Last updated 02/2023Duration: 57m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 1.21 GBGenre: eLearning | Language: English[Auto]
Regression Analysis for Data Science: A Comprehensive Guide from Fundamentals to Applications" What you'll learn Understand how regression analysis fits into the broader context of data science, Develop a deep understanding of the concepts of regression analysis, including its applications. Learn how to interpret regression coefficients and understand practical approach Learn how to deal with common issues in regression analysis Requirements Elementary knowledge of Math and Statistics Description Regression analysis plays a critical role in data science and machine learning. It is a fundamental statistical technique used to understand the relationship between a dependent variable and one or more independent variables. One of the primary applications of regression analysis in data science and machine learning is prediction. Regression models can be used to predict the value of a dependent variable based on the values of independent variables. The course covers the following areas Regression Analysis An Introduction Importance or uses of Regression Analysis Linear Regression and Regression Lines and illustrations Upon completion of the course, students will have a solid understanding of regression analysis and its applications. They will be able to use regression models to analyze data, make predictions, and draw meaningful conclusions. This course is ideal for students pursuing a career in data analysis, statistics, or any field that involves analyzing complex data sets. You'll receive support through a Q&A section, and the course is continually updated based on student feedback, with plans to add new topics in the future. So why wait? Enroll today and take the first step toward achieving your goals. With the right tools and support, you can make your dreams a reality and achieve the high score you deserve. Don't miss out on this opportunity to excel and boost your confidence. Who this course is for Data Science, Machine Learning deep learning students and professional
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