Implementation of different ML Algorithms from scratch, written in Python 3.x

Related tags

Machine Learning python machine-learning ai sklearn machine-learning-algorithms regression artificial-intelligence classification
Overview

Machine Learning Algorithms

Implementation of different machine learning algorithms written in Python.

Contents

Installation of libraries

pip install -r requirements.txt

NOTE: scikit-learn module is used only for accessing the datasets.

Usage

python run_{algorithmToRun}.py

NOTE: All scripts have additional command arguments that can be given by the user.

python run_{algorithmToRun}.py --help

Summary

This project was initially started to help understand the math and intuition behind different ML algorithms, and why they work or don't work, for a given dataset. I started it with just implementing different versions of gradient descent for Linear Regression. I also wanted to visualize the training process, to get a better intuition of what exactly happens during the training process. Over the course of time, more algorithms and visualizations have been added.

Algorithms and Visualizations

Gradient Descent 2D

Gradient Descent 3D

Linear Regression

Linear Regression for a non-linear dataset

This was achieved by adding polynomial features.

Logistic Regression

Logistic Regression for a non-linear dataset

This was achieved by adding polynomial features.

K Nearest Neighbors 2D

K Nearest Neighbors 3D

KMeans 2D

KMeans 3D

Links

Link to first Reddit post

Link to second Reddit post

Citations

Sentdex: ML from scratch

Coursera Andrew NG: Machine Learning

Todos

  • SVM classification, gaussian kernel
  • Mean Shift
  • PCA
  • DecisionTree
  • Neural Network
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Comments
  • Bump numpy from 1.19.2 to 1.22.0

    Bump numpy from 1.19.2 to 1.22.0

    Bumps numpy from 1.19.2 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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Owner
Gautam J
18 | AI | ML | DL
Gautam J
GitHub
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