- Introduction to K-Nearest Neighbors (KNN)     - How KNN Works: The Core Algorithm     - Key Concept     - Advantages of KNN     - Disadvantages of KNN     - The Curse of Dimensionality     - Feature Scaling: Why it's Crucial for KNN     - Numerical Example     - KNN for Regression     - Hyperparameter Tuning: Finding the Optimal K     - Weighted KNN (An Extension)     - Use Cases of KNN     - K-Nearest Neighbors (KNN) Implementation

    - What is Naive Bayes?     - Why "Naive"? The Core Assumption     - Naive Bayes Algorithm     - Types of Naive Bayes Classifiers     - Naive Bayes Algorithm Advantages     - Naive Bayes Algorithm Disadvantages     - Numerical Example: Spam Detection

    - Introduction to Ensemble Learning     - Understanding Gradient Boosting Machines (GBM)     - Advantages of Gradient Boosting     - XGBoost (eXtreme Gradient Boosting)     - LightGBM (Light Gradient Boosting Machine)     - XGBoost vs. LightGBM: A Comparison

    - What is Clustering?     - Introduction to K-Means Clustering     - Key Concepts in K-Means     - The K-Means Algorithm     - Choosing the Right 'K'     - Numerical Example     - Advantages of K-Means     - Limitations of K-Means     - Real-World Applications     - The K-Means Clustering Algorithm using Scikit-Learn

    - What is Clustering?     - DBSCAN Core Concepts     - The DBSCAN Algorithm     - Numerical Example     - Advantages of DBSCAN     - Disadvantages of DBSCAN     - The DBSCAN Clustering Algorithm using Scikit-Learn

    - What is Clustering?     - Introduction to Hierarchical Clustering     - Types of Hierarchical Clustering     - Agglomerative Hierarchical Clustering Algorithm     - Key Concept: Linkage Criteria     - The Dendrogram     - Divisive Hierarchical Clustering  Algorithm     - Advantages of Hierarchical Clustering     - Disadvantages of Hierarchical Clustering     - Applications of Hierarchical Clustering     - Numerical Example     - Hierarchical Clustering Implementation

    - What is PCA?     - Why PCA?     - Core Concepts of PCA     - How PCA Algorithm works     - Numerical Example     - Advantages of PCA     - Limitations of PCA     - Applications of PCA     - Implementation of PCA using Scikit Learn

    - Introduction to Neural Networks     - The Perceptron: The First Artificial Neuron     - Multi-Layer Perceptrons (MLPs)     - Perceptrons vs. Multi-Layer Perceptrons (MLPs)

    - What are Activation Functions?     - Why Do We Need Them? (The Non-Linearity Imperative)     - Sigmoid Function(Logistic Function)     - Tanh Activation Function     - ReLU( Rectified Linear Units) Activation Function     - Leaky ReLU Activation Function     - Exponential Linear Units(ELU) Activation Function     - Softmax Activation Function

    - Forward Propagation     - Feed Forward Neural Network Structure     - Backward propagation     - Flow of Backward Pass

    - Introduction to Optimizers     - Gradient Descent (GD)     - Types of Gradient Descent     - Advantages of Gradient Descent     - Disadvantages and Challenges in Gradient Descent     - Momentum     - Nesterov Accelerated Gradient (NAG)     - Adaptive Learning Rate Methods     - Adagrad (Adaptive Gradient Algorithm)     - RMSprop (Root Mean Square Propagation)     - Adam (Adaptive Moment Estimation)     - Adadelta     - Learning Rate Schedules and Advanced Techniques     - Choosing an Optimizer

    - What are Convolutional Neural Networks (CNNs)?     - The Challenge of Traditional MLPs     - Why CNNs Excel for Image Data     - Core Building Blocks of a CNN     - CNN Architecture     - Training a CNN - The Learning Process     - Real-World Examples & Applications     - Advantages of CNNs     - Limitations of CNNs     - Advanced Concepts & Future Trends

    - Introduction     - Key Features of RNNs     - RNN Architecture     - How RNNs "Remember" - The Hidden State(Example)     - Applications of RNNs     - Challenges with Basic RNNs     - Overcoming Challenges - Gated RNNs     - LSTM vs. GRU     - Implementation of RNN Using Pytorch