API Data Ingestion: REST APIs and JSON Parsing Database Connections: SQL with Pandas and SQLAlchemy File Formats for Data: CSV, Parquet, JSON, Excel, and HDF5 NumPy Fundamentals: Arrays, Data Types, and Vectorization Pandas Basics: DataFrames and Data Loading Streaming Data: Chunking, Iterators, and Large Dataset Handling

Datetime Feature Engineering: Extraction, Encoding, and Cyclical Features Encoding Categorical Variables: Label, One-Hot, Target, and Ordinal Encoding Feature Engineering: Polynomials, Interactions, Binning, and Domain Features Feature Scaling: Standardization, Normalization, and Robust Scaling Imbalanced Data: SMOTE, ADASYN, and Class Weights Imputation Strategies: From Simple to Advanced Techniques Missing Values: Detection, Patterns, and Handling Strategies Outlier Handling: Detection Methods and Treatment Strategies Text Preprocessing: Cleaning, Tokenization, and Normalization

Categorical Data Analysis: Analyzing Discrete Variables Correlation Analysis: Measuring Relationships Between Variables Descriptive Statistics: Measures of Central Tendency and Dispersion Distribution Analysis: Understanding Data Shape and Patterns Distribution Plots: Visualizing Data Shape and Spread Hypothesis Testing: Making Data-Driven Decisions Relationship Plots: Visualizing Associations Between Variables Visualization Guide: Choosing the Right Plot for Your Data

Clustering: K-means, Hierarchical, and DBSCAN Comparison Decision Trees: Splitting Criteria, Pruning, and Interpretability Hyperparameter Tuning: Grid Search, Random Search, and Bayesian Optimization K-Nearest Neighbors: Distance Metrics, Choosing K, and Curse of Dimensionality Linear Regression: Theory, Assumptions, and Diagnostics Logistic Regression: Classification, Sigmoid, and Odds Ratios Machine Learning Overview: From Supervised to Unsupervised Learning Model Evaluation: Metrics, Cross-Validation, and ROC-AUC Naive Bayes: Bayes Theorem, Conditional Independence, and Text Classification Principal Component Analysis: Dimensionality Reduction and Variance Explained Random Forest: Ensembling, Bagging, and Feature Importance Support Vector Machines: Margin Maximization and Kernel Methods XGBoost: Gradient Boosting, Parameters, and When to Use

Backpropagation: The Chain Rule in Neural Networks CNN Design Patterns: From VGG to ResNet Chain-of-Thought: Reasoning in Large Language Models Convolutional Neural Networks: Architecture and Components HiRoPE: Hierarchical Rotary Position Embedding Neural Networks: From Perceptron to Deep Networks RMSNorm: Root Mean Square Layer Normalization RoPE: Rotary Position Embedding Sequential Modeling: RNN, LSTM, and GRU SwiGLU: Swish-Gated Linear Unit Training Stability in Deep Neural Networks Transformer Architecture: Attention Is All You Need

AdaGrad, RMSprop, and Adam Gradient Descent Learning Rate Scheduling Momentum Methods Newton's Method Optimizer Selection Guide Quasi-Newton Methods (L-BFGS) Regularization Techniques Stochastic Gradient Descent Vanishing and Exploding Gradients

AutoML with PyCaret Experiment Tracking with Weights & Biases Hyperparameter Optimization with Optuna Model Deployment Basics Model Versioning with MLflow and DVC

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