This course designed to provide a comprehensive understanding of machine learning concepts and practical application within a 3-month timeframe. The schedule is flexible and can be adjusted based on the pace of learning and the specific needs of the participants. The course encourages hands-on experience through projects and real-world case studies.

Pre-requisite:

  • Basic knowledge of computers.
  • Python Programming

 

Month 1: Introduction to Machine Learning

Week 1-2: Fundamentals of Machine Learning

  • Overview of machine learning concepts
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Applications of machine learning in real-world scenarios

Week 3-4: Python for Machine Learning

  • Setting up the Python environment for machine learning
  • Basics of Python programming for data manipulation and analysis
  • Introduction to key Python libraries for machine learning: NumPy, Pandas, and Scikit-Learn

Month 2: Supervised Learning and Model Evaluation

Week 5-6: Regression and Classification Algorithms

  • Linear regression
  • Logistic regression
  • Decision trees and random forests

Week 7-8: Model Evaluation and Hyperparameter Tuning

  • Metrics for evaluating model performance
  • Cross-validation and overfitting
  • Hyperparameter tuning techniques

Month 3: Unsupervised Learning and Real-world Applications

Week 9-10: Clustering and Dimensionality Reduction

  • K-means clustering
  • Hierarchical clustering
  • Principal Component Analysis (PCA)

Week 11-12: Real-world Applications and Final Project

  • Applications of machine learning in various industries
  • Building a complete machine learning project
  • Final project presentation and review

Final Project:

  • Apply machine learning algorithms to solve a real-world problem
  • Dataset selection, preprocessing, and feature engineering
  • Model training, evaluation, and deployment

Additional Topics Throughout the Course:

  • Natural Language Processing (NLP) and text analysis
  • Introduction to deep learning and neural networks
  • Ethical considerations and responsible AI in machine learning