AIML And Full Stack Development - Shivam Bhardwaj

1. Data Science with Python

📌 Module 1: Introduction to Data Science

• What is Data Science?

• Real-world applications

• Tools & ecosystem overview

📌 Module 2: Python Fundamentals

• Variables, data types, loops, functions

• File handling

• Python libraries overview

📌 Module 3: Data Manipulation

• NumPy for numerical computing

• Pandas for data analysis

• Cleaning, preprocessing & formatting

📌 Module 4: Data Visualization

• Matplotlib & Seaborn

• Creating charts, plots, and dashboards

📌 Module 5: Statistics & Probability

• Mean, median, mode

• Correlation & covariance

• Hypothesis testing

📌 Module 6: Machine Learning Basics

• Supervised vs unsupervised learning

• Linear regression

• Classification models

• Model evaluation

📌 Module 7: Projects

• Sales prediction

• Customer segmentation

• Real-time data analysis with Python

2. Full Stack Development

📌 Module 1: Frontend Essentials

• HTML5

• CSS3

• JavaScript basics

• Responsive design

📌 Module 2: Frontend Frameworks

• React.js basics

• Components, props, state

• Routing & hooks

📌 Module 3: Backend Development

• Python Flask / Node.js

• REST API development

• Authentication & authorization

📌 Module 4: Databases

• SQL (MySQL/PostgreSQL)

• NoSQL (MongoDB)

• CRUD operations

📌 Module 5: Full Stack Integration

• Connecting frontend & backend

• API testing (Postman)

• Deployment process

📌 Module 6: Projects

• Full stack web app

• API-based application

• Portfolio website

3. Artificial Intelligence & Machine Learning

📌 Module 1: Introduction to AI & ML

• Difference between AI, ML, DL

• Use cases

📌 Module 2: Machine Learning Algorithms

• Regression, classification

• Decision trees, random forests

• Clustering algorithms

📌 Module 3: Feature Engineering

• Encoding, scaling, normalization

• Handling missing values

📌 Module 4: Neural Networks (Basics)

• Perceptron model

• Activation functions

• Training workflow

📌 Module 5: ML Deployment

• Model saving

• Web deployment basics

• Intro to MLOps

📌 Module 6: AI/ML Projects

• Image classification

• Sentiment analysis

• Prediction-based models