#Python Programming

Explore insights into developing essential software engineering skills with an emphasis on Python and generative AI. Updated monthly, this project explores key competencies in areas like data structures, algorithms, Linux, version control, database management, backend development, system design, frontend basics, and specialized fields such as machine learning and NLP. Designed for individuals aiming to enhance expertise in adjacent technologies methodically, from Python data tools to advanced AI techniques.

This roadmap serves as a detailed guide for aspiring Full Stack ML engineers, highlighting essential skills such as Python programming, data analysis using libraries like NumPy and Pandas, and data visualization with Matplotlib and Seaborn. It covers statistical methods, practical machine learning applications using Scikit-Learn, and in-depth exploration of deep learning frameworks like TensorFlow and PyTorch. The roadmap also addresses natural language processing and computer vision with OpenCV, and includes insights into MLOps with AWS, as well as essential tools like Git and GitHub for collaboration and version control, providing a solid foundation in machine learning.

SymbolicAI offers a robust framework combining machine learning and neuro-symbolic methodologies using the Symbolic API. It facilitates solving complex problems by dividing them into simpler tasks, integrating classical and differentiable programming seamlessly in Python. This framework aids in effective large language model utilization and enhances AI development. Comprehensive tutorials and documentation are available to assist developers in application integration.