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[su_tab title = “Description”]

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Course Syllabus:

1. Linear Regression with One Variable

2. Linear Algebra Review

3. Linear Regression with Multiple Variables

4. Octave Tutorial

5. Logistic Regression

6. Regularization

7. Neural Networks: Representation

8. Neural Networks: Learning

9. Advice for Applying Machine Learning

10. Machine Learning System Design

11. Support Vector Machines

12. Unsupervised Learning

13. Dimensionality Reduction

14. Anomaly Detection

15. Recommender Systems

16. Large Scale Machine Learning

17. Application Example: Photo OCR

Duration:

10 weeks

Important Date:

Contact Institute

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[su_tab title = “Eligibility”]

• This course is at an undergraduate level, likely situated in second or third year.

Pre-requisites:

•  The only major prerequisite is differential calculus.

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[su_tab title =”Faculty”]

• Andrew Ng

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[su_tab title = “Contact”]

Name :
Email :
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