Machine Learning orientation
1 Introduction
I Machine learning: Shall we?
2 What is machine learning?
3 Outlook
- 3.1 Development of life
  - 3.1.1 When will superhuman AI come, and will it be good?
  - 3.1.2 AI aftermath scenario
- 3.2 Data religion: Dataism
4 Data Ethics
5 Strategies for machine learning
II Machine learning fundamentals
6 Machine learning fundamentals
7 ML project process
8 Machine learning types
9 ML algorithms
10 Food for the algorithms: Data
- 10.1 NLP
- 10.2 Dataset search from Google
11 ML application examples
III Explainable machine learning
12 Explainable machine learning
IV ML online resources
13 ML online courses
14 ML online resources
15 ML online books
- 15.1 Neural Networks and Deep Learning
- 15.2 Deep Learning
V Examples from Kaggle
16 Examples in Kaggle
17 Melbourne University AES/MathWorks/NIH Seizure Prediction
- 17.1 Winning solution (1st)
- 17.2 Solution(4th place)
18 Bosch Production Line Performance
19 Corporación Favorita Grocery Sales Forecasting
20 Rossmann Store Sales
- 20.1 1st place solution
- 20.2 3rd place solution
21 Severstal: Steel Defect Detection
22 Lyft 3D Object Detection for Autonomous Vehicles
- 22.1 3rd place solution
23 APTOS 2019 Blindness Detection
- 23.1 1st place solution summary
24 Predicting Molecular Properties
- 24.1 #1 Solution - hybrid
- 24.2 #2 solution 🤖 Quantum Uncertainty 🤖
VI Real world example
25 Real world example
VII Cloud-based machine learning
26 Cloud-based machine learning
VIII Kaggle Survey
27 Kaggle survey introduction
28 Results
References
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Machine learning orientation

Chapter 3 Outlook

What will the future bring for society?

The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom.
Isaac Asimov