https://github.com/avinashyadav16/machine-learning-for-everybody

This is a folder that contains the codes, data files and notes from the course that was sponsored by the FreeCodeCamp and taught by Kylie Ying.
https://github.com/avinashyadav16/machine-learning-for-everybody

freecodecamp freecodecamp-curriculum freecodecamp-project machine machine-learning machine-learning-algorithms machine-learning-library machinelearning machinelearning-python

Last synced: 2 months ago
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This is a folder that contains the codes, data files and notes from the course that was sponsored by the FreeCodeCamp and taught by Kylie Ying.

• Host: GitHub
• URL: https://github.com/avinashyadav16/machine-learning-for-everybody
• Owner: avinashyadav16
• License: mit
• Created: 2023-10-12T21:21:23.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-25T17:39:07.000Z (over 2 years ago)
• Last Synced: 2024-01-26T13:01:57.560Z (over 2 years ago)
• Topics: freecodecamp, freecodecamp-curriculum, freecodecamp-project, machine, machine-learning, machine-learning-algorithms, machine-learning-library, machinelearning, machinelearning-python
• Language: Jupyter Notebook
• Homepage:
• Size: 13.9 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machine-Learning-For-Everybody

This is a folder 📂 that contains the codes 🧑‍💻, data files 🔢 and notes 📝 from the course that was sponsored by the FreeCodeCamp and taught by Kylie Ying. 👩‍🏫







## ⭐️ Contents ⭐️


⌨️ Introduction

⌨️ Data/Colab Introduction

⌨️ Introduction to Machine Learning

⌨️ Features

⌨️ Classification/Regression

⌨️ Training Model

⌨️ Preparing Data

⌨️ K-Nearest Neighbors

⌨️ KNN Implementation

⌨️ Naive Bayes

⌨️ Naive Bayes Implementation

⌨️ Logistic Regression

⌨️ Logistic Regression Implementation

⌨️ Support Vector Machine

⌨️ SVM Implementation

⌨️ Neural Networks

⌨️ Tensorflow

⌨️ Classification NN using Tensorflow

⌨️ Linear Regression

⌨️ Linear Regression Implementation

⌨️ Linear Regression using a Neuron

⌨️ Regression NN using Tensorflow

⌨️ K-Means Clustering

⌨️ Principal Component Analysis

⌨️ K-Means and PCA Implementations









## ⭐️ Resources ⭐️


🔗 MAGIC DATASET 


🔗 BIKE DATASET** 

🔗 SEEDS/WHEAT DATASET 

** NOTE: 

        For the bikes dataset, please open the downloaded csv file and remove special characters.









    

    

## 📌 Check out the course on YouTube 👇👇

 

     

        

            Machine Learning for Everybody Full Course