Finding into machine learning is quite the journey. And as any adventurer knows, at times it can be useful to have a compass to figure out if you might be heading in the suitable route.
Despite the fact that the title of this movie claims machine learning roadmap, you ought to take care of it as a compass. Discover it, observe your curiosity, understand a little something and use what you discover to create your following actions.
Inbound links:
Interactive Machine Learning Roadmap – https://dbourke.link/mlmap
Machine Learning Roadmap Sources – https://github.com/mrdbourke/device-learning-roadmap
Master ML (beginner-welcoming courses I teach) – https://www.mrdbourke.com/ml-programs/
ML courses/guides I suggest – https://www.mrdbourke.com/ml-methods/
Browse my novel Charlie Walks – https://www.charliewalks.com
Timestamps:
:00 – Hello there & logistics
:57 – Aspect : INTRO
1:42 – Brief overview of subjects
3:05 – What is machine learning?
4:37 – Machine learning vs. conventional programming
7:41 – Why use machine learning?
8:44 – The variety 1 rule of machine learning
10:45 – What is machine learning fantastic for?
14:27 – How Tesla works by using machine learning
17:57 – What we are going to deal with in this movie
20:52 – Aspect 1: Machine Learning Difficulties
22:27 – Types of learning
26:17 – Machine learning issue domains
29:04 – Classification
33:57 – Regression
39:35 – Part 2: Machine Learning Method
41:57 – 6 important measures in a machine learning project
43:57 – Information collection
49:15 – Facts planning
1:04:00 – Teaching a product
1:23:33 – Evaluation/analysis
1:26:40 – Serving a design
1:29:09 – Retraining a model
1:30:07 – An example machine learning venture
1:33:15 – Aspect 3: Machine Learning Resources
1:34:20 – Machine learning resources overview
1:38:36 – Machine learning toolbox (experiment monitoring)
1:39:54 – Pretrained types for transfer mastering
1:41:49 – Knowledge and model monitoring
1:43:35 – Cloud compute services
1:47:07 – Deep mastering hardware (build your possess deep understanding Personal computer)
1:47:53 – AutoML (computerized machine learning)
1:51:47 – Explainability (describing the outputs of your machine learning design)
1:53:38 – Machine learning lifecycle (tools for close-to-close jobs)
1:59:24 – Section 4: Machine Learning Arithmetic
1:59:37 – The major branches of mathematics employed in machine learning
2:03:16 – How I find out the math for machine learning
2:06:37 – Aspect 5: Machine Learning Means
2:07:17 – A warning
2:08:42 – The place to get started mastering machine learning
2:14:51 – Made with ML (one particular of my favourite new internet websites for ML)
2:16:07 – Wokera ai (check your AI capabilities)
2:17:17 – A beginner-helpful route to begin machine learning
2:19:02 – An superior path for understanding machine learning (soon after the newbie route)
2:21:43 – Where by to understand the arithmetic for machine learning
2:22:23 – Textbooks for machine learning
2:24:27 – Exactly where to understand cloud expert services
2:24:47 – Handy guidelines and tidbits of machine learning
2:26:05 – How and why you need to make your own web site
2:28:29 – Illustration machine learning curriculums
2:30:19 – Valuable machine learning sites to check out
2:30:59 – Open-resource datasets
2:31:26 – How to find out how to learn
2:32:57 – Section 6: Summary & Following Techniques
Join elsewhere:
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Help on Patreon – https://little bit.ly/mrdbourkepatreon
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