Module 1: Introduction to Python & Machine Learning

• Introduction to Python
• Features and applications of Python
• Introduction to Machine Learning
• AI vs Machine Learning vs Deep Learning
• Applications of Machine Learning
• Role of Python in ML
• Python environment and IDE setup

Module 2: Python Programming Fundamentals

• Variables and data types
• Input and output
• Type conversion
• Operators
• Comments
• Basic Python programs
• Working with Python libraries

Module 3: Conditional Statements & Loops

• if, elif, else
• Nested conditions
• for and while loops
• break, continue, pass
• Practical programming exercises

Module 4: Python Data Structures

• Strings
• Lists
• Tuples
• Sets
• Dictionaries
• Data structure methods
• List and dictionary comprehension
• Practical data manipulation

Module 5: Functions & Modules

• Creating functions
• Parameters and arguments
• Return statements
• Lambda functions
• Scope of variables
• Modules and packages
• Installing packages using pip

Module 6: File Handling & Exception Handling

• Reading and writing text files
• CSV files
• JSON files
• Exception handling
• try, except, else, finally
• Debugging Python programs

Module 7: Object-Oriented Programming with Python

• Classes and objects
• Constructors
• Attributes and methods
• Inheritance
• Polymorphism
• Encapsulation
• Abstraction

Module 8: NumPy for Machine Learning

• Introduction to NumPy
• Arrays
• Array dimensions
• Indexing and slicing
• Array operations
• Mathematical functions
• Statistical operations
• Multidimensional arrays

Module 9: Pandas for Data Processing

• Introduction to Pandas
• Series and DataFrame
• Creating and importing datasets
• Reading CSV and Excel files
• Selecting and filtering data
• Sorting data
• Grouping data
• Handling missing values
• Data cleaning

Module 10: Data Visualization

• Introduction to data visualization
• Matplotlib
• Line charts
• Bar charts
• Pie charts
• Histograms
• Scatter plots
• Introduction to Seaborn
• Visualizing datasets

Module 11: Statistics for Machine Learning

• Mean, median and mode
• Range and variance
• Standard deviation
• Correlation
• Probability basics
• Normal distribution
• Statistical analysis of datasets

Module 12: Machine Learning Fundamentals

• What is Machine Learning?
• Machine Learning workflow
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Semi-supervised learning overview
• Features and labels
• Training and testing data
• Model training and prediction

Module 13: Data Preprocessing

• Understanding datasets
• Data cleaning
• Handling missing values
• Removing duplicate data
• Encoding categorical data
• Feature scaling
• Normalization
• Standardization
• Train-test split

Module 14: Supervised Learning

• Introduction to supervised learning
• Regression vs Classification
• Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)

Module 15: Unsupervised Learning

• Introduction to unsupervised learning
• Clustering
• K-Means Clustering
• Hierarchical Clustering
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• Practical clustering exercises

Module 16: Model Evaluation & Optimization

• Model performance evaluation
• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix
• Mean Absolute Error
• Mean Squared Error
• Root Mean Squared Error
• Cross-validation
• Hyperparameter tuning

Module 17: Feature Engineering

• Understanding features
• Feature selection
• Feature extraction
• Feature transformation
• Creating new features
• Handling categorical features
• Improving model performance

Module 18: Machine Learning with Scikit-learn

• Introduction to Scikit-learn
• Loading datasets
• Building ML models
• Training models
• Making predictions
• Evaluating models
• Saving and loading trained models
• Building complete ML pipelines

Module 19: Practical Machine Learning Applications

Students will practice projects such as:

• 🏠 House Price Prediction
• 🎓 Student Performance Prediction
• 💳 Customer Classification
• 📧 Spam Email Classification
• 🛒 Customer Segmentation
• 🌸 Flower Classification
• 🚗 Car Price Prediction
• 📊 Sales Prediction
• ❤️ Customer Churn Prediction
• 📈 Stock/Time-Series Prediction basics

Module 20: Final Machine Learning Projects

Students will develop complete projects involving:

• Data collection
• Data preprocessing
• Exploratory Data Analysis
• Feature engineering
• Model selection
• Model training
• Model evaluation
• Prediction
• Result visualization
• Project documentation