Machine Learning for Everybody – Full Course



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

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This Post Has 22 Comments

  1. Kylie Ying

    Thanks for sharing, freeCodeCamp!! Be sure to subscribe to my channel, Kylie Ying, as well 🙂

    Prerequisites for this course: Python, pandas, numpy, matplotlib (or at least enough about Python libraries and documentation to follow along). Some parts of the video will also discuss probability.

  2. Henry Thomas

    Thank you a lot for this video. This is very interesting and informative. Keep posting like those amazing videos, this is awesome.

  3. Eric Koontz

    I really like the presentation of your video the time tou take to explain and build the concepts is spot on to get from zero knowledge to yes i understand this.

  4. I have no idea how my YouTube algorithm brought me here while I was sleeping but it made for some strange dreams

  5. Shawn

    Just because you know how to drive a car does not mean you know how a car works.
    This is essentially a driving lesson posing as an automotive engineering lesson.

  6. Sanjay Meena

    This worked incredibly well! I can finally play it thanks

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