Machine Learning For Bi Part 3

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:

• Section 1: Intro to Regression

  • Supervised Learning landscape

  • Regression vs. Classification

  • Feature engineering

  • Overfitting & Underfitting

  • Prediction vs. Root-Cause Analysis

  
  

• Section 2: Regression Modeling 101

  • Linear Relationships

  • Least Squared Error (SSE)

  • Univariate Regression

  • Multivariate Regression

  • Nonlinear Transformation

  
  

• Section 3: Model Diagnostics

  • R-Squared

  • Mean Error Metrics (MSE, MAE, MAPE)

  • Null Hypothesis

  • F-Significance

  • T-Values & P-Values

  • Homoskedasticity

  • Multicollinearity

  
  

• Section 4: Time-Series Forecasting

  • Seasonality

  • Auto Correlation Function (ACF)

  • Linear Trending

  • Non-Linear Models (Gompertz)

  • Intervention Analysis

Throughout the course we’ll introduce hands-on case studies to solidify key concepts and tie them back to real world scenarios. You’ll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.

If you’re ready to build the foundation for a successful career in Data Science, this is the course for you!

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Join today and get immediate, lifetime access to the following:

• High-quality, on-demand video

• Machine Learning: Regression & Forecasting ebook

• Downloadable Excel project file

• Expert Q&A forum

• 30-day money-back guarantee

Happy learning!

-Josh M. (Lead Machine Learning Instructor, Maven Analytics)

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