Essentials of Python for Machine Learning and Artificial Intelligence

This course provides an introduction to the Python programming language essential for data manipulation, statistical analysis, and modeling techniques required for machine learning and artificial intelligence.  In this course we will explore the wonderfully concise and expressive use of Python’s advanced module features and apply it in probability, statistical testing, signal processing, financial forecasting, and various other applications. This course covers mathematical operations with array data structures, optimization, Probability Density Function, interpolation, Fast Fourier Transform, basic signal processing and other high performance benefits using the core scientific packages NumPy, Scipy, SkLearn/Scikit learn and Matplotlib. Students will gain a deep understanding and problem solving experience with these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. The course will teach practical aspects of python for data wrangling needed for ML and AI applications so that the students will be able to apply lessons to solve problems using machine learning in their own careers and fields. The course uses examples to guide you through foundational concepts, often employing live algorithms to facilitate visual understanding. Pseudocode will be provided for most of the algorithms covered. You are encouraged to use the pseudocode as a reference to create your own programs in Python. The class has in-class quizzes to gauge learning and group activities including discussion. Homework assignments involving programming in Python are designed for in-depth practice.