Machine Learning for Everybody – Full Course
By freeCodeCamp.org
Published: Sep 26, 2022“
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/dri…
- Supervised learning (regression/bikes): https://colab.research.google.com/dri…
- Unsupervised learning (seeds): https://colab.research.google.com/dri…
- Datasets (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/datase…
- Bikes dataset: https://archive.ics.uci.edu/ml/datase…
- Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datase…
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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Tag: Kylie Ying
[#Script #Coding] Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
By freeCodeCamp.org
Published: Jun 15, 2022“
This course will give you an introduction to machine learning concepts and neural network implementation using Python and TensorFlow. Kylie Ying explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews.
Course created by Kylie Ying.
YouTube: https://youtube.com/ycubed
Twitter: https://twitter.com/kylieyying
Instagram: https://instagram.com/kylieyying/This course was made possible by a grant from Google’s TensorFlow team.
Resources
- Datasets: https://drive.google.com/drive/folder…
- Feedforward NN colab notebook: https://colab.research.google.com/dri…
- Wine review colab notebook: https://colab.research.google.com/dri…
Course Contents
- (0:00:00) Introduction
- (0:00:34) Colab intro (importing wine dataset)
- (0:07:48) What is machine learning?
- (0:14:00) Features (inputs)
- (0:20:22) Outputs (predictions)
- (0:25:05) Anatomy of a dataset
- (0:30:22) Assessing performance
- (0:35:01) Neural nets
- (0:48:50) Tensorflow
- (0:50:45) Colab (feedforward network using diabetes dataset)
- (1:21:15) Recurrent neural networks
- (1:26:20) Colab (text classification networks using wine dataset)
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