What will you learn in Unsupervised Learning, Recommenders, Reinforcement Learning Course
Apply clustering algorithms and dimensionality reduction techniques in machine learning.
Understand and build recommender systems using collaborative filtering and matrix factorization.
Grasp the fundamentals of reinforcement learning, including Markov Decision Processes and Q-learning.
Learn how unsupervised learning enhances real-world applications like search engines and video recommendations.
Program Overview
Module 1: Clustering & k-means
⏱️ 1 week
Topics: k-means clustering, elbow method, choosing the number of clusters.
Hands-on: Implement clustering on image data and customer segments.
Module 2: PCA (Principal Component Analysis)
⏱️ 1 week
Topics: Dimensionality reduction, variance explained, PCA implementation.
Hands-on: Use PCA to compress and visualize high-dimensional data.
Module 3: Recommender Systems
⏱️ 1 week
Topics: Content-based filtering, collaborative filtering, low-rank matrix factorization.
Hands-on: Build a movie recommender system using real datasets.
Module 4: Reinforcement Learning
⏱️ 1 week
Topics: Markov Decision Processes, Bellman equations, Q-learning.
Hands-on: Apply Q-learning to game-like environments and decision-making scenarios.
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Job Outlook
Strong demand for ML engineers with skills in unsupervised learning and recommender systems.
Key applications include retail, healthcare, online platforms, and robotics.
Reinforcement learning is gaining traction in AI research and autonomous systems.
Average salary range for ML roles: $110,000–$160,000 annually.
Specification: Unsupervised Learning, Recommenders, Reinforcement Learning
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