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https://github.com/akkefa/ml-notes
Notes for Mathematics for Machine learning and Data Science.
https://github.com/akkefa/ml-notes
book computer-science data-science linear-algebra mathematics notes probability statistics topics
Last synced: about 1 month ago
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Notes for Mathematics for Machine learning and Data Science.
- Host: GitHub
- URL: https://github.com/akkefa/ml-notes
- Owner: akkefa
- License: apache-2.0
- Created: 2021-10-13T09:01:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-22T06:36:12.000Z (3 months ago)
- Last Synced: 2024-11-06T03:47:29.685Z (3 months ago)
- Topics: book, computer-science, data-science, linear-algebra, mathematics, notes, probability, statistics, topics
- Homepage: https://ml-notes.akkefa.com
- Size: 3.53 MB
- Stars: 6
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Machine Learning Notes by Ikram Ali
[![Documentation Status][rtd-badge]][rtd-link]
[rtd-badge]: https://readthedocs.org/projects/ml-math/badge/?version=latest
[rtd-link]: https://ml-notes.akkefa.com/en/latest/
[ml-notes.akkefa.com](ml-notes.akkefa.com)
Welcome! I am **Ikram Ali**, an expert in establishing and leading machine learning engineering and data science teams. My current focus areas are **Natural Language Processing (NLP)** and **MLOps**, where I am dedicated to advancing the frontiers of these technologies.
## Connect with Me
- [LinkedIn](https://www.linkedin.com/in/akkefa/)
- [GitHub](https://www.github.com/akkefa)
## Docs build
```bash
sphinx-autobuild docs docs/_build/html
```
## About Me
My career is dedicated to mastering the diverse skills necessary to spearhead data science projects. This includes expertise in Research, Data Engineering, Machine Learning Engineering, and comprehensive Project Management methodologies like Agile and Product Management. This multifaceted skill set enables me to lead cross-functional teams effectively and navigate the journey of a model from initial concept to full-scale production deployment.
## Topics
### Probability
- What is Probability
- Bayes Theorem
- Random Variables
- Discrete Distributions
- Continuous Distributions
- Joint Distributions
- Covariance and Correlation
- Estimators and Sampling Distributions
- Moment Generating Functions
- Maximum Likelihood Estimation
- Confidence Interval
- Hypothesis Testing
### Calculus
- Introduction
- Derivatives and Partial Derivatives
### Algebra
- Algebra Introduction
### Machine Learning
- What is Machine Learning?
- Logistic Regression
- Non-Parametric Models
- Decision Trees
- Principal Component Analysis (PCA)
- Clustering
- Matrix Factorization
### Deep Learning
- What is Deep Learning
- Vectors, Matrices, and Tensors
- Loss Functions
- Evaluation Metrics
### Mathematics
- Linear Algebra
- Statistics
### Algorithms
- Sorting Algorithms
- Graph Data Structures
- Tree Data Structures
- Shortest Path Algorithms
- Greedy Algorithms
### Graph Theory
- What is Graph Theory
- Graph Neural Networks
- Graph Equations
### Learn PyTorch
- PyTorch Fundamentals
- PyTorch Workflow
- PyTorch Neural Network Classification
- Basic Neural Network
### Recommendation Systems
- Recommendation Systems
- Matrix Factorization
### Practice
- Probability Solutions
- R Solutions