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