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Module 2: Ensemble Methods

Apprentice
Advanced Machine Learning
Module 2: Ensemble Methods

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Advanced Machine Learning

Module 2: Ensemble Methods

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Module 1: Introduction to Advanced Machine Learning

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Section 1.1: Overview of Advanced Machine Learning Techniques

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Section 1.2: Key Concepts and Terminologies

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Section 1.3: Applications and Use Cases

Advanced Machine Learning 10 QA MCQs

Module 2: Ensemble Methods

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Section 2.1: Bagging and Boosting

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Section 2.2: Random Forests

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Section 2.3: Gradient Boosting Machines (GBM)

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Section 2.4: XGBoost and LightGBM

Advanced Machine Learning 10 QA quiz

Module 3: Deep Reinforcement Learning

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Section 3.1: Fundamentals of Reinforcement Learning

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Section 3.2: Deep Q-Learning

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Section 3.3: Policy Gradients

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Section 3.4: Proximal Policy Optimization (PPO)

Advanced Machine Learning 10 QA MCQs

Module 4: Adversarial Training

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Section 4.1: Understanding Adversarial Examples

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Section 4.2: Adversarial Attack Techniques

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Section 4.3: Defensive Techniques Against Adversarial Attacks

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Section 4.4: Robust Model Training

Advanced Machine Learning 10 QA MCQs

Module 5: Transfer Learning

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Section 5.1: Concept and Importance of Transfer Learning

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Section 5.2: Pre-trained Models and Fine-Tuning

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Section 5.3: Domain Adaptation

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Section 5.4: Case Studies and Applications

Advanced Machine Learning 10 QA MCQs

Module 6: Practical Implementation

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Section 6.1: Advanced Python Programming Techniques for Machine Learning

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Section 6.2: Using TensorFlow for Advanced Model Building

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Section 6.3: Implementing Deep Learning Models with PyTorch

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Section 6.4: Integrating Models into Real-world Applications

Assessment

Advanced Machine Learning 50

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