Introduction to Machine Learning

Welcome to the Introduction to Machine Learning course! This is a foundational course that will introduce you to the basics of machine learning and its applications. In this course, you will learn about various algorithms, techniques, and tools used in machine learning.

Course Outline

• Week 1: Introduction to Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning
  • Machine Learning Workflow

• Week 2: Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Decision Trees

• Week 3: Unsupervised Learning

  • Clustering
  • Association Rules

• Week 4: Reinforcement Learning

  • Markov Decision Processes
  • Q-Learning

• Week 5: Introduction to Deep Learning

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

Learning Objectives

• Understand the basic concepts of machine learning.
• Learn different types of machine learning algorithms.
• Gain hands-on experience with machine learning tools and libraries.
• Apply machine learning techniques to real-world problems.

Course Materials

• Machine Learning Book
• Scikit-learn Documentation
• TensorFlow Documentation

Useful Resources

• Kaggle - For practicing machine learning with real-world datasets.
• Udacity Machine Learning Nanodegree - A comprehensive course covering various aspects of machine learning.

Machine Learning