Full Stack AI – 40 Sessions Plan

Python Basics: Syntax, Control Structures, Functions

Data Structures in Python – List, Dict, Set, Tuple

NumPy for Numerical Computing

Pandas – Data Manipulation & Cleaning

Visualization using Matplotlib & Seaborn

Interactive Dashboards using Plotly & Tableau

Storytelling with Data – Generating Business Insights

SQL Basics – SELECT, JOIN, WHERE, GROUP BY

Window Functions, Subqueries, Python Integration

Descriptive & Inferential Statistics, Distributions

Hypothesis Testing, Confidence Intervals, A/B Testing

ML Workflow & Supervised Learning – Linear Regression

Logistic Regression, Decision Trees

Random Forest & Gradient Boosting (XGBoost)

Unsupervised Learning – K-Means, PCA

Model Evaluation – Confusion Matrix, RMSE, AUC

Feature Scaling, Encoding, Handling Outliers

Feature Selection, Missing Values Treatment

Hyperparameter Tuning – GridSearchCV, Cross Validation

Basics of Neural Networks – ANN, ReLU, Softmax

CNNs for Image Classification

RNNs & LSTM for Sequence Modeling

Model Optimization – Dropout, BatchNorm

Transfer Learning – MobileNet, ResNet

Text Preprocessing – Tokenization, Stopwords, TF-IDF

Word Embeddings – Word2Vec, GloVe

Text Classification, Transformers, LLMs Overview

Model Saving (Pickle, Joblib) & REST APIs with Flask

Streamlit Dashboard for AI Apps

Dockerizing Models, GitHub & Version Control

Deployment to Cloud – Heroku, AWS, GCP

Case Study 1: E-commerce Recommendation System

Case Study 2: Fraud Detection in Banking

Case Study 3: Retail Demand Forecasting / AI in Healthcare

Problem Statement + Data Collection & Cleaning

Modeling & Evaluation

Deployment, Presentation & Demo

Resume Building, GitHub Portfolio, Mock Interviews