Machine Learning is a subset of artificial intelligence that puts you in charge of teaching machines how to learn. This growing field of computer science opens up a world of opportunity for qualified machine learning engineers and is set to be an in-demand profession in the years to come. The list of industries using machine learning to speed up decision-making and make areas of their businesses more efficient has kept growing at great speed. In addition, there are plenty of machine learning courses available for the enthusiasts planning on gaining new skills or even breaking into the industry.
If your goal is to learn machine learning in 2022, these are the best machine learning courses to dive right in. They cover creating machine learning algorithms, the mathematical applications behind them and building machine learning applications and systems. We will cover exciting topics including deep learning, artificial neural networks and utilizing algorithms through various programming languages.
What is Machine Learning?
Machine learning is an exciting field of computer science and a practical application of artificial intelligence. Machine learning enables computers and other machinery to use both incoming and historical data to make improvements without being directly programmed. Programmers essentially teach machines to think like humans do.
Much like a human learns through the input of new information, machine learning works in the same way by feeding through data in order for the deep learning process to be initiated. Machines are programmed to look for patterns in the data in order to make improvements and decisions without human intervention. Machine learning algorithms are automatically applied to complex mathematical calculations and now that’s happening faster than ever before.
Some common examples of machine learning are:
- YouTube video recommendations
- Email spam filters
- Malware filtering
- Alexa and Siri
- Facebook sponsored ad content
Why Learn Machine Learning?
Machine learning is set to grow its market share to over $30 billion by 2024, making it one of the fastest growing areas in the tech sphere. With the rising tide of self-driving cars, speech recognition applications and devices, web search algorithms and automated marketing, machine learning has crept into almost every inch of modern daily life.
Further to this, industries that work with masses of data, such as E Commerce giants like Amazon, recognise the importance of using machine learning to automate areas of their business by gathering insights from data in real time. This enables businesses to make decisions faster and maintain a competitive edge. As the demand for machine learning in industries such as finance, healthcare and retail continues to grow, so will the demand for machine learning engineers who can develop machine learning algorithms, applications and programmes.
Top Criteria for Machine Learning Courses
We have selected the best machine learning courses out there by implementing a stringent list of criteria when making a selection.
- The courses must focus entirely on machine learning, not other subsets of artificial intelligence.
- The machine learning courses must utilize open-source programming languages like Python, R and Octave.
- If courses use commercial programming packages, we do not include their courses in our list. They must use open-source libraries only to make the courses more accessible for aspiring machine learning engineers.
- We only include courses that include practical exercises to help students get practical experience that reinforces learning.
- Courses must clearly explain the mathematics behind the algorithms
- Courses should be self-paced to allow students to work at their own pace in their own time, making it more accessible for busy professionals.
- Instructors must be reputable, charismatic and present engaging lectures that keep students interested.
- Have excellent ratings and reviews
- Offer students good value for money
With such strict criteria, some courses do not make the cut. We hope this will help you select a suitable course that aligns with your goals and time constraints.
In addition to our own selection criteria, there are some skills you should have before attempting to learn machine learning. If your dream is to become a machine learning engineer, there are some skills and knowledge you should develop before you get started.
Statistics is focused on data collection, analysis and interpretation. As you may have gleaned from the discussion above, data is at the heart of machine learning. There are two types of statistics: descriptive and inferential. Descriptive statistics describe the data and are presented as mean value’s, standard deviations, medians and so forth. Inferential statistics draws conclusions from a sample set of data by making estimations or testing a hypothesis. A machine learning engineer should be comfortable with statistical analysis in order to be able to infer this to machine learning models.
Probability refers to the likelihood of an event occurring. When data is used in decision-making, decisions are based on the probability of ‘x’ occurring if ‘y’ happens and so forth. When embarking on a machine learning course, you can expect to use rules of probability, notation and probability distribution in your machine learning models.
One of the most important machine learning skills to learn before embarking on this learning path is linear algebra. You must possess an understanding of coding algorithms, linear transformations and Tensor in order to build the complex neural networks required to teach machines to think like people and engage in deep learning.
You cannot build a machine learning model without understanding calculus. You must understand integration, differentiation, partial derivatives and chain rule in order to build neural networks.
Once you have created your machine learning algorithms, they are applied using coding. Therefore knowledge of programming languages is imperative to a successful career as a machine learning engineer. Programming is one of the fundamental machine learning concepts. You will need to have a decent understanding of Python programming. Skills in R, Octave and C++ will also be of great value to your progress.
Data visualization is one of the essential machine learning skills to learn before starting a machine learning course. Data visualization aids you in identifying patterns, outliers and corrupt data.
Top Machine Learning Online Courses In 2022
A pioneer in the field and one of the original machine learning courses, the aptly named Machine Learning course by Stanford University is one of the best machine learning courses around to equip you for a successful career in machine learning engineering. The course is presented by Andrew Ng on the Coursera online learning platform. It broke records with over 100k enrollments in its first two months being launched, making it undoubtedly one of the most popular machine learning courses to date. The course material and assignments are free to access, however the certificate of completion requires a fee.
You will start off by learning the mathematical principles that are core to machine learning, covering concepts in linear algebra and linear regression. Once the foundation has been laid the course jumps to various machine learning techniques and models including deep learning, building artificial neural networks and component analysis.
The course ends off with a practical component, covering how to build large-scale machine learning apps, systems and programs in the real-world. You will end the course with a solid understanding of machine learning models and techniques with the ability to successfully implement machine learning algorithms using Octave and Matlab.
Despite its practical component, the course is heavily based on teaching machine learning concepts to lay a solid foundation so if you want a very hands-on course, some of the others in our list may be more suitable for you.
The course material is broken down into manageable modules that run over an 11 week period, presented as video lectures and text. You will need to set aside around 6 hours a week to complete the course in the time frame, making it a top choice for busy professionals looking to expand their skills.
The Deep Learning Certification was created by Stanford University professors who are experts in the field. One of the three co-creators is Andrew Ng, a computer science professor and AI team leader at Baidu, also behind the wildly popular Stanford University Machine Learning course. Since its launch, this course has been completed by over 225k students. If you want to dive into all there is to know about deep learning, this course covers all the key topics including neural networks, hyperparameter tuning and convolutional neural networks.
The course is split into 5 modules with case studies being used to create real-world context in healthcare settings, self-driving cars, creating music and natural language processing. The course also includes practical assignments to practice machine learning techniques, as well as invaluable advice from professional machine learning engineers.
To successfully complete this course you will need to be able to set aside around 11 hours a week to study. Most students take approximately 3 months to complete the course.
If you are looking for a comprehensive machine learning course but don’t have the time to commit to a 4 year degree, consider enrolling in the Machine Learning Nanodegree. This program condenses course material into a 3 to 12 month period, allowing you to accelerate your career faster.
Udacity offers two different learning paths, one being the Intro to Machine Learning course and the other being the Machine Learning Engineer Course. The Intro to Machine Learning Course is best suited to beginners wanting to learn the fundamentals of machine learning, covering key concepts such as supervised learning models, data cleaning, unsupervised learning, machine learning algorithms and designing deep learning neural networks. You will, however, still need to have an understanding of programming languages like Python, statistics and probabilities.
The course does include a practical component to test your new skills.
The Machine Learning Engineer course is for more advanced students with intermediate Python skills and existing knowledge of machine learning algorithms in order to learn advanced machine learning techniques for real world applications.
Some course highlights include highly engaging content, quizzes, exclusive insight into industry best practices, one-to-one mentoring from experienced machine learning engineers and even career coaching to help you launch your career with your best foot forward.
For a practical introduction to machine learning, the Machine Learning A-Z course on Udemy is a great, affordable option. You will learn how to go about creating machine learning algorithms step-by-step, comprehensively covering important topics and teaching machine learning in Python and R. Created by two data scientists, the course content is suitable for both beginners and advanced students. With over 800k enrollments, this course is one of the most popular courses on Udemy to date.
As far as data science courses go, this offers great value for money. The course is presented over 285 videos, tallying up to almost 41 hours of video lectures. In addition to the videos, there are also 31 articles and 5 downloads to help you along your online learning journey. Learn all about data analysis and predictions - key to creating meaningful machine learning projects.
The hands-on approach of the course makes it the best machine learning course for those antsy to get going rather than spend a lot of time going over theory. Better still, there are no prerequisites for this course other than a basic grasp of high school mathematics. By the end of the course you will be able to build powerful machine learning models that solve real-world problems and select appropriate machine learning models for various challenges.
The course also includes practical exercises, downloadable Python and R coding templates, an in-depth Q&A section and thorough explanations of complex machine learning concepts including deep learning, natural language processing and reinforcement learning.
There are a number of deep learning courses out there but this course stands out having been designed by experts and presented on a reputable learning platform like edX. Expect to cover important topics like unsupervised learning, neural networks and various other machine learning concepts. You will learn how to build machine learning models and machine learning algorithms by using machine learning tools like Tensorflow, PyTorch and Keras.
To flex your new skills, the course ends off with a machine learning project where you will need to create, train and test a deep learning model to solve problems. By the end of this course you have mastered important deep learning concepts like convolutional networks and recurrent networks to be able to apply these concepts to real-world problems. You will be able to use natural language processing, recommender systems, object recognition and text analytics to build deep learning models.
The course is divided into 5 modules that each require 4 hours per week of time, so expect to spend up to 6 weeks on each module as they’re very in-depth and intensive.
If you’re looking for online courses designed by experts, this is a great place to start. The course has been developed by two leading researchers and machine learning professors. The course will teach machine learning concepts including prediction and information retrieval through the use of case studies. The practical approach of the course will equip you to select appropriate machine learning models and algorithms to solve complex problems.
By the end of the course you can expect to be able to learn key machine learning algorithms, work with large datasets to create AI applications that improve and learn over time with the ability to make predictions using data.
There are 4 modules to complete, each requiring around 6 hours per week. The course can be completed in your own time to fit into your busy schedule.
If you do not understand mathematics, you are not going to be successful in the field of machine learning. This is one of few online courses that covers the mathematical foundations of machine learning. Even with a high school or university level understanding of mathematics, it can be hard to apply these to data science in the context of machine learning algorithms and models. These learning courses bridge the gap between mathematical concepts and their application in computer science and machine learning. Each of the learning courses included span up to 6 weeks, so you will take a minimum of 18 weeks to complete this course in total. You will need to be able to set aside 12 hours of study time per week to get the most out of the course.
The first course covers linear algebra and how it links to data science for building machine learning models. You will learn how to write code blocks in Python, with an exercise on Google’s page ranking algorithm to cap it off.
The second course covers multivariate calculus for data fitting. The final course is Dimensionality Reduction to teach you how to compress data. You will need mid-level skills in Python programming.
The course lays a great foundation for further studies by teaching you the mathematical concepts that are often prerequisites for advanced courses. Assignments are graded and peer reviewed for valuable insights to help you refine and improve your skills.
This is one of the best online courses for students wanting to learn how to apply deep learning to trading stocks, image recognition and business analytics to name just a few areas of application. Developed by 2 data scientists, the Deep Learning A-Z course covers everything you’ll need to know to master deep learning and artificial neural networks applications in the real world.
To properly grasp the content covered in this course, you will need to have completed higher grade mathematics and have basic Python programming knowledge. The course covers the 2 branches of deep learning, namely supervised learning and unsupervised learning. You will complete coding assignments and 6 machine learning projects to apply what you have learned to real world problems by creating machine learning algorithms that solve said problems.
Some bonus features of this course are excellent support, with replies from tutors received in under 48 hours. The course is packaged into over 20 hours of on-demand video lectures for flexible and interactive learning. Unlike other similar learning courses, the A-Z course has you work on 6 real world case studies so you will be able to develop algorithms for problems related to stock price recognition, image recognition, suggestions systems (like Netflix suggested content) and fraud detection. This sets you up to be able to solve problems for a variety of industries right from the start.
Developed by professionals at IBM and presented on Coursera, the Machine Learning with Python certification will highlight the many ways machine learning is used in a variety of industries. The course is broken down into two areas. The first area looks at machine learning concepts and their applications in the real world. The second area dives into machine learning models, machine learning techniques and machine learning tools. You will learn about machine learning algorithms, supervised learning and unsupervised learning with a project at the end of the course submitted for assessment to demonstrate your understanding of the course content.
You will work on building machine learning systems for real world applications like cancer detection, recommendation systems, economic trend projections, customer behavior predictions and many other exciting areas. The course will take on average 5 to 6 weeks to complete with around 6 hours per week needed to successfully complete the course.
When you have completed the course you can choose to continue your studies by completing the IBM Artificial Intelligence Professional Certificate and IBM Data Science Professional Certificate.
The Google AI Education platform offers free learning courses for students aspiring to pursue careers in artificial intelligence and machine learning. The interactive learning platform presents courses in video and article formats for an immersive learning experience. The crash course lives up to its name… you will learn how to solve common machine learning problems quickly using Python and Tensorflow to build machine learning models.
Some learning courses you can expect to cover include linear regression, classification, training sets, neural networks and machine learning engineering. If you’ve been playing around with machine learning for a while but want to learn more and develop your skills, this is one of the best online courses to do that. One thing to be aware of is that you do not receive a certificate at the end of the course, so if this is something important to you for your resume, perhaps one of the other courses listed would be better suited for you.
Are you a machine learning novice? This is one of the best online courses for beginners and will empower you with both a theoretical and practical understanding of machine learning models, techniques and tools. By the end of this course you will be equipped with the skills and knowledge to build machine learning models for business applications such as automations, fraud detection and more.
Despite being aimed at beginners, you will need basic coding knowledge and high school level mathematics to enroll in this course. With over 24 hours of lectures packed into this exciting online course, you can expect to set aside 8 hours a week for a period of 12 weeks in order to complete this course.
If you’ve been searching through online courses that will help you get the basics right, then this course, available on Udemy, is a great option for you. It will lay the foundations for you to develop the core skills and knowledge required to succeed in this exciting field and embark on a career path as a machine learning engineer.
The course will break down the differences between classical programming, machine learning and deep learning. You will also touch on complex topics like neural networks, validation, testing and regularization. This course will also teach you which machine learning tools to use to develop machine learning algorithms for the real world.
The course consists of over 17 hours of on-demand videos, articles, downloadable resources and a certificate of completion. You are able to pace yourself and fit learning into your own schedule.
As far as learning courses are concerned, nanodegrees are growing in popularity particularly in the tech sphere. This online machine learning course will teach you all about machine learning algorithms, supervised learning, data cleaning, unsupervised learning and deep learning.
Each learning area ends off with a practical assignment to help you reinforce your newfound knowledge and put your skills to the test. The course will teach machine learning models, designing neural networks, unsupervised learning techniques and much more. The projects are based on real life scenarios, equipping you to step into roles with top level businesses upon completion.
The course takes on average 3 months to complete when dedicating 10 hours a week to your studies. You have access to tutors and mentors for support throughout your learning journey.
This unconventional but groundbreaking course aims to show prospective machine learning engineers that machine learning can be applied to just about any industry. You will learn how to apply machine learning to the arts by using it to analyze human movement and music as its data source.
While the course certainly covers the fundamentals of machine learning, it does so with artistic flair. The course consists of 7 modules, each taking around 8 hours to complete. If you offer to audit the course you can access the material for free but not the exercises. For access to exercises and course content you will need to pay for a subscription. Assignments are peer-reviewed giving you the opportunity to gain meaningful critique of your work to improve your skills.