Welcome to the Machine Learning 101 course! This comprehensive guide will take you through the fundamentals of machine learning, covering various algorithms and their applications. Whether you're a beginner or looking to refresh your knowledge, this course is designed to cater to all levels.

Course Outline

  • Introduction to Machine Learning

    • What is Machine Learning?
    • Types of Machine Learning

  • Data Preprocessing

    • Data Cleaning
    • Data Transformation
    • Feature Engineering

  • Supervised Learning

    • Linear Regression
    • Logistic Regression
    • Decision Trees

  • Unsupervised Learning

    • Clustering
    • Association Rules

  • Neural Networks and Deep Learning

    • Introduction to Neural Networks
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)

  • Evaluation and Optimization

    • Model Evaluation Metrics
    • Hyperparameter Tuning

  • Real-World Applications

    • Natural Language Processing
    • Computer Vision
    • Predictive Analytics

Learning Resources

For further reading, you can explore the following resources:

Course Prerequisites

  • Basic understanding of programming (Python preferred)
  • Familiarity with statistics and linear algebra

Machine Learning

Join us on this exciting journey to master machine learning!