Module 1: Python Full Foundation
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- Introduction to Python & Environment Setup (Anaconda, Jupyter)
- Python Syntax, Variables, Data Types & Type Casting
- Control Structures (If, Loop, While, For)
- Functions, Recursion & Lambda Expressions
- String, List, Tuple, Set, Dictionary Operations
- File Handling & Exception Handling
- Object-Oriented Programming (Classes, Inheritance, Polymorphism)
- Modules, Packages & Virtual Environments
- Python DateTime, Math, and OS Libraries
- Comprehensions, Generators & Iterators
- Working with JSON, CSV & Excel Data
- Introduction to Regular Expressions (Regex)
- Working with APIs using Requests Library
- Mini Project: Python Automation or Data Extraction
Module 2: Data Science Libraries
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- Introduction to Data Science & Workflow
- NumPy — Arrays, Broadcasting, Mathematical Operations
- Pandas — Series, DataFrame, Data Cleaning & Transformation
- Matplotlib — Data Visualization Basics
- Seaborn — Advanced Visual Analytics
- Exploratory Data Analysis (EDA) in Depth
- Working with Real-World Datasets (Kaggle/CSV)
- Feature Engineering & Data Preprocessing
- Handling Missing Values & Outliers
- Encoding Categorical Data
- Data Normalization & Scaling
- Statistics for Data Science — Mean, Variance, Correlation
- Probability & Hypothesis Testing
- Mini Project: Data Analysis & Visualization Dashboard
Module 3: Machine Learning (Supervised, Unsupervised, Reinforcement)
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- Introduction to Machine Learning & ML Pipeline
- Supervised Learning Concepts (Regression, Classification)
- Linear & Multiple Regression Models
- Logistic Regression, KNN, Decision Tree, Random Forest
- Naive Bayes, SVM, Gradient Boosting, XGBoost
- Unsupervised Learning — K-Means, Hierarchical Clustering
- Dimensionality Reduction (PCA, t-SNE)
- Reinforcement Learning Basics (Agent, Environment, Reward)
- Q-Learning and Deep Q-Learning Overview
- Model Evaluation — Confusion Matrix, ROC, Precision, Recall
- Cross Validation, Grid Search, Hyperparameter Tuning
- Bias-Variance Tradeoff & Overfitting Solutions
- Real ML Projects: Price Prediction, Sentiment Analysis
Module 4: Scikit-Learn (SKLEARN) & Model Building
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- Introduction to Scikit-learn
- Dataset Loading & Splitting (train_test_split)
- Pipeline Creation & Model Selection
- Feature Scaling, Encoding, and Transformation
- Model Evaluation Metrics (R², Accuracy, MAE, MSE)
- GridSearchCV & RandomizedSearchCV
- Model Persistence using Joblib & Pickle
- Custom Transformers & Pipelines
- Model Interpretability (SHAP, LIME)
- Time Series Forecasting in Scikit-learn
- Handling Imbalanced Data (SMOTE, Class Weights)
- Model Deployment Basics
- Project: End-to-End ML Pipeline using Scikit-Learn
Module 5: Deep Learning with Keras & PyTorch
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- Introduction to Deep Learning Concepts
- Understanding Neural Networks, Perceptron, Activation Functions
- Feedforward & Backpropagation
- Keras Sequential & Functional API
- Building ANN Models for Regression & Classification
- TensorFlow Basics & GPU Setup
- PyTorch Tensors, Datasets & DataLoaders
- Implementing CNN for Image Recognition
- RNN & LSTM for Sequential Data
- Transfer Learning using Pre-trained Models
- Model Evaluation & Visualization (TensorBoard)
- Optimization (Adam, SGD, Learning Rate Schedulers)
- Mini Project: Image Classification using CNN
Module 6: Artificial Intelligence, LLMs & NLP
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- AI Overview — Intelligent Agents, Search Algorithms, Optimization
- Natural Language Processing (NLP) Fundamentals
- Text Preprocessing, Tokenization, Stopwords, Lemmatization
- Bag of Words, TF-IDF, Word2Vec, GloVe
- Sentiment Analysis, Topic Modeling
- Introduction to Transformers (BERT, GPT, T5)
- Large Language Models (LLMs) — Overview & Use Cases
- Fine-tuning Pre-trained LLMs
- AI Agents & Prompt Engineering
- Speech Recognition & Text-to-Speech
- Generative AI — Image & Text Generation
- Mini Project: Chatbot using Transformer API
- Ethics & Future of AI in Industry
Module 7: Model Deployment, MLOps & Final Projects
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- Flask / FastAPI for ML Model Deployment
- Streamlit & Gradio for Interactive Dashboards
- Model Deployment on AWS / Azure / GCP
- Dockerization of ML Models
- Version Control using Git & GitHub
- CI/CD for ML Pipelines
- Monitoring Models in Production
- Real-Time Prediction APIs
- End-to-End Capstone Project (Data to Deployment)
- Resume Building & Interview Preparation
- Career Support & Placement Assistance
