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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

Course Curriculum

Module 01
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
Module 02
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
Certification 00:00:00

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