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Forecasting & Machine Learning for BI, PART 3: Regression

Forecasting & Machine Learning for BI, PART 3: Regression 

Demystify Machine Learning and build foundational Data Science skills like regression & forecasting, without any code!

What you'll learn

  • Build foundational machine learning & data science skills, without writing complex code
  • Use intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques
  • Predict numerical outcomes using regression modeling and time-series forecasting techniques
  • Calculate diagnostic metrics like R-Squared, Mean Error, F-Significance and P-Values to diagnose model quality
  • Explore unique, hands-on case studies to see how regression analysis can be applied to real-world business intelligence use cases

Description

This course is PART 3 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:

  • PART 1: QA & Data Profiling
  • PART 2: Classification
  • PART 3: Regression & Forecasting
  • PART 4: Unsupervised Learning (Coming Soon!)

This course makes data science approachable to everyday people, and is designed to demystify powerful Machine Learning tools & techniques without trying to teach you a coding language at the same time.

Instead, we'll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most Data Science and Machine Learning courses, you won't write a SINGLE LINE of code.

COURSE OUTLINE:

In this Part 3 course, we’ll start by introducing core building blocks like linear relationships and least squared error, then show you how these concepts can be applied to univariate, multivariate, and non-linear regression models.

From there we'll review common diagnostic metrics like R-squared, mean error, F-significance, and P-Values, along with important concepts like homoscedasticity and multicollinearity.

Last but not least we’ll dive into time-series forecasting, and explore powerful techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of key business decisions using intervention analysis:

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