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Section 2.2: Random Forests

Section 2.2: Random Forests
Advanced Machine Learning
Module 2: Ensemble Methods
Section 2.2: Random Forests

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Advanced Machine Learning

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Instructor

Akshay Kumar

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Module 1: Introduction to Advanced Machine Learning

Section 1.1: Overview of Advanced Machine Learning Techniques

Section 1.2: Key Concepts and Terminologies

Section 1.3: Applications and Use Cases

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Module 2: Ensemble Methods

Section 2.1: Bagging and Boosting

Section 2.2: Random Forests

Section 2.3: Gradient Boosting Machines (GBM)

Section 2.4: XGBoost and LightGBM

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Module 3: Deep Reinforcement Learning

Section 3.1: Fundamentals of Reinforcement Learning

Section 3.2: Deep Q-Learning

Section 3.3: Policy Gradients

Section 3.4: Proximal Policy Optimization (PPO)

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Module 4: Adversarial Training

Section 4.1: Understanding Adversarial Examples

Section 4.2: Adversarial Attack Techniques

Section 4.3: Defensive Techniques Against Adversarial Attacks

Section 4.4: Robust Model Training

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Module 5: Transfer Learning

Section 5.1: Concept and Importance of Transfer Learning

Section 5.2: Pre-trained Models and Fine-Tuning

Section 5.3: Domain Adaptation

Section 5.4: Case Studies and Applications

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Module 6: Practical Implementation

Section 6.1: Advanced Python Programming Techniques for Machine Learning

Section 6.2: Using TensorFlow for Advanced Model Building

Section 6.3: Implementing Deep Learning Models with PyTorch

Section 6.4: Integrating Models into Real-world Applications

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Assessment

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