Chapter 1

Ensemble Learning

Boosting vs Bagging philosophies

medium • 1 min read

AdaBoost mechanism

medium • 2 min read

Gradient Boosting Machines (GBM)

medium • 3 min read

XGBoost, LightGBM, and CatBoost (Advanced implementations)

medium • 4 min read

Stacking and Blending models

medium • 5 min read

Chapter 2

Time Series Forecasting

Components of Time Series (Trend, Seasonality, Irregularity)

medium • 1 min read

Stationarity testing (Augmented Dickey-Fuller test)

medium • 2 min read

Exponential Smoothing methods (Holt-Winters)

medium • 3 min read

ARIMA and SARIMA models

medium • 4 min read

Evaluating forecast accuracy (MAPE, MAD)

medium • 5 min read

Chapter 3

Deep Learning Fundamentals

Artificial Neural Networks (ANN) architecture

medium • 1 min read

Activation functions (ReLU, Sigmoid, Tanh)

medium • 2 min read

Forward propagation and Loss functions

medium • 3 min read

Backpropagation algorithm and chain rule

medium • 4 min read

Optimizers (Adam, RMSProp, SGD with momentum)

medium • 5 min read

Chapter 4

Specialized Neural Networks

Convolutional Neural Networks (CNN) for image data

medium • 1 min read

Pooling layers and data augmentation in CNNs

medium • 2 min read

Recurrent Neural Networks (RNN) sequence modeling

medium • 3 min read

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)

medium • 4 min read

Transfer learning using pre-trained network architectures

medium • 5 min read

Chapter 5

Model Deployment and MLOps

Hyperparameter tuning (Grid Search, Random Search, Bayesian opimization)

medium • 1 min read

Saving and loading models (Pickle, Joblib, ONNX)

medium • 2 min read

Creating REST APIs with Flask or FastAPI

medium • 3 min read

Containerization with Docker

medium • 4 min read

Model monitoring (Data drift and concept drift)

medium • 5 min read