What you'll learn
✓ Build and train real-world Machine Learning models using Python, Scikit-learn, TensorFlow, and PyTorch from scratch.
✓ Apply ML algorithms to real use cases in finance, healthcare, e-commerce, NLP, computer vision, and time series forecasting.
✓ Deploy ML models using FastAPI, Docker, and Streamlit, and manage lifecycles with MLflow, CI/CD, and Kubernetes.
✓ Prepare for AI/ML job interviews with hands-on projects, portfolio-building guidance, and advanced MLOps practices.

Requirements
● Basic understanding of Python programming (variables, loops, functions).
● Familiarity with high school-level math (algebra, basic statistics).
● Curiosity and commitment to learn Machine Learning through hands-on projects.

Description
It’s a multi-level ML mastery experience engineered for 2025 and beyond — and it’s designed and taught by Darwish, a Software backend engineer turned AI architect who’s helped thousands transition into cutting-edge tech careers.

You won’t just learn theory — you’ll understand how real Machine Learning workflows operate in production environments. Throughout the course, we focus on practical problem-solving, industry best practices, and building an engineer mindset that bridges the gap between development and AI. You’ll explore data preparation, feature engineering, model selection, evaluation strategies, optimization techniques, and deployment pipelines used in real-world scenarios. Each section is designed to progressively increase your confidence, helping you move from experimentation to building scalable AI solutions that can run in cloud and enterprise environments.

Whether you're starting from scratch or aiming to become an AI Engineer, ML Engineer, or ML Architect, this course is your all-in-one interactive, project-driven, deployment-ready roadmap.

I created this course based on my own transition from .NET and enterprise systems into the world of AI. I know exactly what it feels like to start from zero — and I’ve packed everything I wish I had when I began: step-by-step Jupyter notebooks, high-energy walkthroughs, real-world projects, and the tools used by today’s top AI teams.