Welcome to the "Introduction to Machine Learning" repository! This repository is designed to provide you with hands-on practice materials for learning the fundamental concepts of machine learning, including Convolutional Neural Networks (CNNs), Logistic Regression, TensorFlow, and Python programming.
Machine learning is a rapidly evolving field that empowers computers to learn from data and make intelligent decisions. This repository aims to provide a structured learning path to help you grasp the basics of machine learning, followed by a deep dive into Convolutional Neural Networks (CNNs), Logistic Regression, and practical implementation using TensorFlow and Python.
The repository contains the following notebooks:
- Introduction to Machine Learning: This notebook covers the fundamental concepts of machine learning, including supervised learning, unsupervised learning, and key terminology.
- Convolutional Neural Networks (CNNs): Dive into the world of image classification and computer vision with this notebook, where you'll learn how to build and train CNNs to recognize objects in images.
- Logistic Regression: Understand the basics of logistic regression, a fundamental algorithm for binary classification tasks, and implement it from scratch.
- TensorFlow Basics: Get hands-on experience with TensorFlow, a popular open-source machine learning framework. Learn how to define and train simple models using TensorFlow.
- Python for Machine Learning: A primer on using Python for machine learning tasks. Covering essential libraries and techniques for data manipulation, visualization, and model evaluation.
Follow these steps to get started with the practice notebooks:
Make sure you have the following prerequisites installed:
- Python (version 3.10)
- Jupyter Notebook
- TensorFlow (version 2.X)
- Required Python libraries: numpy, matplotlib
- Clone this repository to your local machine using:
git clone https://github.com/your-username/introduction-to-machine-learning.git
cd introduction-to-machine-learning- Install the required Python libraries:
pip install numpy matplotlib tensorflow
- Launch Jupyter Notebook:
jupyter notebook - Open the desired notebook from the list mentioned above.
- Follow the instructions within each notebook to learn and practice machine learning concepts.
Contributions are welcome and encouraged! If you find any issues or want to enhance the existing content, feel free to submit a pull request. Please ensure that your contributions align with the goals of this repository.
This repository is licensed under the MIT License, which means you're free to use, modify, and distribute the code as you see fit.
Happy learning!
Disclaimer: This repository is created for educational purposes. The notebooks and code examples are intended to facilitate learning and experimentation in machine learning concepts.