2020 Machine Learning Roadmap (87% valid for 2024)
1,066,770 views 4 years ago
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

Shorts