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Ocademy Open Machine Learning Book

PREREQUISITES

1. Python programming introduction
2. Python programming basics
3. Python programming advanced

DATA SCIENCE

4. Introduction
5. Working with data
6. Data visualization
7. Data Science lifecycle
8. Data Science in the cloud
9. Data Science in the real world

MACHINE LEARNING BASICS

10. Machine Learning overview
11. Regression models for Machine Learning
12. Getting started with classification
13. Parameter Optimization
- 13.1. Loss function
- 13.2. Gradient descent

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

34. Overview
35. Problem framing
36. Data engineering
37. Model training & evaluation
38. Model deployment

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

42. Self-paced assignments
43. Slides

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