AI & Data Science
Practice Challenges

Master the core concepts of machine learning with hands-on exercises covering supervised and unsupervised learning algorithms.

Build and train neural networks using TensorFlow. Learn about CNNs, RNNs, and advanced architectures for real-world applications.

Create compelling data visualizations using matplotlib, seaborn, and plotly. Learn to tell stories with data through effective charts and graphs.

Master complex SQL queries, window functions, and database optimization techniques essential for data science workflows.

Learn to identify and mitigate bias in AI systems. Understand ethical considerations and fairness metrics in machine learning models.

Introduction to reinforcement learning concepts. Implement Q-learning and policy gradient methods for game-playing agents.