This curriculum is designed for students and aspiring AI practitioners who want to gain hands-on experience in building AI systems and completing real-world projects. It provides a structured learning path, starting from the fundamentals of Machine Learning (ML) and progressing toward advanced Deep Learning techniques. By following this curriculum, you'll not only understand core AI concepts but also develop practical expertise in training and deploying models, working with datasets, and tackling AI challenges. By the end of this roadmap, you’ll be well-prepared to engage with AI at a deeper level—whether it’s through research projects, open-source contributions, or solving real-world problems.
Pre-requisites:
The following curriculum assumes a basic knowledge of the Python programming language, some of its libraries, familiarity with Jupyter Notebooks and the Kaggle platform. If you are new to any of these, you can refer to the links and tutorials given below to get started:
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Basic Python Programming: Link
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Jupyter Notebook: Link
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Python Libraries
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Kaggle account:
Kaggle is a leading platform for machine learning and data science. It offers real-world datasets, community-driven notebooks, and competitions that help users effectively apply ML techniques. The platform also provides a collaborative environment where users can explore different approaches to solving problems, understand industry-standard workflows, and improve their coding and analytical skills. To fully engage with this curriculum, you'll need to explore and modify Kaggle notebooks as part of your learning process.
Useful links: