Description
Statistical Learning Course for Data Science Specialization. Statistical learning is a very important specialization for those pursuing a career in data science or looking to increase their expertise in the field. This program builds on your basic knowledge of statistics and equips you with advanced techniques for model selection, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods. In addition, you will gain a deep understanding of coefficient estimation and interpretation that will be valuable in explaining and justifying your models to clients and companies. Through this specialization, you will gain the conceptual knowledge and communication skills to effectively communicate the rationale behind your model selection and coefficient interpretations. This specialization can be considered for academic credit as part of CU Boulder’s Master of Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from the departments of Applied Mathematics, Computer Science, Information Science, and others at CU Boulder. With performance-based admission and no application process, the MS-DS is ideal for individuals with a wide range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Applied Learning Project: During this specialization, learners will complete many programming assignments designed to help learners master statistical learning concepts, including regression, classification, trees, SVM, unsupervised learning, splines, and sample methods. They will complete the retake.
- Learn the required skills from university and industry experts
- Master a topic or tool with hands-on projects
- Develop a deep understanding of key concepts
What you will learn in Statistical Learning for Data Science Specialization course
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Explain why statistical learning is important and how it can be used.
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Explain the advantages and disadvantages of specific models in specific situations.
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Apply many regression and classification techniques.
Statistical Learning for Data Science Specialization course specifications
- Publisher: Coursera
- English language
- Duration of training: 4 months including 9 hours of work per week
- Number of courses: 3
- teacher: Osita Onyejekwe, James Bird
- Education level: Intermediate
- Presenting institution/university: University of Colorado Boulder
Course headings
Course images
Sample video of the course
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
The version of 2024/5 compared to 2023/10 has increased the number of 9 lessons and the duration of 2 hours and 7 minutes.
download link
Regression and Classification
Resampling, Selection and Splines
Trees, SVM and Unsupervised Learning
File(s) password: www.downloadly.ir
Size
3.82 GB
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