Core Fundamentals
Machine Learning Basics
Learning Paradigms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Semi-Supervised Learning
- Self-Supervised Learning
Model Training & Evaluation
- Model Training Fundamentals
- Training vs Validation vs Test Sets
- Cross-Validation
- Overfitting and Underfitting
- Regularization Techniques
- Bias-Variance Tradeoff
Feature Engineering
- Feature Engineering Principles
- Feature Selection Methods
- Feature Scaling and Normalization
- Dimensionality Reduction
- PCA (Principal Component Analysis)
Evaluation Metrics
Classical ML Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Ensemble Methods
Clustering
Deep Learning Architecture
Neural Network Fundamentals
- Neural Networks Basics
- Perceptron and Multi-Layer Perceptron
- Activation Functions
- Backpropagation
- Gradient Descent and Optimization
- Batch, Mini-Batch, and Stochastic Gradient Descent
Optimization Algorithms
Convolutional Neural Networks
- CNN Fundamentals
- Convolution Operations
- Pooling Layers
- CNN Architectures Overview
- ResNet Architecture
- VGG Architecture
- Inception Networks
Recurrent Neural Networks
- RNN Fundamentals
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- Vanishing and Exploding Gradients
- Bidirectional RNNs
Transformer Architecture
- Transformer Architecture Overview
- Self-Attention Mechanism
- Multi-Head Attention
- Positional Encoding
- Encoder-Decoder Architecture
- Vision Transformers (ViT)
Modern Architectures
- Attention Mechanisms in Deep Learning
- Residual Connections
- Batch Normalization
- Layer Normalization
- Dropout and Regularization
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