Applied AI in Medicine Certificate

Introduction & Orientation
Orientation to the program, overview of different AI architectures, and PyCharm IDE installation.

Session 1: Writing, Editing, Saving, Retrieving, and Running Simple Programs
Introduction to basic data types, keywords, variables, operators, assignments, multi-statements, comments, Python I/O, expressions, and more.

Session 2: Control Flow, Python Functions, and Algorithms
Overview of control flow including conditions, operators, control statements, and loops. Introduction to widely used Python functions and algorithmic concepts.

Session 3: Functions in Python Programming
Survey of functions in Python including defining a function, calling a function, and passing parameters to a function.

Session 4: Strings and Text Files in Python
Introduction to string, characters, array of characters, manipulation, indexing, string slicing, and related built-in functions, as well as comprehensive instruction on working with files.

Session 5: Native Data Types in Python
Introduction to the basic operation and iteration methods for lists and dictionaries.

Session 6: Common Modules in the Python Standard Libraries
Orientation to widely used modules in the Python standard libraries, as well as important math functions.

Session 7: NumPy
Understand how NumPy offers clarity and ease-of-use over traditional programming languages. Learn how to use NumPy features to support large, multi-dimensional vectors, arrays, and matrices.

Session 8: Matplotlib and Data Visualization
A deep dive into data plotting and visualization features offered by Matplotlib.

Session 9: SciPy
Explore SciPy functionalities such as optimization, linear algebra, statistics, and differential equations.

Session 10: Pandas and Data Analysis
Discover how Pandas allows users to perform data analysis. Learn how to use Pandas features to clean, manipulate, explore, and understand large volumes of data.

Session 11: Heart Disease Risk factor prediction using Logistic Regression and Random Forest Classifiers
Implementation of a machine learning application from scratch including visualizations.

Session 12: Image processing exercise using SVD
Build an image compression algorithm using machine learning libraries.