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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FAQs
- Requires familiarity with supervised learning concepts.
- Basic knowledge of Python and linear algebra recommended.
- Ideal for students or professionals in data science and AI.
- Focuses on practical implementation of ML algorithms.
- Helps learners transition to advanced unsupervised and reinforcement learning.
- Covers k-means clustering, elbow method, and cluster selection.
- Introduces Principal Component Analysis (PCA) for dimensionality reduction.
- Hands-on exercises with image data and customer segmentation.
- Teaches visualization of high-dimensional datasets.
- Applies unsupervised methods to real-world scenarios.
- Explains content-based and collaborative filtering.
- Covers low-rank matrix factorization methods.
- Hands-on project: building a movie recommender system.
- Teaches evaluation metrics for recommendation quality.
- Prepares learners for practical applications in retail, streaming, and e-commerce.
- Covers Markov Decision Processes (MDPs) and Bellman equations.
- Teaches Q-learning for decision-making scenarios.
- Hands-on exercises in game-like environments.
- Prepares learners for applying RL in robotics, AI research, and autonomous systems.
- Builds foundational knowledge for advanced RL studies.
- 4 modules: Clustering, PCA, Recommender Systems, Reinforcement Learning.
- Each module designed to take ~1 week.
- Self-paced with lifetime access.
- Certificate awarded upon completion.
- Total duration: ~4 weeks for focused learning.
