Machine Learning for Everybody – Full Course

Embark on a comprehensive 3-4 hour course designed to introduce Machine Learning concepts to absolute beginners. Gain a solid foundation in Machine Learning basics and learn to implement various concepts using TensorFlow. Explore supervised learning techniques like classification and regression, as well as unsupervised learning methods. Work with real-world datasets, including MAGIC Gamma Telescope, Seoul Bike Sharing Demand, and wheat seeds. Master essential algorithms such as K-Nearest Neighbors, Naive Bayes, Logistic Regression, Support Vector Machines, and Neural Networks. Dive into practical implementations using Google Colab, covering topics from data preparation to advanced techniques like K-Means Clustering and Principal Component Analysis. By the end of this course, developed by Kylie Ying and supported by Google, you'll have hands-on experience with key Machine Learning concepts and be well-equipped to tackle more advanced topics in the field.

⌨️ Intro
⌨️ Data/Colab Intro
⌨️ Intro to Machine Learning
⌨️ Features
⌨️ Classification/Regression
⌨️ Training Model
⌨️ Preparing Data
⌨️ K-Nearest Neighbors
⌨️ KNN Implementation
⌨️ Naive Bayes
⌨️ Naive Bayes Implementation
⌨️ Logistic Regression
⌨️ Log Regression Implementation
⌨️ Support Vector Machine
⌨️ SVM Implementation
⌨️ Neural Networks
⌨️ Tensorflow
⌨️ Classification NN using Tensorflow
⌨️ Linear Regression
⌨️ Lin Regression Implementation
⌨️ Lin Regression using a Neuron
⌨️ Regression NN using Tensorflow
⌨️ K-Means Clustering
⌨️ Principal Component Analysis
⌨️ K-Means and PCA Implementations