2020 Machine Learning Roadmap (87% valid for 2024)
Skills:
Supervised Learning53%
Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction.
Although the title of this video says machine learning roadmap, you should treat it as a compass. Explore it, follow your curiosity, learn something and use what you learn to create your next steps.
Links:
Interactive Machine Learning Roadmap - https://dbourke.link/mlmap
Machine Learning Roadmap Resources - https://github.com/mrdbourke/machine-learning-roadmap
Learn ML (beginner-friendly courses I teach) - https://www.mrdbourke.com/ml-courses/
ML courses/books I recommend - https://www.mrdbourke.com/ml-resources/
Read my novel Charlie Walks - https://www.charliewalks.com
Timestamps:
0:00 - Hello & logistics
0:57 - PART 0: INTRO
1:42 - Brief overview of topics
3:05 - What is machine learning?
4:37 - Machine learning vs. traditional programming
7:41 - Why use machine learning?
8:44 - The number 1 rule of machine learning
10:45 - What is machine learning good for?
14:27 - How Tesla uses machine learning
17:57 - What we're going to cover in this video
20:52 - PART 1: Machine Learning Problems
22:27 - Categories of learning
26:17 - Machine learning problem domains
29:04 - Classification
33:57 - Regression
39:35 - PART 2: Machine Learning Process
41:57 - 6 major steps in a machine learning project
43:57 - Data collection
49:15 - Data preparation
1:04:00 - Training a model
1:23:33 - Analysis/evaluation
1:26:40 - Serving a model
1:29:09 - Retraining a model
1:30:07 - An example machine learning project
1:33:15 - PART 3: Machine Learning Tools
1:34:20 - Machine learning tools overview
1:38:36 - Machine learning toolbox (experiment tracking)
1:39:54 - Pretrained models for transfer learning
1:41:49 - Data and model tracking
1:43:35 - Cloud compute services
1:47:07 - Deep learning hardware (build your own deep learning PC)
1:47:53 - AutoML (automatic machine learning)
1:51:47 - Explainability
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Chapters (33)
Hello & logistics
0:57
PART 0: INTRO
1:42
Brief overview of topics
3:05
What is machine learning?
4:37
Machine learning vs. traditional programming
7:41
Why use machine learning?
8:44
The number 1 rule of machine learning
10:45
What is machine learning good for?
14:27
How Tesla uses machine learning
17:57
What we're going to cover in this video
20:52
PART 1: Machine Learning Problems
22:27
Categories of learning
26:17
Machine learning problem domains
29:04
Classification
33:57
Regression
39:35
PART 2: Machine Learning Process
41:57
6 major steps in a machine learning project
43:57
Data collection
49:15
Data preparation
1:04:00
Training a model
1:23:33
Analysis/evaluation
1:26:40
Serving a model
1:29:09
Retraining a model
1:30:07
An example machine learning project
1:33:15
PART 3: Machine Learning Tools
1:34:20
Machine learning tools overview
1:38:36
Machine learning toolbox (experiment tracking)
1:39:54
Pretrained models for transfer learning
1:41:49
Data and model tracking
1:43:35
Cloud compute services
1:47:07
Deep learning hardware (build your own deep learning PC)
1:47:53
AutoML (automatic machine learning)
1:51:47
Explainability
🎓
Tutor Explanation
DeepCamp AI