"Linear Algebra: Vectors, Matrices, Dot Products, Probability: Random Variables, Distributions, Bayes' Theorem ,Statistics: Mean, Variance, Correlation, Covariance ,Optimization: Gradient Descent, Cost Functions ,Python for ML: NumPy, Matplotlib Basics

Data Types, Collection Methods, and Data Quality Issues ,Data Cleaning, Normalization, Handling Missing Values ,Feature Engineering and Encoding Techniques ,Train-Test Split, Cross-Validation, and Bias-Variance Tradeoff ,Linear and Logistic Regression: Theory + Hands-on

Decision Trees: Concepts and Implementation, Random Forests: Concepts and Implementation ,Support Vector Machines: Kernels and Applications ,KNN and Naive Bayes Classifiers ,Model Evaluation Metrics: Accuracy, Precision, Recall, F1, AUC-ROC

Clustering: K-Means,Hierarchical Clustering ,Dimensionality Reduction: PCA and Applications ,Introduction to Neural Networks: Perceptron, Activation Functions ,Training Deep Neural Networks and Backpropagation ,CNNs and RNNs Introduction: Image and Sequence Processing

AI in Healthcare, Agriculture, Finance, and Smart Cities, Real-World Case Studies: Disease Prediction, Yield Forecast, Traffic Analytics ,TensorFlow and Keras: Basic Model Building & Training