Ocademy Open Machine Learning Book

PREREQUISITES

DATA SCIENCE

MACHINE LEARNING BASICS

ADVANCED MACHINE LEARNING

14. Clustering models for Machine Learning
- 14.1. Introduction to clustering
- 14.2. K-Means clustering
15. Getting started with ensemble learning
16. Introduction to Gradient Boosting
17. Unsupervised learning
18. Kernel method
19. Unsupervised learning: PCA and clustering
20. Model selection

DEEP LEARNING

21. Intro to Deep Learning
22. Neural Networks
23. Convolutional Neural Networks
- 23.9. Stylenet / Neural-Style
- 23.10. Deepdream in TensorFlow
24. Recurrent Neural Networks
- 24.1.1. Long-short term memory
- 24.1.2. Bidirectional RNN
25. Time series
26. Autoencoder
27. Object detection
28. Image classification
29. Image segmentation
30. Natural Language Processing
- 30.7.1. Text Preprocessing
- 30.7.2. Word embedding
31. Generative adversarial networks
32. Diffusion Model
33. Deep Q-learning

MACHINE LEARNING OPERATIONS

Large Language Models

39. Introduction
40. Large Language Models Basic
41. Implementing a GPT model from Scratch To Generate Text
- 41.10. Pretraining on Unlabeled Data

OTHERS

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