THE FUTURE IS HERE

AI Now

2020 Machine Learning Roadmap (95% valid for 2022)

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 (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