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Week - 1 |
Introduction to Machine Learning: Basic Concepts and Learning Paradigms |
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Week - 2 |
Hypothesis Evaluation, Performance Metrics and Cross-Validation |
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Week - 3 |
Loss Functions and Optimization Fundamentals |
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Week - 4 |
Supervised Learning 1: Decision Trees and Rule-Based Systems |
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Week - 5 |
Supervised Learning 2: Bayesian Learning and Probabilistic Models |
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Week - 6 |
Supervised Learning 3: Support Vector Machines |
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Week - 7 |
Unsupervised Learning |
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Week - 8 |
Unsupervised Learning 1: K-Means Algorithm and Clustering |
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Week - 9 |
Unsupervised Learning 2: Dimensionality Reduction and Principal Component Analysis |
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Week - 10 |
Modern Learning Approaches 1: Reinforcement Learning Algorithms and Introduction to Artificial Neural Networks |
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Week - 11 |
Modern Learning Approaches 2: Deep Learning Architectures |
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Week - 12 |
Modeling with Real-World Datasets 1: Data Preparation and Model Training |
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Week - 13 |
Modeling with Real-World Datasets 2: Analysis and Interpretation of Results |
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Week - 14 |
Machine Learning Project Presentations |