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/drive/16w3TDn_tAku17mum98EWTmjaLHAJcsk0?usp=sharing
Supervised learning (regression/bikes): https://colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?usp=sharing
Unsupervised learning (seeds): https://colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?usp=sharing
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/datasets/MAGIC+Gamma+Telescope
Bikes dataset: https://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand
Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datasets/seeds
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
Thanks to our Champion and Sponsor supporters:
Raymond Odero
Agustín Kussrow
aldo ferretti
Otis Morgan
DeezMaster
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