Welcome to the Machine Learning Course Repository! This repository contains all the resources, assignments, and project files for the machine learning course.
The repository is organized into the following directories:
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Dataset: This folder contains all the datasets used throughout the course for assignments, projects, and practice exercises.
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Homework: This folder includes all the homework assignments. Each homework is in its own subfolder, with relevant instructions and required files.
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Lecture: This folder contains the lecture materials organized by topic. Each subfolder corresponds to a specific topic or module in the course. Click on each topic to navigate directly to its files:
- 00_Image: Contains images that are referenced and used within the Jupyter notebooks and lecture notes.
- 01_Mathematical_Foundations: Fundamental mathematical concepts needed for machine learning.
- 02_Algebra_with_NumPy: Introduction to algebra using NumPy.
- 03_Introduction_to_MachineLearning: Basics of machine learning and its applications.
- 04_Polynomial_Regression: Understanding and applying polynomial regression.
- 05_Logistic_Regression: Logistic regression for binary classification.
- 06_Data_Preprocessing: Techniques and tools for cleaning and preprocessing data.
- 07_Regularization
- 0_Gradient_Descent: Detailed study of the gradient descent optimization algorithm.
- 0_Manage_Overfitting: Strategies to manage and prevent overfitting in models.
- 0_K-Nearest_Neighbors_KNN: K-Nearest Neighbors algorithm for classification.
- 0_Naive_Bayes_Classifier: Introduction to the Naive Bayes classification technique.
- 0_Decision_Tree: Concepts of decision trees and their implementation.
- 0_Support_Vector_Machines_SVM: Support Vector Machines for classification and regression tasks.
- 0_Dimensionality_Reduction_PCA: Dimensionality reduction using Principal Component Analysis (PCA).
- 0_K-Means_Clustering: K-Means clustering for unsupervised learning.
- 0_Hierarchical_Clustering: Techniques and methods for hierarchical clustering.
- 0_DBSCAN: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for clustering.
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Project: This folder is dedicated to the course projects. Each project will have its own subfolder containing the project description, code, and any related files.
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Clone the Repository: Start by cloning this repository to your local machine using the following command:
git clone https://github.com/yourusername/machine-learning-course.git
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Navigate the Directories: Explore the different folders to find the materials you need. Each folder is organized by topic or module.
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Run the Code: Most of the code provided is in Python. Make sure you have the required dependencies installed. You can install them using:
pip install -r requirements.txt
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Submit Assignments: For homework and projects, follow the submission guidelines provided in each assignment's folder.
This repository is licensed under the MIT License. See the LICENSE file for more information.
If you would like to contribute to this repository, feel free to fork it and create a pull request. Contributions such as improving documentation, adding comments to the code, or providing additional resources are always welcome.
If you have any questions or suggestions, feel free to open an issue or contact me directly at Tlegeram