Learn Machine Learning with Python Code
Master machine learning, deep learning, and neural networks with 50+ free tutorials, interactive demos, and production-ready Python code. From beginner to advanced AI projects.
My Journey: From Zero to Machine Learning Expert
Self-taught ML engineer sharing everything I learned about artificial intelligence and deep learning
Hi, I'm Otman - Machine Learning Engineer 👋
I'm a self-taught machine learning engineer who believes that AI education should be free and accessible. This platform shares everything I've learned about deep learning, neural networks, and Python machine learning through real-world projects and hands-on tutorials.
Starting with zero background in artificial intelligence, I've spent thousands of hours masteringTensorFlow, PyTorch, scikit-learn, and advanced ML algorithms. Every tutorial here comes from solving real problems and building production-ready machine learning models.
My mission is to make machine learning accessible to everyone. Whether you're learning supervised learning, unsupervised learning, computer vision, or natural language processing, you'll find step-by-step guides with complete Python code examples.
"The best way to learn machine learning is by building real projects with real code."
Explore Different Topics
Read detailed ML articles, from coding beginner projects to explaining SOTA papers
Interactive Machine Learning Demos & Open Source Projects
Try live machine learning models, explore production-ready Python code, and fork deep learning projects for your own applications
Frequently Asked Questions About Machine Learning
Common questions about learning machine learning and AI
What is machine learning and how do I get started?
Machine learning is a subset of artificial intelligence that enables computers to learn from data without explicit programming. Start with Python basics, then learn libraries like scikit-learn, TensorFlow, and PyTorch through hands-on projects.
Which Python libraries are essential for machine learning?
Essential Python libraries include NumPy for numerical computing, Pandas for data manipulation, Matplotlib/Seaborn for visualization, scikit-learn for traditional ML, TensorFlow/PyTorch for deep learning, and Jupyter notebooks for development.
How long does it take to learn machine learning?
With consistent practice, you can learn machine learning basics in 3-6 months. Mastering advanced topics like deep learning and neural networks typically takes 1-2 years of dedicated study and hands-on project experience.
What's the difference between machine learning and deep learning?
Machine learning uses algorithms to learn from data, while deep learning is a subset of ML that uses neural networks with multiple layers. Deep learning excels at tasks like image recognition, natural language processing, and complex pattern recognition.
Do I need a computer science degree to learn machine learning?
No, you don't need a CS degree. Many successful ML engineers are self-taught. Focus on learning Python, statistics, linear algebra, and building real projects. Our free tutorials provide a complete learning path from beginner to advanced.
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