10 More Must-See Free Courses for Machine Learning and Data Science
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
Our last collection of free machine learning and data science courses was well received, so why not compile another? Here are 10 more courses to help with your winter learning season. Courses range from introductory machine learning to deep learning to natural language processing and beyond.
This collection comes courtesy of Columbia University, Krakow Technical University, MIT, UC Berkeley, University of Washington, University of Wisconsin–Madison, and Yandex Data School.
If, after reading this list, you find yourself wanting more free quality, curated learning materials, check out the related posts further below.
1. Machine Learning
University of Washington
This course is designed to provide a thorough grounding in the fundamental methodologies and algorithms of machine learning. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics, and from optimization.
Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra, probability, statistics and algorithms.
2. Machine Learning
University of Wisconsin–Madison
This course will cover the key concepts of machine learning, including classification, regression analysis, clustering, and dimensionality reduction. Students will learn about the fundamental mathematical concepts underlying machine learning algorithms, but this course will equally focus on the practical use of machine learning algorithms using open source libraries from the Python programming ecosystem.
3. Algorithms (in Journalism)
This is a course on algorithmic data analysis in journalism, and also the journalistic analysis of algorithms used in society. The major topics are text processing, visualization of high dimensional data, regression, machine learning, algorithmic bias and accountability, monte carlo simulation, and election prediction.
All coding is done in Python, using Pandas, matplotlib, scikit learn.
4. Practical Deep Learning
Yandex Data School
5. Big Data in 30 Hours
Krakow Technical University
The goal of this technical, hands-on class is to introduce practical Data Engineering and Data Science to technical personnel (corporate, academic or students), during 15 lectures (2 hours each). All subjects are introduced by examples that students are expected to immediately play with using either command-line or GUI tools.
Prerequisites: the participants need to be technical, reasonably fluent in general programming and operating systems, with basic exposure to Linux shell, databases, and SQL. Working knowledge of Python will be needed for the lectures 9-15.
Note that this course is underdevelopment, and not all lessons have been completed.
6. Deep Reinforcement Learning Bootcamp
UC Berkeley (& others)
Reinforcement learning considers the problem of learning to act and is poised to power next generation AI systems, which will need to go beyond input-output pattern recognition (as has sufficed for speech, vision, machine translation) but will have to generate intelligent behavior. Example application domains include robotics, marketing, dialogue, HVAC, optimizing healthcare and supply chains.
This two-day long bootcamp will teach you the foundations of Deep RL through a mixture of lectures and hands-on lab sessions, so you can go on and build new fascinating applications using these techniques and maybe even push the algorithmic frontier.
7. Introduction to Artificial Intelligence
University of Washington
8. Brains, Minds and Machines Summer Course
This course explores the problem of intelligence—its nature, how it is produced by the brain and how it could be replicated in machines—using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. Materials are drawn from the Brains, Minds and Machines Summer Course offered annually at the Marine Biological Laboratory
9. Design and Analysis of Algorithms
This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.
10. Natural Language Processing
University of Washington
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