Machine Learning for Everybody – Full Course

Description:

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. ⭐️ Code and Resources ⭐️ Supervised learning (classification/MAGIC) Supervised learning (regression/bikes) Unsupervised learning (seeds) Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters) MAGIC dataset Bikes dataset Seeds/wheat dataset 🏗 Google provided a grant to make this course possible. ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning. ⭐️ 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