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:    / 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...
Dataets (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) KNearest 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) KMeans Clustering
⌨ (3:23:46) Principal Component Analysis
⌨ (3:33:54) KMeans and PCA Implementations

Thanks to our Champion and Sponsor supporters:
Raymond Odero
Agustín Kussrow
aldo ferretti
Otis Morgan
DeezMaster



Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news