📄️ Python for AI - Complete
Mastering Python applied to artificial intelligence, from the useful syntax to the scientific libraries. Duration: 8 hours, with applied projects, a 40-question exam and a verifiable certificate.
📄️ 1. The useful syntax
Module 1 of the premium course Python for AI — Complete: the subset of Python used daily in data work — variables, types, f-strings, conditions, loops and slicing — without the language's dark corners.
📄️ 2. Data structures
Module 2 of the premium course Python for AI — Complete: choosing between list, dictionary, tuple and set, and writing readable comprehensions — the thinking pattern that prepares you for pandas.
📄️ 3. Functions and modules
Module 3 of the premium course Python for AI — Complete: writing clean functions — keyword arguments, default values, docstrings — organizing a project into modules and understanding imports.
📄️ 4. NumPy and vectorization
Module 4 of the premium course Python for AI — Complete: the ndarray, the vectorization that replaces loops, broadcasting, boolean masks and axis-wise aggregations.
📄️ 5. pandas: DataFrame and indexing
Module 5 of the premium course Python for AI — Complete: reading data, understanding Series and DataFrame, selecting with loc and iloc, filtering by condition and inspecting an unknown dataset.
📄️ 6. Data cleaning
Module 6 of the premium course Python for AI — Complete: diagnosing then treating missing values, duplicates, wrong types and entry inconsistencies — with the business trade-offs each choice implies.
📄️ 7. Joins and grouping
Module 7 of the premium course Python for AI — Complete: merge and its four join types, groupby and the split-apply-combine pattern, pivot_table — and the checks that prevent silently wrong results.
📄️ 8. Visualization
Module 8 of the premium course Python for AI — Complete: Matplotlib's figure/axes interface, Seaborn's statistical charts, choosing the right chart per question, and the rules of visual honesty.
📄️ 9. Environments and dependencies
Module 9 of the premium course Python for AI — Complete: why isolate every project, creating and activating a virtual environment, freezing dependencies in requirements.txt and making a project reproducible.
📄️ 10. Jupyter notebooks
Module 10 of the premium course Python for AI — Complete: what notebooks do better than anything, the hidden-state and out-of-order execution trap, and the practices that keep a notebook reliable — up to migrating logic into modules.
📄️ 11. Recap and exam
Synthesis of the ten modules of the premium course Python for AI — Complete, then the certification exam: 40 corrected questions, 70% passing threshold, verifiable online certificate.