This repository contains machine learning classes designed to provide students with hands-on experience in various machine learning algorithms. The classes are divided into categories based on their complexity and are suitable for students of all levels.
- Introduction to Python Programming Part One
- Introduction to Python Programming Part Two
- Linear Regression
- Gradient_Descent
- Logistic Regression Part One
- Logistic Regression Part Two
- Neural Network
- Backward Propagation
- Convolutional Neural Network
Before taking these classes, students should have a basic understanding of the following topics:
- Python programming
- understanding of machine learnin concepts
- Probability and statistics
To set up the environment for these classes, you can follow these steps:
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Install [Python] (https://www.python.org/downloads/) on your system if you haven't already( or use google colab). You can download Python from the official website
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Install the required Python libraries. You can do this by running the following command in your terminal:
pip install numpy pandas sklearn matplotlib torch
- Install the Jupyter notebook if you haven't already. You can do this by running the following command in your terminal:
pip install jupyter- To install Anaconda, you can use the following commands. Replace 'Anaconda3-2021.05-Linux-x86_64.sh' with the actual file name.
wget https://repo.anaconda.com/archive/Anaconda3-2021.05-Linux-x86_64.sh
bash Anaconda3-2021.05-Linux-x86_64.sh- After installation, you can run the following command to launch Jupyter Notebook:
jupyter notebook- Open the Jupyter Notebook and navigate to the directory where you have cloned the files and start you journey!