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Title:2020 Machine Learning Roadmap (still valid for 2021)
Duration:02:37:14
Viewed:495,349
Published:12-07-2020
Source:Youtube

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 machine learning (via the beginner course I teach) - https://dbourke.link/mlcourse
Learn deep learning (a more advanced course I teach) - https://dbourke.link/ZTMTFcourse

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

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