11 best machine learning courses
Machine learning is a fast-growing profession, and these courses can help you get ahead
The demand for engineers with machine learning knowledge and experience snowballs as organizations want to deploy and make ML a pivotal feature in their products. The worldwide ML market is projected to grow from $7.3 billion in 2020 to $30.6 billion in 2024, according to analyst firm Market Research Future.
Around 10% of large companies use at least 10 AI applications, such as chatbots, process optimization, and fraud analysis, according to research by MMC Ventures. No wonder a machine learning engineer can earn an average salary of $120,591, according to Salary.com.
What is machine learning?
Before we find out the best machine learning courses, we should know what machine learning is.
Machine learning is a part of AI and computer science that helps computers learn and act from experience without explicit human programming to begin with.
The machine learning process starts with collecting data then cleaning, preparing, and manipulating it. A machine learning model is selected and then trained. Data is tested against the model, and the model should improve over time, becoming better at what it does.
Some machine learning applications include self-driving cars, image recognition, and speech recognition, to name a few.
If you want to get into machine learning, here are several machine learning courses to consider.
11 best machine learning courses
There are numerous machine learning courses available online. Here are 11 of our favorites.
1. Machine Learning for All – University of London via Coursera
Duration of study: 22 hours
This course introduces learners to machine learning with no programming knowledge. It looks at machine learning basics and offers a hands-on approach. Students use user-friendly tools developed at Goldsmiths, the University of London to do a machine learning project, such as training a computer to recognize images.
2. Introduction to Machine Learning for Coders
Duration of study: 24 hours
In this course, students learn the most important machine learning models, including how to create them from scratch and key skills in data preparation, model validation, and building data products. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program.
Students who wish to attend this course should have at least one year of coding experience and high school math.
3. Machine Learning A-Z: Hands-On Python & R In Data Science
Duration of study: 42 hours
If you want to learn about machine learning algorithms and create them in Python and R, this course is for you.
This hands-on course includes several examples of code to practice on. This course helps students build an army of powerful machine learning models and know-how to combine them to solve any problem.
It also teaches students to know which machine learning model to choose for each type of problem and handle specific topics like reinforcement learning, NLP, and deep learning.
4. Machine Learning with caret in R
Duration of study: 4 hours
Students in this course will learn how to build and evaluate predictive models, tune them for optimal performance, pre-process data for better results, and much more. The course also uses the Caret R package, which provides a consistent interface to all of R's machine learning facilities.
5. Understanding Machine Learning
Duration of study: 43 minutes
This short course gives students a clear introduction to machine learning. It explores the open source programming language R and allows students to train, test, and use a model.
6. Machine Learning with Python: A Practical Introduction
Duration of study: Five weeks of 4-6 hours of study each week
This is an IBM course where students can earn a skill badge digital credential. It teaches the difference between the two main types of machine learning methods: supervised and unsupervised.
Students will also learn about supervised and unsupervised learning algorithms, including classification and regression for supervised learning algorithms, and clustering and dimensionality reduction for unsupervised learning algorithms.
Parts of the course deal with how statistical modeling relates to machine learning and how to compare them. There are also real-life examples of the different ways machine learning affects society.
7. Machine Learning Specialization
Duration of study: 7 Months of 3 hours of study per week
This course runs through a series of practical case studies to help students gain applied experience in major areas of machine learning, including prediction, classification, clustering, and information retrieval.
Students can also learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
The applied learning project will see students implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization.
8. Learning Track: Machine Learning & Deep Learning in Financial Markets
Duration of study: 39 hours
For those interested in machine learning and its applications in trading, this is the course for you. It covers simple logistic regression models to complex LSTM models.
Students will learn to tune hyperparameters, gradient boosting, ensemble methods, advanced techniques to make robust predictive models. The course requires prior programming experience to fully understand the implementation of machine learning algorithms taught.
9. Machine Learning and AI: Support Vector Machines in Python
Duration: 8 hours 53 minutes
If you want to learn how to apply support vector machines (SVMs) to practical applications, such as image recognition, spam detection, medical diagnosis, and regression analysis, this is the course you want to sign up for.
SVMs are one of the most powerful machine learning models around, hence this course’s existence. The course takes a step-by-step approach to build up all the theories you need to understand how the SVM works.
10. Machine Learning – Columbia University via edX
Duration: 12 weeks of 8-10 hours of learning per week
This is a more advanced introduction to machine learning and covers supervised learning techniques for regression and classification and unsupervised learning techniques.
The first half of the course covers supervised learning techniques for regression and classification. The second half covers three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data.
11. Complete Machine Learning and Data Science: Zero to Mastery
Duration: 42 hours
This course introduces students to all of the modern skills of a Data Scientist. Students learn to build many real-world projects to add to their portfolios.
The course has access to all the code, workbooks, and templates (Jupyter Notebooks) on GitHub. It prides itself on having all the necessary resources in one place and teaching the latest trends and on-the-job skills employers seek.
Once students know the basics of machine learning and Python, they then look at advanced topics like Neural Networks, Deep Learning, and Transfer Learning.
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