What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning Applications of Machine Learning Overview of ML Tools and Libraries (Scikit-Learn, TensorFlow, PyTorch)

Importance of Data Cleaning Handling Missing Data and Outliers Feature Scaling and Transformation Feature Selection Techniques

Linear Regression and Logistic Regression Decision Trees and Random Forests Support Vector Machines (SVM) Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score)

Bayes' Theorem Foundations & Probability Spaces The Class Conditional Independence Assumption Algorithmic Structural Variations: Gaussian, Multinomial, & Bernoulli Handling Zero-Frequency Anomalies via Laplace Smoothing

Clustering Techniques (K-Means, DBSCAN, Hierarchical Clustering) Principal Component Analysis (PCA) for Dimensionality Reduction Anomaly Detection Methods

Introduction to Neural Networks Activation Functions and Backpropagation Introduction to Deep Learning and Convolutional Neural Networks (CNNs) Hands-on Implementation using TensorFlow/Keras

Cross-Validation Techniques Grid Search vs. Random Search Regularization Techniques (L1, L2) Bias-Variance Tradeoff

ML in Healthcare, Finance, and E-commerce Natural Language Processing (NLP) Basics Image and Speech Recognition Ethical Considerations and Bias in ML