themrityunjaypathak / machinelearningmathematics

Mathematics required for Machine Learning

Machine Learning Mathematics

• This repository contains Jupyter notebooks covering essential mathematical concepts used in machine learning, including linear algebra, probability, statistics, and calculus.

• Each notebook includes detailed explanations, mathematical theory, and practical code examples to help you understand these fundamental concepts in the context of machine learning.

Table of Contents

• Introduction
• Notebook List
• Getting Started
• Contributions
• License
• Acknowledgments

Introduction

• Math is the core concept in machine learning which is used to express the idea within the machine learning model.

• Mathematics forms the backbone of Machine Learning, So Understanding the Mathematical concepts is not just an advantage but an absolute necessity.

• These are some core mathematical concepts that power Machine Learning :

  • Linear Algebra : Matrices, vectors, and their transformations are fundamental to understanding how data is represented and manipulated in ML model.

  • Geometry : Understanding geometry behind the ML models helps in building classification, clustering and reccomender systems models.

  • Statistics : Statistics encompasses methods for summarizing and drawing inferences from data, making it a key component in the field of machine learning.

  • Probability : It provides a mathematical framework for modeling uncertain events and is fundamental for understanding the uncertainties present in machine learning algorithms.

  • Calculus : Understanding derivatives and integrals helps in optimizing models through techniques like gradient descent.

Notebook List

Here is a list of Jupyter Notebooks included in this repository :

Linear Algebra

• Introduction to Matrices
• Matrix Operation using Python

Geometry

• Distance Metrics
• Similarity Measures
• Box Cox Transformation

Statistics

• Basics of Statistics
• Descriptive Statistics with Python
• Bias and Variance
• Covariance and Correlation
• Normal Distribution
• Hypothesis Testing

Probability

• Introduction to Probability
• Probability Distribution

Calculus

• Introduction to Calculus

Getting Started

1. Clone this repository to your local machine :

   git clone https://github.com/TheMrityunjayPathak/MachineLearningMathematics.git

2. Install Jupyter Notebook or JupyterLab :

   pip install jupyterlab

3. Launch Jupyter :

   jupyter notebook

4. Open the desired notebook from the repository in your Jupyter environment and start learning!

Contributions

• Contributions are Welcome!

• If you find any issues, errors, or want to add more notebooks covering other mathematical concepts relevant to machine learning, feel free to open an issue or submit a pull request.

License

• This repository is licensed under the MIT License. Feel free to use the code and notebooks for your own learning and projects.

Acknowledgements

• The notebooks in this repository are created based on various online tutorials, courses, and blogs.

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