Linear Algebra: Vectors, Matrices, and Operations Probability & Statistics: Bayes’ Theorem, Distributions, Expectation Calculus: Derivatives, Partial Derivatives, and Optimization Gradient Descent and its Variants (SGD, Adam)

Supervised Learning: Concept and Workflow Unsupervised Learning: Clustering and Dimensionality Reduction Reinforcement Learning Basics Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)

Regression: Linear Regression, Polynomial Regression, Ridge & Lasso Regression Classification: Logistic Regression, Decision Trees, Random Forests Support Vector Machines (SVM) and Naïve Bayes Hyperparameter Tuning and Model Optimization

Introduction to Time Series Data Moving Averages and Exponential Smoothing ARIMA, SARIMA, and LSTM for Time Series Forecasting Anomaly Detection in Time Series Data

Understanding AI Model Development Lifecycle Pretrained Models vs. Custom Models Model Training, Validation, and Deployment Model Interpretability and Explainability (SHAP, LIME)

Feature Engineering & Selection Bias-Variance Tradeoff Ensemble Methods (Bagging, Boosting, XGBoost) Model Deployment (Flask, FastAPI, Docker)

Introduction to Hadoop & Spark AWS/GCP for Data Science (S3, BigQuery, Redshift) Distributed Computing for Large Datasets Stream Processing (Kafka, Spark Streaming)