The editors at Solutions Review have compiled this list of the best machine learning courses on Udemy to consider if you’re looking to grow your skills.
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 on Udemy if you’re looking to grow your skills for work or play. Udemy is one of the top online education platforms in the world with more than 130,000 courses, expert instruction, and lifetime access that allows you to learn on your own schedule. As you can see below, we broke the best machine learning courses on Udemy down into categories based on the recommended proficiency level. Each section also features our inclusion criteria. Click GO TO TRAINING to learn more and register.
Best Machine Learning Courses on Udemy for Beginners
Note: We included courses with more than 800 reviews and a rating of 4.2 stars or better.
Description: If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.
Description: In this introductory course, you will be guided through wilderness of machine learning for data science. Accessible to everyone, this introductory course not only explains machine learning, but where it fits in the “technosphere” around us, why it’s important now, and how it will dramatically change our world today and for days to come. This course is a great primer for starting Python or R, introducing the fundamentals that you need before going hands-on.
Description: In this course, you will first talk about clustering. This is where instead of training on labels, you will try to create your own. You’ll do this by grouping together data that look alike. Next, the instructors will go into Gaussian mixture models and kernel density estimation, where they will talk about how to “learn” the probability distribution of a set of data. All the algorithms they’ll talk about in this course are staples in machine learning and data science, so if you want to know how to automatically find patterns in your data with data mining and pattern extraction, without needing someone to put in manual work to label that data, then this course is for you.
Description: There are many courses on machine learning already available. The instructor built this course to be the best introduction to the topic. No subject is left untouched, and students never leave any area in the dark. If you take this course, you will be prepared to enter and understand any sub-discipline in the world of machine learning. Requirements include a basic understanding of terminal and command line usage, and the ability to read basic math equations.
Description: This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
Description: This course is part of a series on data engineering and will show you the basics of machine learning for data engineers. The module is geared toward answering questions for the Google Certified Data Engineering exam. This is not a general course or introduction to machine learning. This is a very focused course for learning the concepts you’ll need to know to pass the certification.
Description: This course, which is instructed by a data scientist with 4-years of experience, is designed for absolute beginners to data science and machine learning. It covers each aspect of Python languages required in data science, machine learning and deep learning. This course is for beginners in Python development, programming, data science, and machine learning. Python for Data Science has a nearly perfect 4.9 stars.
Description: The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world datasets. This course covers several techniques in a practical manner as well. The course is targeted towards students wanting to gain a fundamental understanding of deep and machine learning models. Basic knowledge of programming is recommended.
Best Machine Learning Courses on Udemy for Intermediate
Note: We included courses with more than 1,000 reviews and a rating of 4.2 stars or better.
Description: This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy. In addition to the 9-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You’ll also get four hands-on labs that allow you to practice what you’ve learned, and gain valuable experience in model tuning, feature engineering, and data engineering.
Description: This is the first and only online course where you can learn how to deploy machine learning models. In this course, you will learn every aspect of how to put your models in production. The course is comprehensive and yet easy to follow. Throughout this course, you will learn all the steps and infrastructure required to deploy machine learning models professionally. In this course, you will have at your fingertips, the sequence of steps that you need to follow to deploy a machine learning model, plus a project template with full code, that you can adapt to deploy your own models.
Description: Machine Learning Practical brings the best industry professionals together. Each presenter has a unique style which is determined by their experience. Students will need to adjust on the fly to complete the course. The module will demystify how real data science projects like instead of looking at only polished samples which are only introducing you to the matter but not providing the real experience. This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of data science.
Description: Learn how to build recommender systems from one of Amazon’s pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation technologies. this course is very hands-on; you’ll develop your own framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.
Description: In this course, Penny de Byi introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years of experience working with games and computer graphics. Throughout the course, you will follow along with hands-on workshops designed to teach you about the fundamental machine learning techniques, distilling the mathematics in a way that the topic becomes accessible to the most noob of novices.
Description: In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable, and more interpretable machine learning models. This is the most comprehensive online course in variable selection. You will learn a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features. At the end of the course, you will have a variety of tools to select and compare different feature subsets and identify the ones that return the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production.
Best Machine Learning Courses on Udemy for Experts
Note: We included courses with more than 500 reviews and a rating of 4.3 stars or better.
Description: This practice exam offers a realistic, full-length simulation of what you can expect in the AWS MLS-C01 exam. It’s not a “brain dump,” but a complete, 65-question, 3-hour practice exam with original questions of the same style, topics, difficulty, and breakdown of the real exam. It’s a great test of your readiness before you decide to invest in the real exam, and a great way to see what sorts of topics the exam will touch on. The instructors also include a 10-question warmup test that will give you a rough idea of your readiness.
Description: In this course, the instructors take a very methodical, step-by-step approach to build up all the theory you need to understand how the SVM really works. You are going to use Logistic Regression as a starting point, which is one of the very first things you learn about as a student of machine learning. So if you want to understand this course, just have a good intuition about Logistic Regression, and by extension have a good understanding of the geometry of lines, planes, and hyperplanes.
Description: With more than 500 slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro! This course covers the most important concepts without any fillers or irrelevant information. The module is designed for developers and data scientists wanting to get certified in AWS Machine Learning.
Best Machine Learning Courses on Udemy for All Levels
Note: We included courses with more than 1,000 reviews and a rating of 4.2 stars or better.
Description: This course was designed by two professional data scientists to share knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. The instructors will walk you step-by-step through the field 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. The module is also packed with practical exercises that are based on real-life examples. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Description: This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms. The module is designed for both beginners with some programming experience or experienced developers looking to make the jump to data science. It also features more than 100 HD video lectures and detailed code notebooks for every lecture.
Description: This comprehensive course is comparable to other data science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture, this is one of the most full-featured modules for data science and machine learning on Udemy. Instructors will teach you how to program with R, how to create amazing data visualizations, and how to use machine learning with R!
Description: Learn data science and machine learning from scratch, get hired, and have fun along the way with the most modern, up-to-date data science course on Udemy. This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away. If you already know how to program, you can dive right in and skip the section where instructors teach you Python from scratch. If you are completely new, instructors take you from the very beginning and actually teach you Python and how to use it in the real world for various projects.
Description: Scala and Spark are two of the most in-demand skills right now, and with this course, you can learn them quickly and easily. This course comes with full projects including topics such as analyzing financial data or using machine learning to classify eCommerce customer behavior. The instructors teach the latest methodologies of Spark 2.0 so you can learn how to use SparkSQL, Spark DataFrames, and Spark’s MLlib. After completing this course you will feel comfortable putting Scala and Spark on your resume!
Description: You’ll learn the fundamental tools of the Bayesian method – through the example of A/B testing – and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future. The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied. This course is for students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work.
Description: You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells, and much more! Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.
Description: This course follows directly from the course Unsupervised Machine Learning for Cluster Analysis, where you can learn how to measure the probability distribution of a random variable. In this course, you’ll learn to measure the probability distribution of a sequence of random variables. This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. All of the materials of this course can be downloaded and installed for free.
Description: This course is divided into three main parts covering Python programming fundamentals, financial analysis in Python, and AI/ML application in finance and banking. The course contains mini-challenges and coding exercises in almost every video so you will learn in a practical and easy way. You will also build more than 6 full practical projects that you can add to your portfolio of projects to showcase your future employer during job interviews. There is no prior experience required, even if you have never used Python or any programming language before.
Description: Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science. At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Instructors take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional. The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.
Description: Learn about cloud-based machine learning algorithms, how to integrate with your applications, and certification preparation. The module features hands-on labs, guidance on how to deploy a notebook instance on the AWS Cloud, insight into algorithms provided by SageMaker service, and training on optimizing and deploying models. The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python.
Description: After completing this course you will be able to confidently build predictive machine learning and deep learning models to solve business problems and create a business strategy, answer machine learning related interview questions, and participate and perform in online data analytics competitions such as Kaggle competitions. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in real-world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.
Description: This course is all about ensemble methods. In this course, you’ll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of. To motivate the discussion, students will learn about an important topic in statistical learning, the bias-variance trade-off. Instructors will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.
Description: Led by deep learning guru Dr. Jon Krohn, this first entry in the Machine Learning Foundations series will give you the basics of mathematics such as linear algebra, matrices, and tensor manipulation, that operate behind the most important Python libraries and machine learning and data science algorithms. Throughout each of the sections, you’ll find plenty of hands-on assignments and practical exercises to get your math game up to speed!
Description: After completing this course you will be able to confidently build predictive Machine Learning models to solve business problems and create a business strategy, answer Machine Learning related interview questions, and participate and perform in online data analytics competitions such as Kaggle competitions. With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
Description: The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts. The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus, and Probability Theory. The course also includes projects and quizzes after each section to help solidify your knowledge of the topic as well as learn exactly how to use the concepts in real life. At the end of this course, you will not have not only the knowledge to build your own algorithms, but also the confidence to actually start putting your algorithms to use in your next projects.
Description: This comprehensive course is designed to be on par with bootcamps that usually cost thousands of dollars. Students will get exclusive access to weekly live video streams where instructors will go through interactive machine learning projects. You’ll also be able to directly ask questions during the streams that will coincide with section launches corresponding to new machine learning algorithms added to the course content! These weekly streams will include live Q&A with the instructor of the course, Jose Portilla, as well.
Description: Through this course, you will learn about the current state of AI, how it’s disrupting businesses globally and in diverse fields, how it might impact your current role and what you can do about it. This course also dives into the various building blocks of AI and why it’s necessary for you to have a high-level overview of these topics in today’s data-driven world. This course does not assume any prior knowledge of artificial intelligence or its associated terms.
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