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Predictive Modeling and Analytics
by Dan Zhang- 3.6
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Approx. 11 hours to complete
Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business....
Remote Sensing Image Acquisition, Analysis and Applications
by John Richards- 4.5
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Approx. 23 hours to complete
Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. That requires the use of the mathematics of vector and matrix algebra, and statistics....
Introduction to Quantum Science & Technology
by Mahdi Hosseini- 0.0
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16 Weeks
Learn about fundamental concepts and engineering challenges of quantum technologies. Emerging quantum systems are disruptive technologies redefining computing and communication. Teaching quantum physics to engineers and educating scientists on engineering solutions are critical to address fundamental and engineering challenges of the quantum technologies. Identify fundamental differences between quantum mechanics and classical mechanics....
$2250
Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD
by Greg Mayer- 0.0
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3 Weeks
This course takes you through roughly five weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. In the first part of this course you will explore methods to compute an approximate solution to an inconsistent system of equations that have no solutions....
$199
Guided Tour of Machine Learning in Finance
by Igor Halperin- 3.8
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Approx. 24 hours to complete
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds...
Visual Perception for Self-Driving Cars
by Steven Waslander- 4.7
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Approx. 31 hours to complete
0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses)....
Algorithmic Thinking (Part 1)
by Luay Nakhleh , Scott Rixner , Joe Warren- 4.7
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Approx. 12 hours to complete
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. Module 1 - Core Materials...
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Supervised Learning: Regression
by Mark J Grover , Miguel Maldonado- 4.8
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Approx. 11 hours to complete
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques....
Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
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Approx. 34 hours to complete
2) Basic linear algebra and probability....
Unsupervised Learning
by Mark J Grover , Miguel Maldonado- 4.9
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Approx. 9 hours to complete
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data....
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