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Find out everything you need to know about learning AI in 2023, from tips to get you started, helpful resources, and insights from industry experts.

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 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

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 Get your Free Token for AssemblyAI Speech-To-Text API 👇 https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_pat_60 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: https://www.assemblyai.com 🐦 Twitter: https://twitter.com/AssemblyAI 🦾 Discord: https://discord.gg/Cd8MyVJAXd ▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1 🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #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

1. Learn broadly. 2. Learn deeply. 3. Don't be afraid to re-learn

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) ⚙ My Gear 💡 BenQ Screen Bar Desk Light - https://amzn.to/3tH6ysL 🎧 Sony Noise Cancelling Headphones - https://amzn.to/3tLl82G 📱 Social Media https://www.instagram.com/gilesmcmullen/ https://twitter.com/GilesMcMullen 👌 SUBSCRIBE to ME!👌 https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg?sub_confirmation=1 #learnmachinelearning #machinelearning #learnpython

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Explore this conversation and more from the learnmachinelearning community

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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. 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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