Overview

Course Outcome:After taking this course, students will be able to understand and implement machine learning algorithms using Python for regression, binary classification, and multi-class classification with applications to real-world datasets.Course Topics and Approach:This introductory course on machine learning focuses on Supervised Learning, which involves finding functions that fit data and then using the functions to make predictions. Applications include image classification, text sentiment classification, house price prediction. The core of this course involves study of the following algorithms:Linear Regression, Logistic Regression, Neural Networks for regression, binary, and multiclass classificationUnlike many other courses, this course:Has a detailed presentation of the the math underlying the above algorithms including optimization algorithms and back propagation formulasHas a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorizationHas homework questions (programming and theory) and solutions that allow learners to get practice with the course materialThe course codes are then used to address case studies involving real-world data including image classification, text message spam/no spam classification, and house price prediction. Course Audience:This course is designed for:Scientists, engineers, and programmers and others interested in machine learning/data scienceNo prior experience with machine learning is neededStudents should have knowledge ofBasic linear algebra (vectors, matrix multiplication, transpose)Multivariable calculus (to follow details of optimization and backpropagation formulas)Python 3 programmingStudents should have a Python installation, such as the Anaconda platform, on their machine with the ability to run programs in the command window and in Jupyter NotebooksTeaching Style and Resources:Course includes many examples with plots used to help students get a better understanding of the materialCourse has 50+ exercises with solutions (theoretical, Jupyter Notebook, and programming) to allow students to gain additional practiceAll resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.

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