Machine Learning for Everybody – Full Course_part1

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:

⭐️ 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.

⭐️ 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

Hosted on Acast. See acast.com/privacy for more information.