20 episodes

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.

Machine Learning Andrew Ng

    • Technology
    • 3.9 • 158 Ratings

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.

    • video
    1. Machine Learning Lecture 1

    1. Machine Learning Lecture 1

    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.

    • 4 sec
    • video
    2. Machine Learning Lecture 2

    2. Machine Learning Lecture 2

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.

    • 4 sec
    • video
    3. Machine Learning Lecture 3

    3. Machine Learning Lecture 3

    science, math, engineering, computer, technology, robotics, algebra, locally, weighted, logistic, regression, linear, probabilistic, interpretation, Gaussian, distribution, digression, perceptron

    • 4 sec
    • video
    4. Machine Learning Lecture 4

    4. Machine Learning Lecture 4

    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.

    • 4 sec
    • video
    5. Machine Learning Lecture 5

    5. Machine Learning Lecture 5

    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.

    • 4 sec
    • video
    6. Machine Learning Lecture 6

    6. Machine Learning Lecture 6

    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.

    • 4 sec

Customer Reviews

3.9 out of 5
158 Ratings

158 Ratings

Felixrpl ,

Inspiring

Awesome and very inspiring lectures to achieve things with the machine learning.

MarryC1 ,

awesome course

I took this course and it is the way to get started with fundementals of ML.

Улугбек ,

Thank you so much

Really super course. Thank you prof. Ng and Stanfor U.

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