1/ Starting my road to learning about AI. I’ve done some courses here and there but I am planning a more consistent effort over the next few years.
Mostly documenting my path for others and for fun. Often people suggest things I didn’t know which can be helpful.
Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.
✏️ Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed
⭐️ Code and Resources ⭐️
🔗 Supervised learning (classification/MAGIC): https://colab.research.google.com/drive/16w3TDn_tAku17mum98EWTmjaLHAJcsk0?usp=sharing
🔗 Supervised learning (regression/bikes): https://colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?usp=sharing
🔗 Unsupervised learning (seeds): https://colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?usp=sharing
🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)
🔗 MAGIC dataset: https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope
🔗 Bikes dataset: https://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand
🔗 Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datasets/seeds
🏗 Google provided a grant to make this course possible.
⭐️ Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:00:58) Data/Colab Intro
⌨️ (0:08:45) Intro to Machine Learning
⌨️ (0:12:26) Features
⌨️ (0:17:23) Classification/Regression
⌨️ (0:19:57) Training Model
⌨️ (0:30:57) Preparing Data
⌨️ (0:44:43) K-Nearest Neighbors
⌨️ (0:52:42) KNN Implementation
⌨️ (1:08:43) Naive Bayes
⌨️ (1:17:30) Naive Bayes Implementation
⌨️ (1:19:22) Logistic Regression
⌨️ (1:27:56) Log Regression Implementation
⌨️ (1:29:13) Support Vector Machine
⌨️ (1:37:54) SVM Implementation
⌨️ (1:39:44) Neural Networks
⌨️ (1:47:57) Tensorflow
⌨️ (1:49:50) Classification NN using Tensorflow
⌨️ (2:10:12) Linear Regression
⌨️ (2:34:54) Lin Regression Implementation
⌨️ (2:57:44) Lin Regression using a Neuron
⌨️ (3:00:15) Regression NN using Tensorflow
⌨️ (3:13:13) K-Means Clustering
⌨️ (3:23:46) Principal Component Analysis
⌨️ (3:33:54) K-Means and PCA Implementations
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How I would learn Machine Learning (if I could start over)
In this video, I give you my step by step process on how I would learn Machine Learning if I could start over again, and provide you with all recommended resources.
All courses: https://github.com/AssemblyAI-Examples/ML-Study-Guide
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#MachineLearning #DeepLearning
0:00 Introduction
1:01 MATH
1:58 PYTHON PYTHON
2:37 ML TECH STACK ML TECH STACK
3:35 ML COURSES ML COURSES
4:44 HANDS-ON & DATA PREPARATION
5:17 PRACTICE & PRACTICE & BUILD PORTFOLIO
6:16 SPECIALIZE & CREATE BLOG
How to learn AI and ML in 2023 - A complete roadmap
Free monthly learning resources and insights https://gilesknowledge.substack.com/
Here are the links to the machine learning resources mentioned:
http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
https://opentechschool.github.io/python-data-intro/core/recap.html
https://www.kaggle.com/learn/intro-to-machine-learning
https://developers.google.com/machine-learning/crash-course/
https://www.tensorflow.org/tutorials
https://machinelearningmastery.com/start-here/
https://www.youtube.com/playlist?list=PL8erL0pXF3JYm7VaTdKDaWc8Q3FuP8Sa7
https://mml-book.github.io/
https://www.probabilitycourse.com/chapter1/1_1_0_what_is_probability.php
https://www.statisticsdonewrong.com/
https://github.com/ageron/handson-ml3
https://courses.cs.duke.edu/spring20/compsci527/papers/Domingos.pdf
https://datascience.stackexchange.com/
https://stats.stackexchange.com/?tags=machine-learning
https://pytorch.org/tutorials/
https://scikit-learn.org/stable/tutorial/index.html
That's 16 in total!
Learn Data Science
🎓 Data Quest - https://bit.ly/3yClqbZ
Learn Python with Giles
🎓 Exploratory Data Analysis with Python and Pandas - https://bit.ly/2QXMpxJ
🎓 Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09
📚 My favourite python books for beginners (affiliate links)
📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE
📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl
📙 Python Basics - A Practical Introduction to Python 3 https://amzn.to/3fHRMdb
📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr
📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b
🆓 Free Python Resource
https://python-programming.quantecon.org/intro.html
(This is a great introduction to python)
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#learnmachinelearning #machinelearning #learnpython
2020 Machine Learning Roadmap (95% valid for 2023)
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
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