E.g., Saturday, February 13, 2016
E.g., Saturday, February 13, 2016
E.g., Saturday, February 13, 2016
Self Paced

A real Caltech course, not a watered-down version. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.

Average: 6.6 (13 votes)
Self Paced

This course introduces the field of artificial intelligence (AI). Materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.

Average: 8.3 (3 votes)
Mar 14th 2016

Aimed at a general business audience, this course demystifies artificial intelligence, provides an overview of a wide range of cognitive technologies, and offers a framework to help you understand their business implications. Some experts have called artificial intelligence "more important than anything since the industrial revolution."[i] That makes this course essential for professionals working in business, operations, strategy, IT, and other disciplines. Throughout the course, participants will build a knowledge base on cognitive technologies to equip them to engage in discussions with colleagues, customers, and suppliers and help them shape cognitive technology strategy in their organization.

Average: 8.5 (4 votes)
Self Paced Course - Start anytime

Can we program machines to learn like humans? This Reinforcement Learning course will teach you the algorithms for designing self-learning agents like us!

No votes yet
Self Paced Course - Start anytime

Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

No votes yet
Self Paced Course - Start anytime

This course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff.

No votes yet
Feb 15th 2016

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.

Average: 5.5 (4 votes)
Jan 25th 2016

Learn the principles of machine learning and the importance of algorithms.

No votes yet
Nov 10th 2015

The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

Average: 4 (1 vote)
Nov 15th 2015

Learn key concepts of data science and machine learning with examples on how to build a cloud data science solution with R, Python and Azure Machine Learning from the Cortana Analytics Suite.

No votes yet
Sep 8th 2015

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.

Average: 9.5 (2 votes)
Jun 29th 2015

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark.

Average: 10 (2 votes)
Jan 19th 2015

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Average: 8 (8 votes)
 

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