With this [course_title], you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, a set of data used to discover potentially predictive relationships and, also learn about over training and techniques to avoid it such as cross-validation. The course will guide you through wilderness of Machine Learning for Data Science.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: Open Culture
|Lecture 01 – The Learning Problem||01:21:00|
|Lecture 02 – Is Learning Feasible?||01:17:00|
|Lecture 03 -The Linear Model I||01:20:00|
|Lecture 04 – Error and Noise||01:18:00|
|Lecture 05 – Training Versus Testing||01:17:00|
|Lecture 06 – Theory of Generalization||01:18:00|
|Lecture 07 – The VC Dimension||01:14:00|
|Lecture 08 – Bias-Variance Tradeoff||01:17:00|
|Lecture 09 – The Linear Model II||01:27:00|
|Lecture 10 – Neural Networks||01:25:00|
|Lecture 11 – Overfitting||01:20:00|
|Lecture 12 – Regularization||01:15:00|
|Lecture 13 – Validation||01:26:00|
|Lecture 14 – Support Vector Machines||01:14:00|
|Lecture 15 – Kernel Methods||01:18:00|
|Lecture 16 – Radial Basis Functions||01:22:00|
|Lecture 17 – Three Learning Principles||01:16:00|
|Lecture 18 – Epilogue||01:10:00|
|Submit Your Assignment||00:00:00|
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