Introduction to Machine Learning
What you'll learn
- Introduction to Machine Learning: math, algorithms, and Python coding for Linear and Logistic Regression and Neural Networks
- Linear Algebra (matrix multiplication), Multivariable Calculus (gradients), Python programming
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 classification
Unlike many other courses, this course:
Has a detailed presentation of the the math underlying the above algorithms including optimization algorithms and back propagation formulas
Has a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorization
Has homework questions (programming and theory) and solutions that allow learners to get practice with the course material
The 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.
This course is designed for:
Scientists, engineers, and programmers and others interested in machine learning/data science
No prior experience with machine learning is needed
Students should have knowledge of
Basic linear algebra (vectors, matrix multiplication, transpose)
Multivariable calculus (to follow details of optimization and backpropagation formulas)
Python 3 programming
Students 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 Notebooks
Teaching Style and Resources:
Course includes many examples with plots used to help students get a better understanding of the material
Course has 50+ exercises with solutions (theoretical, Jupyter Notebook, and programming) to allow students to gain additional practice
All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.
Who this course is for:
- Students without any previous experience with machine learning
- Students with previous experience who would like to revisit the math, algorithms and coding for machine learning in detail
PhD in Applied Math from Massachusetts Institute of Technology
10 years experience doing research in applied math and teaching undergraduate and graduate courses at New York University, Oregon State University, and the University of British Columbia.
17 years experience in financial risk management space working at a software start-up, a financial information services company, and a large international bank.
Currently, consulting on machine learning projects.