Machine Learning with Python

Machine Learning with Python is a comprehensive course that covers the fundamentals of machine learning and its application using Python. This course is designed for beginners as well as those who have some experience with programming but want to delve deeper into the field of machine learning.

Course Outline

• Introduction to Machine Learning

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

• Python Basics

  • Python Syntax
  • Data Types
  • Control Structures

• Data Preprocessing

  • Data Cleaning
  • Data Transformation
  • Feature Scaling

• Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines

• Unsupervised Learning

  • Clustering
  • Association Rules
  • Dimensionality Reduction

• Deep Learning

  • Neural Networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)

Course Materials

• Textbooks

  • "Python Machine Learning" by Sebastian Raschka
  • "Deep Learning with Python" by François Chollet

• Online Resources

  • Kaggle
  • Scikit-learn Documentation
  • TensorFlow Documentation

Prerequisites

• Basic knowledge of Python programming
• Familiarity with basic statistics and linear algebra

Why Learn Machine Learning with Python?

• Versatility: Python is a versatile language that can be used for a wide range of applications, including web development, data analysis, and machine learning.
• Community Support: Python has a large and active community, which means you can find plenty of resources and support when you need it.
• Ease of Use: Python is known for its simplicity and readability, making it an excellent choice for beginners.

Python Machine Learning

For more information on Python machine learning, check out our Python for Data Science course.