R
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Machine Learning Certification Training using Python
Platform: Edureka
Description: Edureka’s Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This training exposes you to concepts of statistics, time series and different classes of machine learning algorithms like supervised, unsupervised, and reinforcement algorithms. Throughout the course, you’ll be solving real-life case studies on media, healthcare, social media, aviation, and HR.
Related paths/tracks: Graphical Models Certification Training, Reinforcement Learning, Natural Language Processing with Python
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Data Science: Machine Learning (Harvard)
Platform: edX
Description: Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
Related paths/tracks: Machine Learning for Data Science and Analytics (Columbia), Machine Learning Fundamentals (UC San Diego), Machine Learning with Python: from Linear Models to Deep Learning
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An Introduction to Machine Learning
Platform: Experfy
Description: As an introduction to machine learning, this course is presented at a level that is readily understood by all individuals interested in machine learning. This course provides a history of machine learning, defines data, and explains what is meant by big data; and classifies data in terms of computer programming. It covers the basic concept of numeral systems and the common numeral systems used by computer hardware to establish programming languages. Providing practical applications of machine learning.
Related paths/tracks: Machine Learning for Predictive Analytics, Feature Engineering for Machine Learning, Supervised Learning: Classification, Supervised Learning: Linear Regression, Unsupervised Learning: Clustering
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Machine Learning Course Online
Platform: Intellipaat
Description: This machine learning course will help you master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology. Intellipaat’s machine learning certification training comes with 24/7 support, multiple assignments, and project work to help you gain real-world exposure.
Related path/track: Artificial Intelligence Course and Training
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Artificial Intelligence Foundations: Machine Learning
Platform: LinkedIn Learning
Description: In this course, we review the definition and types of machine learning: supervised, unsupervised, and reinforcement. Then you can see how to use popular algorithms such as decision trees, clustering, and regression analysis to see patterns in your massive data sets. Finally, you can learn about some of the pitfalls when starting out with machine learning.
Related paths/tracks: Essential Math for Machine Learning: Python Edition, Applied Machine Learning: Algorithms, Applied Machine Learning Foundations
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Machine Learning Training
Platform: Mindmajix
Description: Mindmajix Machine Learning Training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. You will gain in-depth knowledge of all the concepts of machine learning including supervised and unsupervised learning, algorithms, support vector machines, etc., through real-time industry use cases, and this will help you in clearing the Machine Learning Certification Exam.
Related path/track: Machine Learning with Python Training
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Understanding Machine Learning
Platform: Pluralsight
Description: Have you ever wondered what machine learning is? That’s what this course is designed to teach you. You’ll explore the open-source programming language R, learn about training and testing a model as well as using a model. By the time you’re done, you’ll have a clear understanding of exactly what machine learning is all about.
Related paths/tracks: Understanding Machine Learning with Python, Understanding Machine Learning with R, Machine Learning: Executive Briefing, How Machine Learning Works, Deploying Machine Learning Solutions
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Machine Learning Certification Course
Platform: Simplilearn
Description: This machine learning online course offers an in-depth overview of machine learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time-series modeling. Learn how to use Python in this machine learning certification training to draw predictions from data.
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Data Science and Machine Learning with Python – Hands On!
Platform: Skillshare
Description: If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry – and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice.
Related paths/tracks: Demystifying Artificial Intelligence: Understanding Machine Learning, Goal-Driven Artificial Intelligence and Machine Learning
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Become a Machine Learning Engineer
Platform: Udacity
Description: Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in the industry.
Related paths/tracks: Intro to Machine Learning with PyTorch, Intro to Machine Learning with TensorFlow
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Machine Learning A-Z: Hands-On Python & R In Data Science
Platform: Udemy
Description: This course has been designed by two professional data scientists that can share their knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. The course will walk you step-by-step into the world of machine learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of data science.
Related paths/tracks: Python for Data Science and Machine Learning Bootcamp, Machine Learning, Data Science and Deep Learning with Python, Data Science and Machine Learning Bootcamp with R
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NOW READ: The Best Machine Learning Certifications Online
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Tim King
Executive Editor
Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.
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