Course Overview

This course is designed for beginners who want to learn the basics of machine learning. It will provide a solid foundation in key concepts, algorithms, and practical applications of machine learning.

Course Structure

• Module 1: Introduction to Machine Learning
  • Introduction to machine learning and its applications
  • Supervised learning vs. unsupervised learning
  • Types of machine learning algorithms
  • Overview of the machine learning process

• Module 2: Data Preprocessing and Exploration
  • Data cleaning and handling missing values
  • Feature selection and feature engineering
  • Exploratory data analysis
  • Data visualization techniques

• Module 3: Supervised Learning Algorithms
  • Linear regression
  • Logistic regression
  • Decision trees and random forests
  • K-nearest neighbors (KNN)
  • Support vector machines (SVM)
• Module 4: Unsupervised Learning Algorithms
  • K-means clustering
  • Hierarchical clustering
  • Principal Component Analysis (PCA)
  • Association rule learning
• Module 5: Model Evaluation and Performance Metrics
  • Evaluating classification models
  • Evaluating regression models
  • Confusion matrix, precision, recall, and F1-score
  • Overfitting and underfitting
  • Cross-validation techniques
• Module 6: Introduction to Deep Learning
  • Neural networks and their components
  • Activation functions
  • Feedforward neural networks
  • Backpropagation algorithm
  • Introduction to deep learning frameworks (e.g., TensorFlow, Keras)
• Module 7: Practical Applications of Machine Learning
  • Image classification
  • Text classification
  • Sentiment analysis
  • Recommendation systems
• Module 8: Ethics and Bias in Machine Learning
  • Ethical considerations in machine learning
  • Addressing bias in machine learning models
  • Fairness and transparency in machine learning
• Conclusion

Course Materials

Throughout the course, you will have access to various learning materials, including lecture slides, code examples, and additional resources to deepen your understanding of machine learning concepts.