Sunday, 29 January, 2023 14:52
This course is designed for students who want to learn Machine Learning. In this course, you will learn how to use Lists, Tuples, and Dictionaries and the most popular Python packages like Numpy, Pandas, and Matplotlib. Besides, learning the Math in Machine Learning, It is one of the main parts of this course and learning Machine Learning algorithms. By doing so, you will be able to work and solve problem in the World.
- Reference 1: The Quick Python Book, Third Edition
- Reference 2: Python Data Science Handbook: Essential Tools for Working with Data, [Data Files] )
- Reference 3: Mathematics for Machine Learning
- Reference 4: Python Machine Learning, third edition
- Reference 5: Matrix Cookbook (Actually, This book is not a reference for this course. But, it is highly recommended for further study in Linear Algebra)
- Note: Some topics through the course maybe not included in above references. Based on every specific topic you should read some other articles which are mentioned on that topic.
- Step 01 - Introduction
- Step 02 - Colections, Control Flows, and Linear Algebra
- Step 03 - Functions, Classes, and Matrix Calculations
- Step 04 - Files, Exceptions, Probability, and Optimization
- Step 05 - Numpy, Pandas and Matplotlib
- Step 06 - Machine Learning Basics(I)
- Step 07 - Machine Learning Basics(II)
- Step 08 - Supervised Learning Task
- Step 09 - Unsupervised Learning Task
- Step 10 - Neural Networks Definition
- Step 11 - Implementation Tools
- Step 12 - Neural Networks Models