farzadasgari / machinepy

A comprehensive Machine Learning course, guiding beginners to build and train machine learning models. It includes Jupyter Notebooks, exercises, and resources for a structured learning experience. (Work in Progress!)

MachinePy

Machine Learning Course: A Comprehensive Guide (Work in Progress!)

This repository contains the resources for a comprehensive Machine Learning course designed to guide you from beginner to a confident practitioner. Whether you're new to programming or have some experience, this course will equip you with the knowledge and skills to build and train machine learning models.

What You'll Learn:

• Machine Learning Fundamentals: Grasp the core concepts and principles of machine learning, including supervised, unsupervised, and reinforcement learning.
• Data Preparation and Exploration: Learn essential techniques for data cleaning, manipulation, visualization, and feature engineering to prepare your data for machine learning algorithms.
• Common Machine Learning Algorithms: Explore popular algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN).
• Model Selection and Evaluation: Understand how to choose the right algorithm for your problem and effectively evaluate the performance of your models.
• Model Tuning and Optimization: Learn techniques to fine-tune hyperparameters and optimize your models for better performance.
• Deep Learning Fundamentals (Optional): Get introduced to the exciting world of Deep Learning architectures like Neural Networks and Convolutional Neural Networks (CNNs).
• Real-World Applications: Explore the practical applications of machine learning in various domains, including computer vision, natural language processing, and recommendation systems.

Course Structure:

• Jupyter Notebooks: The core of the course will be a series of Jupyter Notebooks that guide you through each concept step-by-step with code examples and explanations.
• Exercises and Projects: Each section will include exercises and mini-projects to solidify your understanding and provide hands-on practice.
• Resources: A curated list of additional resources, such as articles, tutorials, and books will be provided for further exploration.

Getting Started:

1. Clone this Repository: Use git clone https://github.com/farzadasgari/machinepy.git to clone this repository to your local machine.
2. Set Up Your Environment: Install Python, Jupyter Notebook, and any necessary libraries mentioned in the course materials.
3. Follow the Jupyter Notebooks: Start with the introductory notebooks and work your way through the course content at your own pace.

Disclaimer:

This course is under development, and new content will be added regularly. Feel free to contribute to the project by submitting pull requests or opening issues if you find any errors or have suggestions for improvement.

License:

This repository is licensed under the MIT License (see LICENSE file for details).

Let's embark on this exciting journey into the world of Machine Learning!

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Farzad Asgari

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