This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss … [Read more...] about Statistical Learning for Engineering Part 2
Probability and Statistics
Engineering Probability and Statistics Part 1
Welcome to Engineering Probability and Statistics Part 1. Throughout your time in this course, you will be given opportunities to check your understanding of course material, as well as engage in quizzes to reflect on all the concepts you have explored within each module. By the end of this part 1 course on engineering probability and statistics, you will have a foundational … [Read more...] about Engineering Probability and Statistics Part 1
Basic Principles of Geostatistical Geospatial Modeling
Ready to harness the power of geostatistics for your data? In this Geospatial Specialization Course #1: Basic Principles of Geostatistical Geospatial Modeling course, you’ll learn how to identify key variables, address outliers and missing data, and apply univariate and bivariate analyses—all within the versatile R programming environment. You’ll quickly master correlation and … [Read more...] about Basic Principles of Geostatistical Geospatial Modeling
Foundations of Probability and Random Variables
The course "Foundations of Probability and Random Variables" introduces fundamental concepts in probability and random variables, essential for understanding computational methods in computer science and data science. Through five comprehensive modules, learners will explore combinatorial analysis, probability, conditional probability, and both discrete and continuous random … [Read more...] about Foundations of Probability and Random Variables
Linear Regression
This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless. This course is part of the Performance Based Admission courses for the Data Science … [Read more...] about Linear Regression
