2020 Machine Learning Roadmap (87% valid for 2024)

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-...
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 (explaining the outputs of your machine learning model)
1:53:38 - Machine learning lifecycle (tools for end-to-end projects)
1:59:24 - PART 4: Machine Learning Mathematics
1:59:37 - The main branches of mathematics used in machine learning
2:03:16 - How I learn the math for machine learning
2:06:37 - PART 5: Machine Learning Resources
2:07:17 - A warning
2:08:42 - Where to start learning machine learning
2:14:51 - Made with ML (one of my favourite new websites for ML)
2:16:07 - Wokera ai (test your AI skills)
2:17:17 - A beginner-friendly path to start machine learning
2:19:02 - An advanced path for learning machine learning (after the beginner path)
2:21:43 - Where to learn the mathematics for machine learning
2:22:23 - Books for machine learning
2:24:27 - Where to learn cloud services
2:24:47 - Helpful rules and tidbits of machine learning
2:26:05 - How and why you should create your own blog
2:28:29 - Example machine learning curriculums
2:30:19 - Useful machine learning websites to visit
2:30:59 - Open-source datasets
2:31:26 - How to learn how to learn
2:32:57 - PART 6: Summary & Next Steps

Connect elsewhere:
Get email updates on my work - https://dbourke.link/newsletter
Support on Patreon - https://bit.ly/mrdbourkepatreon

Web - https://dbourke.link/web
Quora - https://dbourke.link/quora
Medium - https://dbourke.link/medium
Twitter - https://dbourke.link/twitter
LinkedIn - https://dbourke.link/linkedin

#machinelearning #datascience