Applied Machine Learning & Deep Learning Tutorials and Resources

Welcome to the Applied Machine Learning repository!

Summary

Machine learning is revolutionizing various industries, and this repository aims to empower learners with the knowledge and practical skills needed to harness the power of machine learning effectively. Whether you're just starting your machine learning journey or seeking to enhance your existing expertise, you'll find valuable content tailored to your needs.

--> Applied machine learning is a subset of artificial intelligence that focuses on using algorithms and statistical models to enable computer systems to learn from data and make predictions or decisions without explicit programming.

In other words, it's about building and deploying practical machine-learning solutions to solve real-world problems.

Key Features

• Tutorials: Step-by-step tutorials covering a wide range of machine learning techniques and projects. Each tutorial includes hands-on coding examples and clear explanations, making it easy to follow along.

• Notes: A collection of useful notes, cheat sheets, and quick references for essential machine learning concepts and popular libraries.

• Resources: Additional reading materials, research papers, and links to external resources to deepen your understanding of applied machine learning.

• Most Important Concepts: A section dedicated to explaining key concepts such as supervised learning, unsupervised learning, overfitting, evaluation metrics, cross-validation, and hyperparameter tuning.

Resources

Books:

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Python for Data Analysis
• Mathematics for Machine Learning and Deep Learning
• Deep Learning
• Python for Probability, Statistics, and Machine Learning
• Pattern Recognition and Machine Learning

Courses:

• Machine Learning - Andrew NG
• Deep Learning Specialization
• MLOps | Machine Learning Operations Specialization
• MLOps

Online Platforms and Tutorials:

• Kaggle : Kaggle is a data science and machine learning community that hosts competitions and provides datasets and kernels (code notebooks) for learning and practicing machine learning.

• Towards Data Science : A popular medium publication with numerous tutorials, case studies, and practical examples in data science and machine learning.

• Scikit-Learn Documentation: The official documentation of Scikit-Learn, a popular Python library for machine learning, contains comprehensive guides, examples, and API references.

• TensorFlow and Keras Documentation: The official documentation of TensorFlow and Keras provides guides, tutorials, and examples for deep learning applications.

How to Contribute

We believe in community-driven learning, and contributions from fellow data enthusiasts are highly encouraged. Whether you want to share your own tutorial, add a helpful resource, or improve existing content, please check the CONTRIBUTING.md file for guidelines on how to contribute.

Disclaimer: Include any necessary disclaimers or copyright notices here if applicable.

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