Practice Notebooks¶
Every topic below ships a practice notebook of graded exercises and a matching solutions notebook with worked answers and explanations.
Open a notebook in Colab to run it in the browser with no local setup — nothing to install, and a free GPU is available under Runtime → Change runtime type. Use Read rendered if you only want to read the saved output.
How to get the most out of these
Work through the practice notebook first and only open the solutions once you have attempted every cell. The exercises are ordered so each one builds on the previous.
Course 1 — Programming Refresher¶
Variables Datatypes¶
108 cells.
Control Flow Functions¶
108 cells.
Data Structures¶
108 cells.
OOP Modules¶
110 cells.
File I/O Exceptions¶
108 cells.
Course 2 — Applied Data Science with Python¶
Intro Data Science¶
104 cells.
Python Essentials¶
104 cells.
Numpy¶
103 cells.
Linear Algebra¶
108 cells.
Statistics Fundamentals¶
108 cells.
Probability Distributions¶
108 cells.
Advanced Statistics¶
108 cells.
Pandas¶
155 cells.
Data Wrangling¶
108 cells.
Matplotlib¶
103 cells.
Data Visualization¶
108 cells.
Seaborn¶
134 cells.
Regex JSON APIs¶
108 cells.
Course 3 — Machine Learning¶
EDA Feature Engineering¶
108 cells.
Clustering¶
108 cells.
Classification¶
108 cells.
Imbalanced Data¶
108 cells.
Model Evaluation¶
108 cells.
Course 4 — Deep Learning with Keras & TensorFlow¶
Neural Network Basics¶
108 cells.
Keras Tensorflow¶
108 cells.
Preprocessing Imbalance¶
108 cells.
Model Evaluation DL¶
108 cells.
Course 5 — Generative AI, Prompt Engineering & ChatGPT¶
Prompt Engineering¶
108 cells.
Chatgpt Applications¶
108 cells.
Genai Optimization¶
108 cells.
Course 6 — Advanced Generative AI¶
RAG Architectures¶
102 cells.
Vector Databases Chroma¶
102 cells.
Multimodal Generative Models¶
102 cells.