What Will You Learn?
The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful
About This Course
Provider: Edx
Format: Online
Duration: 32 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: Upon completion, you will be able to implement popular machine learning algorithms, apply principal component analysis, and incorporate regularization techniques to construct a proficient movie recommendation system.
Course Prerequisites: Fundamental programming skills, Statistical concepts such as probability, inference, and modeling
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: Harvard University
Key Topics: Machine Learning Algorithms, Algorithms, Data Science, Recommender Systems, Machine Learning, Forecasting, Speech Recognition, Principal Component Analysis
Topic Covered:
- - Introduction and Welcome
- - Introduction to Machine Learning
- - Machine Learning Basics
- - Smoothing and Linear Regression for Prediction
- - Cross-validation and k-Nearest Neighbors
- - The Caret Package
- - Model Fitting and Recommendation Systems
- - Final Assessment and Course Wrap-Up
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