Descriptions
Machine Learning 3: Regression & Forecasting, This course is part 3 of a 4-part series designed to help you build a deep, fundamental understanding of machine learning tools and techniques, from data profiling and QA to classification modeling, regression and forecasting, unsupervised learning, and more. You’ll review the supervised learning landscape, revisit key concepts like feature engineering, splitting, and overfitting, and explore the world of regression modeling. The course introduces core building blocks such as linear relationships and least squared error, and demonstrates their application in univariate, multivariate, and non-linear regression models. You’ll learn about common diagnostic metrics like R-squared, mean error, F-significance, and P-values, as well as important concepts like homoscedasticity and multicollinearity. Finally, you’ll dive into time-series forecasting, exploring techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of business decisions using intervention analysis. Throughout the course, case studies are used to reinforce key concepts and connect them to real-world scenarios, including estimating property prices, forecasting seasonal trends, predicting sales for new product launches, and measuring the business impact of website changes. This is not a coding course; instead, you’ll use familiar tools like Excel to demystify complex topics and see exactly how they work. If you’re ready to build the foundation for a successful career in data science, this course is for you.
What you’ll learn
- Review supervised learning concepts and feature engineering
- Understand linear, multivariate, and non-linear regression models
- Apply diagnostic metrics such as R-squared, mean error, F-significance, and P-values
- Explore time-series forecasting and intervention analysis
- Connect regression analysis to real-world business scenarios using Excel
Who this course is for
- Aspiring data scientists seeking a strong foundation in regression and forecasting
- Business analysts and professionals wanting to apply machine learning concepts without coding
- Anyone interested in practical, Excel-based approaches to machine learning
Specificatoin of Machine Learning 3: Regression & Forecasting
- Publisher : Maven Analytics
- Teacher : Josh MacCarty
- Language : English
- Level : All Levels
- Duration : 4 hours and 0 minutes
Content of Machine Learning 3: Regression & Forecasting
Requirements
- We’ll use Microsoft Excel (Office 365 Pro Plus) for demos, but you are not required to follow along
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : English
Quality: 720p
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
404 MB
