Machine Learning for Everybody by Kylie Ying - Course Notebook https://www.youtube.com/watch?v=i_LwzRVP7bg

Overview

This repository contains the Jupyter notebook used to follow along with the online course "Machine Learning for Everybody" by Kylie Ying. The course offers a comprehensive introduction to machine learning using Python, covering essential libraries such as Numpy, Pandas, TensorFlow, and Matplotlib. To interact with these notebooks, simply download them alongside their corresponding dataset. These datasets are real world data concerning bike sales in Seoul, South Korea; teloscope data, and seeds data.

Course /Project/ Notes Content

The course includes detailed explanations and practical examples of various machine learning techniques and models including:

• Loss Functions
• Data Splits
• Nearest Neighbor
• Naive Bayes
• Support Vector Machines
• Neural Networks
• Linear Regression
• Unsupervised Learning

Technologies Used

• Python
• Jupyter Notebook
• Numpy
• Pandas
• TensorFlow
• Matplotlib

Setup and Installation

Clone the Repository

git clone https://github.com/yourgithubusername/machine-learning-course-notebook.git
cd machine-learning-course-notebook
pip install -r requirements.txt
jupyter notebook ML_LEARNING.ipynb

After opening the notebook in Jupyter, you can run each cell to see the output of the code blocks. The notebook is interactive, allowing you to modify the code and immediately see the results of your changes.

You can adjust any of the paths or instructions as necessary to fit your specific repository setup.