Linear Algebra Basics: Vectors, Matrices, Dot Product, Probability and Statistics: Basic Distributions and Bayes’ Concept, Optimization Basics: Gradient Descent and Cost Functions, Data Handling using Python (NumPy, Pandas), Data Visualization , Overview of Data Analytics Process

Data Collection and Cleaning, Basic Feature Engineering and Encoding, Train-Test Split and Cross Validation, Bias-Variance Concept, Regression: Linear and Logistic, Classification Models: KNN, Naive Bayes, Practice: Predictive Model on Dataset

Decision Trees and Random Forest Basics, Support Vector Machines (SVM) Overview, Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC, Overfitting and Regularization Basics, Basic Hyperparameter Tuning, Model Explainability (intro to SHAP/LIME), Overview of MLOps

Neural Networks Basics: Perceptron and Activation Functions,Backpropagation Concept, Deep Learning Basics,Computer Vision Basics: CNN and Image Classification,Natural Language Processing Basics: Text preprocessing (tokenization, stemming, embeddings),Sentiment Analysis,,Basics of Transformers and LLMs

Generative AI Basics (LLMs, Diffusion overview),Prompt Writing Techniques,Tools Overview: APIs, Hugging Face, LangChain, Agent-based Systems: Decision-making and Task Flow,Building AI Agents, Real-world Examples: Chatbots, Smart Systems, Predictive Applications