Homemade Machine Learning
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🤖 Interactive Machine Learning Experiments
For Octave/MatLab version of this repository please check machinelearningoctave project.
This repository contains examples of popular machine learning algorithms implemented in Python with mathematics behind them being explained. Each algorithm has interactive Jupyter Notebook demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions right in your browser. In most cases the explanations are based on this great machine learning course by Andrew Ng.
The purpose of this repository is not to implement machine learning algorithms by using 3^{rd} party library oneliners but rather to practice implementing these algorithms from scratch and get better understanding of the mathematics behind each algorithm. That's why all algorithms implementations are called "homemade" and not intended to be used for production.
Supervised Learning
In supervised learning we have a set of training data as an input and a set of labels or "correct answers" for each training set as an output. Then we're training our model (machine learning algorithm parameters) to map the input to the output correctly (to do correct prediction). The ultimate purpose is to find such model parameters that will successfully continue correct input→output mapping (predictions) even for new input examples.
Regression
In regression problems we do real value predictions. Basically we try to draw a line/plane/ndimensional plane along the training examples.
Usage examples: stock price forecast, sales analysis, dependency of any number, etc.
🤖
Linear Regression

📗 Math  Linear Regression  theory and links for further readings 
⚙️ Code  Linear Regression  implementation example 
▶️ Demo  Univariate Linear Regression  predictcountry happiness
score byeconomy GDP

▶️ Demo  Multivariate Linear Regression  predictcountry happiness
score byeconomy GDP
andfreedom index

▶️ Demo  Nonlinear Regression  use linear regression with polynomial and sinusoid features to predict nonlinear dependencies
Classification
In classification problems we split input examples by certain characteristic.
Usage examples: spamfilters, language detection, finding similar documents, handwritten letters recognition, etc.
🤖
Logistic Regression

📗 Math  Logistic Regression  theory and links for further readings 
⚙️ Code  Logistic Regression  implementation example 
▶️ Demo  Logistic Regression (Linear Boundary)  predict Iris flowerclass
based onpetal_length
andpetal_width

▶️ Demo  Logistic Regression (NonLinear Boundary)  predict microchipvalidity
based onparam_1
andparam_2

▶️ Demo  Multivariate Logistic Regression  MNIST  recognize handwritten digits from28x28
pixel images 
▶️ Demo  Multivariate Logistic Regression  Fashion MNIST  recognize clothes types from28x28
pixel images
Unsupervised Learning
Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.
Clustering
In clustering problems we split the training examples by unknown characteristics. The algorithm itself decides what characteristic to use for splitting.
Usage examples: market segmentation, social networks analysis, organize computing clusters, astronomical data analysis, image compression, etc.
🤖
Kmeans Algorithm

📗 Math  Kmeans Algorithm  theory and links for further readings 
⚙️ Code  Kmeans Algorithm  implementation example 
▶️ Demo  Kmeans Algorithm  split Iris flowers into clusters based onpetal_length
andpetal_width
Anomaly Detection
Anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
Usage examples: intrusion detection, fraud detection, system health monitoring, removing anomalous data from the dataset etc.
🤖
Anomaly Detection using Gaussian Distribution

📗 Math  Anomaly Detection using Gaussian Distribution  theory and links for further readings 
⚙️ Code  Anomaly Detection using Gaussian Distribution  implementation example 
▶️ Demo  Anomaly Detection  find anomalies in server operational parameters likelatency
andthreshold
Neural Network (NN)
The neural network itself isn't an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.
Usage examples: as a substitute of all other algorithms in general, image recognition, voice recognition, image processing (applying specific style), language translation, etc.
🤖
Multilayer Perceptron (MLP)

📗 Math  Multilayer Perceptron  theory and links for further readings 
⚙️ Code  Multilayer Perceptron  implementation example 
▶️ Demo  Multilayer Perceptron  MNIST  recognize handwritten digits from28x28
pixel images 
▶️ Demo  Multilayer Perceptron  Fashion MNIST  recognize the type of clothes from28x28
pixel images
Machine Learning Map
The source of the following machine learning topics map is this wonderful blog post
Prerequisites
Installing Python
Make sure that you have Python installed on your machine.
You might want to use venv standard Python library to create virtual environments and have Python, pip
and all dependent packages to be installed and served from the local project directory to avoid messing with system wide packages and their versions.
Installing Dependencies
Install all dependencies that are required for the project by running:
pip install r requirements.txt
Launching Jupyter Locally
All demos in the project may be run directly in your browser without installing Jupyter locally. But if you want to launch Jupyter Notebook locally you may do it by running the following command from the root folder of the project:
jupyter notebook
After this Jupyter Notebook will be accessible by http://localhost:8888
.
Launching Jupyter Remotely
Each algorithm section contains demo links to Jupyter NBViewer. This is fast online previewer for Jupyter notebooks where you may see demo code, charts and data right in your browser without installing anything locally. In case if you want to change the code and experiment with demo notebook you need to launch the notebook in Binder. You may do it by simply clicking the "Execute on Binder" link in top right corner of the NBViewer.
Datasets
The list of datasets that is being used for Jupyter Notebook demos may be found in data folder.