Machine Learning for Everybody – Full Course

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?uspsharing
Supervised learning (regression/bikes): https://colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?uspsharing
Unsupervised learning (seeds): https://colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?uspsharing
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/MAGICGammaTelescope
Bikes dataset: https://archive.ics.uci.edu/ml/datasets/SeoulBikeSharingDemand
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