This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progression of Jupyter notebooks.
The pedagogical approach uses multiple frameworks—including NumPy, Pandas, scikit-learn, TensorFlow, Keras, and Hugging Face—in a single cohesive sequence. Each concept is introduced with minimal explanatory text and runnable code that can be modified and rerun, and inline tasks require immediate application of newly introduced techniques. The curriculum builds skills across data loading, manipulation, visualization, and preprocessing; classical machine learning algorithms; neural network construction and training; computer vision pipelines; and natural language processing tasks including text classification with transformers.
The entire curriculum is delivered as Jupyter notebooks that combine text, code, and visualizations, and can be run interactively in any notebook environment.
