Machine Learning for Everybody – Full Course

By freeCodeCamp.org
Published: Sep 26, 2022

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Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.

Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed

Code and Resources
• Supervised learning (classification/MAGIC): https://colab.research.google.com/dri…
• Supervised learning (regression/bikes): https://colab.research.google.com/dri…
• Unsupervised learning (seeds): https://colab.research.google.com/dri…
• Datasets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)
• MAGIC dataset: https://archive.ics.uci.edu/ml/datase…
• Bikes dataset: https://archive.ics.uci.edu/ml/datase…
• Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datase…

Google provided a grant to make this course possible.

Contents
• (0:00:00) Intro
• (0:00:58) Data/Colab Intro
• (0:08:45) Intro to Machine Learning
• (0:12:26) Features
• (0:17:23) Classification/Regression
• (0:19:57) Training Model
• (0:30:57) Preparing Data
• (0:44:43) K-Nearest Neighbors
• (0:52:42) KNN Implementation
• (1:08:43) Naive Bayes
• (1:17:30) Naive Bayes Implementation
• (1:19:22) Logistic Regression
• (1:27:56) Log Regression Implementation
• (1:29:13) Support Vector Machine
• (1:37:54) SVM Implementation
• (1:39:44) Neural Networks
• (1:47:57) Tensorflow
• (1:49:50) Classification NN using Tensorflow
• (2:10:12) Linear Regression
• (2:34:54) Lin Regression Implementation
• (2:57:44) Lin Regression using a Neuron
• (3:00:15) Regression NN using Tensorflow
• (3:13:13) K-Means Clustering
• (3:23:46) Principal Component Analysis
• (3:33:54) K-Means and PCA Implementations

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