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25h09m of online video course

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Programming languages ( Python, Ruby, Java, C )

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Machine learning

It is a subfield of engineering and computer science that evolved from the study of pattern recognition and the theory of computational learning in artificial intelligence.

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Stanford
6 Courses / 8173 students

Located between San Francisco and San Jose in the heart of Silicon Valley, Stanford University is recognized as one of the worlds leading research and teaching institutions.

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Course content

1h08m

Lecture 1 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h16m

Lecture 2 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CCS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h13m

Lecture 3 | Machine Learning (Stanford)

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/BGwS/

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:

1h13m

Lecture 4 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h15m

Lecture 5 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h13m

Lecture 6 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h15m

Lecture 7 | Machine Learning (Stanford)

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/zJX/

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h17m

Lecture 8 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h14m

Lecture 9 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

1h12m

Lecture 10 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford