Note: Repository contents are regularly updated with the latest materials.

Machine Learning Theory and Practice 🚀

Welcome to a deep dive into the world of machine learning algorithms and their efficient implementations! This repository provides a well-structured collection of machine learning algorithms designed in Python, leveraging the robust capabilities of the Numpy, Scikit-learn, and Tensorflow libraries.

📢 Call to Collaborators: Our quest for knowledge is ever-evolving! If you're passionate about Machine Learning and wish to contribute, please check out here for guidelines on how to get started.

🌌 Repository Vision

• ANI vs AGI: ANI (Artificial Narrow Intelligence) is the concept of an AI system that can perform one task very well, such as self-driving cars or smart speakers. AGI (Artificial General Intelligence) is the concept of an AI system that can perform any task a human can. There has been a lot of progress in ANI, but AGI is still a long way off. The goal of this repository is to explore the various algorithms that are used to build ANI systems.
• Neural Networks and Brain Simulation: Although modern deep learning has seen advancements in simulating neurons, there are limitations. The artificial neurons we build are overly simplistic compared to their biological counterparts, and our understanding of how the human brain works is still rudimentary. The path to AGI through brain simulation appears to be quite challenging.
• One Learning Algorithm Hypothesis: Based on certain animal experiments, it is suggested that much of intelligence might be due to one or a few learning algorithms - the concept of one learning algorithm hypothesis. Depending on the input data, different parts of the brain can learn to perform various tasks. The challenge lies in discovering these algorithms and implementing them in a computer.
• Flexibility of the Brain: Experiments show that the human brain is highly adaptable, capable of processing a wide range of sensor inputs. Researchers are studying these mechanisms to understand if they can be replicated in AI systems.

📚 Curated Learning Resources

Course/Resource	Provider/Platform
Machine Learning Specialization by DeepLearning.AI	Coursera
Deep Learning Specialization by DeepLearning.AI	Coursera
Mathematics for Machine Learning and Data Science Specialization by DeepLearning.AI	Coursera
Introduction to Deep Learning by MIT	MIT
Dr. Roi Yehoshua on Medium	Medium

💡 Personal Motivation

As an avid learner of computer science and mathematics, the intriguing cross-section of programming and predictive modeling has captivated my attention. This fascination for understanding how machines interpret data to make decisions has led me to embark on this Machine Learning journey. I am eager to unravel these concepts and apply them to solve real-world problems, thereby building upon my solid foundation in programming and mathematical thinking.

🤝 Contributors and Collaborators

Name	GitHub Profile
Izhar Ali	ali-izhar
Saeed Ahmad	saeedahmadicp

📜 License

This repository is licensed under the MIT License.