Welcome to the Machine Learning Tutorial repository! This repository contains code and resources for a comprehensive machine learning tutorial.
This tutorial is designed to provide you with a solid foundation in machine learning concepts and techniques. Whether you're a beginner looking to get started or an experienced practitioner aiming to refresh your knowledge, this tutorial has something for you.
-
Code Examples: Explore practical code examples that cover various machine learning algorithms, including linear regression, logistic regression, decision trees, and more.
-
Datasets: Access sample datasets used in the tutorial for hands-on practice and experimentation.
-
Jupyter Notebooks: Dive into interactive Jupyter notebooks that walk you through key machine learning concepts and their implementation.
This tutorial covers a wide range of machine learning algorithms, including:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Naive Bayes
- Principal Component Analysis (PCA)
- K-Means Clustering
- Maximum Likelihood Estimate (MLE)
- Gradient Boosting
- Neural Networks (Deep Learning)
- Recommender Systems
- And many more... (feel free to contact me to add more ML algorithms)
- Clone this repository to your local machine:
git clone git@github.com:Alexyskoutnev/Machine-Learning-Tutorial.git-
Explore the folders and choose a topic or algorithm you'd like to learn or practice.
-
Follow the instructions provided in the README files within each folder to run code examples or Jupyter notebooks.
-
Experiment with different datasets and parameters to gain hands-on experience.
If you have suggestions, improvements, or additional code examples you'd like to contribute, feel free to open a pull request. We welcome contributions from the community to make this tutorial even better.
This project is licensed under the MIT License - see the LICENSE file for details.