Machine learning is the science of getting computers to act without being explicitly programmed. In this course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Why do we recommend this course?
Great, comprehensive, high quality course on all important topics in the field of machine learning, and a differentiated presentation. Exams can be completed in this course. Wrong statements in the exam lead to a reference to corresponding course material.
Which topics are covered?
Get a broad introduction to machine learning, datamining, and statistical pattern recognition
Understand supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
Understand unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)
Know and learn from best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)
Learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas from numerous case studies and applications
What will I achieve?
By the end of the course, you‘ll be able to...
understand the basic principles of linear regression.
implement an artificial neural network for digit recognition.
understand machine learning algorithms and machine learning applications.
Which prerequisites do I need to fulfill?
Prerequisites: basic mathematical knowledge
Who is offering this course?
The creators of the learning opportunities are responsible for their content.