Core Concepts

• Descriptive Statistics
• Probability Distributions
• Inferential Statistics
• Hypothesis Testing

Advanced Topics

• Linear Algebra for Data Science
• Bayesian Statistics
• Time Series Analysis
• Statistical Inference with Python

Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Model Evaluation Metrics

Unsupervised Learning

• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction (PCA)
• Association Rule Learning
• Anomaly Detection

Advanced ML

• Ensemble Methods
• Reinforcement Learning Basics
• Hyperparameter Tuning
• ML Deployment Strategies

Projects

• Project 1: Sales Forecasting
• Project 2: Customer Segmentation
• Project 3: Credit Risk Analysis
• Project 4: Sentiment Analysis
• Project 5: Predictive Maintenance

Main Project

Project Name: AI-Powered Recommendation System

This project involves building a recommendation system using collaborative filtering and content-based algorithms, deploying it using Flask or FastAPI, and integrating with a user-facing dashboard.

Data Science Tools

• Jupyter Notebooks
• Google Colab
• VS Code
• Tableau
• Power BI

Development and Deployment

• Git and GitHub
• Docker
• Flask and FastAPI
• AWS (S3, EC2)
• Heroku and Netlify