2020 Machine Learning Roadmap

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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.

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 machine learning (via the course I teach) - https://dbourke.link/mlcourse

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

#machinelearning #datascience