Machine Learning for Everybody – Full Course

Machine Learning for Everybody – Full Course

freeCodeCamp.org via freeCodeCamp Direct link

⌨️ Intro

1 of 25

1 of 25

⌨️ Intro

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Classroom Contents

Machine Learning for Everybody – Full Course

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

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