This repository contains machine learning notebooks from my website appliedprogramming.net. The examples and projects in the repo contain the explantory theory about the different algorithms used and the necessary preprocessing steps to clean, visualize and analyse the data. The datasets for most of the examples are taken from the UCI Machine Learning repository.
| Deep Learning using TensorFlow |
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| Introduction to TensorFlow |
| Transfer Learning for Classifying Animal images |
| Speech Recognition of Digits |
| One Shot Learning to Classify Omniglot data |
| How to use SyntaxNet |
| Machine Learning using Scikit-Learn |
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| Introduction |
| Getting Started |
| Regression |
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| Predicting Electrical Energy Output with Regression Analysis |
| Air Quality Prediction |
| Advanced Scikit-Learn Regression Techniques |
| Clustering |
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| Customer Segmentation For Market Analysis |
| Seeds Clustering |
| Applying K-Means for Image Quantization |
| Ensemble Learning |
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| Ensemble Learning to Classify Patients with Heart Disease |
| OnlineNewsPopularity Classification using Ensembles |
| Classifying Default of Credit Card Clients |
| Machine Learning using GraphLab Create library |
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| Predicting House Prices using GraphLab Create |
| Predicting sentiment from product reviews |
| Building a song recommender |
| Document Retrieval from Wikipedia data |
NOTE: Please feel free to send pull requests and help me improve the code base so that people who want to get into this fascinating field of ML and AI can get the best resources possible! :)
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