Machine Learning 3: Regression
Explore the building blocks of regression and time-series forecasting, and learn how to apply them to real-world projects.
This course is PART 3 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
- QA & Data Profiling
- Regression & Forecasting
- Unsupervised Learning
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.
Throughout the course we’ll introduce 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.
NOTE: This is NOT a coding course, and doesn't cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
- Course Structure & Outline
- About this Series
- DOWNLOAD: Course Resources
- Setting Expectations
- Supervised vs. Unsupervised Learning
- RECAP: Key Concepts
- Regression 101
- Regression Workflow
- Feature Engineering
- Splitting & Overfitting
- Prediction vs. Root-Cause Analysis
- QUIZ: Intro to Regression
- Intro to Regression
- Linear Relationships
- Least Squared Error
- Univariate Linear Regression
- CASE STUDY: Univariate Regression
- Multiple Linear Regression
- Non-Linear Regression
- CASE STUDY: Non-Linear Regression
- QUIZ: Regression Modeling
- Intro to Model Diagnostics
- Sample Model Output
- Mean Error (MSE, MAE, MAPE)
- Null Hypothesis
- T-Values & P-Values
- Variance Inflation (VIF)
- RECAP: Sample Model Output
- QUIZ: Model Diagnostics
- Intro to Forecasting
- Auto Correlation Function
- CASE STUDY: Auto Correlation
- One-Hot Encoding
- CASE STUDY: One-Hot Encoding
- Moving Averages
- CASE STUDY: Moving Averages
- Linear Trend
- CASE STUDY: Seasonality + Trend
- Non-Linear Forecasting
- CASE STUDY: Non-Linear Forecast
- Intervention Analysis
- CASE STUDY: Intervention Analysis
- QUIZ: Time-Series Forecasting
- Looking Ahead to Part 4
- Course Feedback Survey
- Share the love!
- Next Steps
WHO SHOULD TAKE THIS COURSE?
Data Analysts or BI experts looking to transition into a data science role or build a fundamental understanding of core ML topics
R or Python users seeking a deeper understanding of the models and algorithms behind their code
Anyone looking to learn the basics of machine learning through hands-on demos and intuitive, crystal clear explanations
WHAT ARE THE COURSE REQUIREMENTS?
- We'll use Microsoft Excel (Office 365 Pro Plus) for demos, but you are not required to follow along
Start learning for FREE, no credit card required!
Every subscription includes access to the following course materials
- Interactive Project files
- Downloadable e-books
- Graded quizzes and assessments
- 1-on-1 Expert support
- 100% satisfaction guarantee
- Verified credentials & accredited badges
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