Short Description:

Learn how to design, train, and evaluate machine learning models using real-world data. This course provides a solid foundation in supervised and unsupervised learning techniques, preparing you for advanced AI and data science applications.

Detailed Course Overview:

This comprehensive course introduces students to the principles and practices of machine learning, one of the most transformative fields in artificial intelligence.
Through practical examples and interactive projects, participants will learn how to use algorithms to make predictions, classify data, and uncover patterns in large datasets.

By the end of this course, learners will be able to build and deploy intelligent models capable of learning from data, improving performance, and generating valuable insights for business and research.

Modules:

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Real-world Applications and Case Studies
• ML vs AI vs Deep Learning

Module 2: Data Preparation and Feature Engineering

• Understanding Data Types and Sources
• Data Cleaning and Preprocessing
• Feature Extraction and Selection
• Handling Missing and Imbalanced Data

Module 3: Supervised Learning Techniques

• Linear and Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Model Evaluation Metrics (Accuracy, Precision, Recall, F1)
• Module 4: Unsupervised Learning and Clustering
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Dimensionality Reduction and Visualization

Module 5: Neural Networks and Deep Learning Basics

• Perceptron and Multi-Layer Networks
• Activation Functions
• Training Neural Networks
• Overfitting and Regularization

Module 6: Model Optimization and Deployment

• Hyperparameter Tuning
• Cross Validation
• Model Deployment using Cloud Platforms
• Ethical and Responsible AI

Module 7: Final Project

• End-to-End ML Project using Python and Scikit-learn
• Presentation and Evaluation

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