"Trees, SVM and Unsupervised Learning" offers working professionals a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, participants will learn to build powerful predictive models and understand the advantages and disadvantages of each technique. This course covers practical applications, including binary classification and K > 2 classes, and provides valuable experience in generating data representations through PCA and clustering. Participants will gain insights into when and how to apply these techniques in different scenarios. This course is a valuable asset for individuals looking to upskill or transition into the field of data science.
Participants will also have the opportunity to earn academic credit for their work by taking this course as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The program is interdisciplinary, bringing together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. No application process is required, making it ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
Certificate Available ✔
Get Started / More Info
Explore "Trees, SVM and Unsupervised Learning" through modules covering support vector machines, neural networks, and decision trees, providing hands-on experience in building predictive models and data representation techniques.
Module 1: This module introduces the course, provides support resources, and encourages participants to introduce themselves. Participants will also learn how to earn academic credit for the course.
Module 2: Delve into support vector machines through in-depth instruction, practical hands-on experience, assignments, and practice labs, enabling participants to apply SVMs for binary classification and K > 2 classes.
Module 3: Gain insights into neural networks, their strengths and weaknesses compared to other machine learning algorithms, and their application to unsupervised learning. Engage in a neural networks lab and assignment for practical experience.
Module 4: Understand decision trees, bagging, and random forests, and participate in a walkthrough to solidify the learning. This module covers decision trees' strengths, weaknesses, and practical applications.
This course equips learners with the skills to plan, design, and analyze experiments effectively, using modern experimental strategies and data analysis techniques....
An Intuitive Introduction to Probability is a comprehensive course offering practical knowledge of Probability Theory, featuring lively, entertaining modules and...
Improving Your Statistical Inferences is a comprehensive course that equips learners with the knowledge and skills to enhance their statistical inferences, experimental...
Mathematical Biostatistics Boot Camp 2 offers a comprehensive study of data analysis and statistical inference, focusing on one and two independent samples.