Course Contents

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Applications of Machine Learning
• Types of Machine Learning
• AI vs Machine Learning vs Deep Learning
• Machine Learning Workflow

Module 2: Python for Machine Learning

• Python Fundamentals
• Data Types and Structures
• Functions and Modules
• NumPy Basics
• Pandas for Data Analysis

Module 3: Data Collection and Preparation

• Data Gathering Techniques
• Data Cleaning
• Handling Missing Values
• Data Transformation
• Feature Engineering

Module 4: Exploratory Data Analysis (EDA)

• Data Visualization
• Statistical Analysis
• Correlation Analysis
• Outlier Detection
• Pattern Recognition

Module 5: Supervised Learning

• Introduction to Supervised Learning
• Classification Algorithms
• Regression Algorithms
• Model Training
• Model Evaluation

Module 6: Unsupervised Learning

• Clustering Techniques
• K-Means Clustering
• Hierarchical Clustering
• Association Rule Mining
• Dimensionality Reduction

Module 7: Machine Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes

Module 8: Model Evaluation & Optimization

• Training and Testing Datasets
• Cross Validation
• Performance Metrics
• Hyperparameter Tuning
• Bias-Variance Tradeoff

Module 9: Deep Learning Fundamentals

• Introduction to Neural Networks
• Artificial Neural Networks (ANN)
• Deep Learning Concepts
• Activation Functions
• Introduction to TensorFlow and Keras

Module 10: Natural Language Processing (NLP)

• Text Processing
• Sentiment Analysis
• Text Classification
• Chatbot Fundamentals
• Language Models Overview

Module 11: Computer Vision Basics

• Image Processing Concepts
• Image Classification
• Object Detection Overview
• Facial Recognition Basics
• Real-World Applications

Module 12: Machine Learning Deployment

• Model Deployment Concepts
• Creating APIs for ML Models
• Cloud Deployment Basics
• Model Monitoring
• Production Best Practices

Module 13: Ethics and Responsible AI

• AI Ethics
• Data Privacy
• Fairness and Bias
• Explainable AI
• Responsible Machine Learning

Module 14: Live Projects & Case Studies

• House Price Prediction System
• Customer Churn Prediction
• Sales Forecasting Project
• Sentiment Analysis Application
• Image Classification Project
• End-to-End Machine Learning Solution