C3 - Unsupervised Learning, Recommenders, Reinforcement Learning

In the third course of the Machine Learning Specialization, you will:

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

Week 1 - Unsupervised learning

This week, you will learn two key unsupervised learning algorithms: clustering and anomaly detection

Learning Objectives

• Implement the k-means clustering algorithm
• Implement the k-means optimization objective
• Initialize the k-means algorithm
• Choose the number of clusters for the k-means algorithm
• Implement an anomaly detection system
• Decide when to use supervised learning vs. anomaly detection
• Implement the centroid update function in k-means
• Implement the function that finds the closest centroids to each point in k-means

Week 2 - Recommender systems

Learning Objectives

• Implement collaborative filtering recommender systems in TensorFlow
• Implement deep learning content based filtering using a neural network in TensorFlow
• Understand ethical considerations in building recommender systems

Week 3 - Reinforcement learning

This week, you will learn about reinforcement learning, and build a deep Q-learning neural network in order to land a virtual lunar lander on Mars!

Learning Objectives

• Understand key terms such as return, state, action, and policy as it applies to reinforcement learning
• Understand the Bellman equations
• Understand the state-action value function
• Understand continuous state spaces
• Build a deep Q-learning network