Module 1: Python for Data Science

• Introduction to Python and its packages

• Data Structures (Lists, Tuples, Dictionaries, Sets)

• Functions, Loops & Conditional Statements

• File & Exception Handling

• Numpy, Pandas, Matplotlib, and Seaborn for Data Analysis and Visualization
  Hands-on: Basic Data Analysis and Visualization Project

Module 2: Statistics & MySQL for Machine Learning

• Descriptive & Inferential Statistics

• Sampling Techniques, Outlier Detection, and Normal Distribution

• Hypothesis Testing (T-Test, Z-Test, ANOVA, Chi-Square)

• Database Fundamentals: SQL vs NoSQL

• MySQL CRUD Operations, Joins, Keys, and Data Manipulation
  Hands-on: Data Cleaning and Query Optimization Exercises

Module 3: Machine Learning Fundamentals

• Introduction to ML: Types & Applications

• Data Preprocessing, Feature Engineering, and Encoding

• Model Evaluation & Validation (K-Fold, GridSearchCV)

• Regression Algorithms: Linear, Polynomial, Ridge, and Lasso

• Classification Algorithms: Logistic Regression, KNN, SVM
  Hands-on: Loan Eligibility Classification Project

Module 4: Advanced Machine Learning & AI Concepts

• Decision Trees, Random Forest, Gradient Boosting, and XGBoost

• Clustering (K-Means, DBSCAN)

• Principal Component Analysis (PCA)

• Recommendation Systems (Content-Based & Collaborative Filtering)
  Hands-on Projects:

• Water Quality Classification

• House Price Prediction

• Netflix Recommendation System

Module 5: Career Readiness & Industry Integration

• Resume & LinkedIn Optimization

• Mock Interviews with Industry Experts

• Career Assistance & Internship Placement Support

• IBM Global Certification
  Capstone: Real-World Project Showcase & Phase Test

Tools & Technologies Covered:

Python, Numpy, Pandas, Matplotlib, Seaborn, MySQL, Scikit-learn, XGBoost, IBM Tools