Machine Learning & Artificial Intelligence Principles Overview
From robotics, speech recognition, and analytics, to finance and social network analysis, machine learning comprises one of the most useful scientific toolsets of our age. This course provides an overview of the core principles of machine learning using a hands-on, project-based curriculum. There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data.
This is a refreshed version of the original Machine Learning & AI Principles professional development on-site course with materials added using a popular Python neural network package, Keras. As ML and AI are both rapidly developing fields, we've added content on 2 popular neural network architectures currently used in AI: Convolutional neural networks (CNNs) and Long-Short Term Memory Networks (LSTMs).
Who is this course for?
This is designed for people working in any number of data-intensive fields, including consulting, finance, IT, healthcare, and logistics, as well as for recent college graduates and entrepreneurs interested or specializing in those fields.
Considering the data science immersive bootcamp?
Part-Time Alumni can apply the amount of tuition paid for one part-time professional development course towards enrollment in an upcoming bootcamp upon admittance.
Firm knowledge of the Python programming environment. There will not be any introductory Python material in this course. Students should not take this course if they are not comfortable coding in Python.
Basic understanding of vector and matrix algebra (how to add and multiply vectors/matrices), as well as basic understanding of the notion of a mathematical function (e.g., understanding what f(x)=x^2 or f(x) = sin(x) means).
Basic calculus and linear algebra is helpful but not required (e.g., how to take derivatives, what a linear system of equations is, etc.). A quick refresher on these topics will be provided. (Note: Knowledge of statistics is not required for this course.)
Upon completion of the Machine Learning course, students have: