Machine Learning Course Introduction

Welcome to the "Machine Learning" course in the ABC Compute Forum! This page provides an overview of the course content and key topics.

• Course Description This course aims to introduce the fundamental concepts of machine learning, covering both theoretical and practical aspects. You will learn about various algorithms, techniques, and tools used in machine learning, and apply them to real-world problems.

• Course Outline

  • Week 1: Introduction to Machine Learning
    • What is Machine Learning?
    • Types of Machine Learning
    • Machine Learning Process
  • Week 2: Supervised Learning
    • Linear Regression
    • Logistic Regression
    • Decision Trees
  • Week 3: Unsupervised Learning
    • Clustering
    • Association Rules
    • Dimensionality Reduction
  • Week 4: Reinforcement Learning
    • Markov Decision Processes
    • Q-Learning
    • Policy Gradient Methods
  • Week 5: Neural Networks and Deep Learning
    • Introduction to Neural Networks
    • Backpropagation Algorithm
    • Convolutional Neural Networks
    • Recurrent Neural Networks

• Prerequisites

  • Basic knowledge of programming (Python preferred)
  • Basic understanding of mathematics (linear algebra, calculus)

• Resources

  • Python Machine Learning by Sebastian Raschka
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

• Further Reading Explore more about machine learning in our ABC Compute Forum.

• Image Gallery

  

Enjoy your learning journey in the world of machine learning!