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⌨️ Training Model
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Classroom Contents
Machine Learning for Everybody – Full Course
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- 1 ⌨️ Intro
- 2 ⌨️ Data/Colab Intro
- 3 ⌨️ Intro to Machine Learning
- 4 ⌨️ Features
- 5 ⌨️ Classification/Regression
- 6 ⌨️ Training Model
- 7 ⌨️ Preparing Data
- 8 ⌨️ K-Nearest Neighbors
- 9 ⌨️ KNN Implementation
- 10 ⌨️ Naive Bayes
- 11 ⌨️ Naive Bayes Implementation
- 12 ⌨️ Logistic Regression
- 13 ⌨️ Log Regression Implementation
- 14 ⌨️ Support Vector Machine
- 15 ⌨️ SVM Implementation
- 16 ⌨️ Neural Networks
- 17 ⌨️ Tensorflow
- 18 ⌨️ Classification NN using Tensorflow
- 19 ⌨️ Linear Regression
- 20 ⌨️ Lin Regression Implementation
- 21 ⌨️ Lin Regression using a Neuron
- 22 ⌨️ Regression NN using Tensorflow
- 23 ⌨️ K-Means Clustering
- 24 ⌨️ Principal Component Analysis
- 25 ⌨️ K-Means and PCA Implementations
