This course introduces data structures and algorithms, focusing on practical implementation in Python.

An introductory course to familiarize the rich library of Seaborn to visualize variety of data types.

Learn how to clean, transform, and analyze messy datasets using Python, focusing on data wrangling techniques.

This course provides hands-on experience dealing with imbalanced data in machine learning, which is critical for machine learning algorithms.

Stop grinding through endless practice questions, and start breaking down real-world problems. Tackle your interview in Python with confidence.

Learn how to implement object detection algorithms, focusing on the YOLO (You Only Look Once) framework.

This course aims to equip you with the techniques to train the DL models, convert them to the TF Lite format, and deploy these models into your Android apps.

Discover how to build deep learning models using JAX and Flax, leveraging modern Python libraries for high-performance machine learning.

This course is ideal for anyone who wants to learn PyTorch, starting from PyTorch basics and expanding to use PyTorch for deep learning.

Good data is central to effective AI applications. This course teaches the basics of data for AI, covering what data is needed, how to extract data from existing databases and basic data skills including setup of a Python notebook environment, basic data exploration and simple data visualizations.

How do populations grow? How do viruses spread? What is the trajectory of a glider? Introduce yourself to the modelling cycle which includes: analyzing a problem, formulating it as a mathematical model, calculating solutions and validating your results.

This course will teach you how to use proven object-oriented design patterns to significantly enhance the stability, testability, and maintainability of your Python development while decreasing your development time.