Machine Learning for Data Analysis: Regression & Forecasting

Overview

Machine Learning made simple with Excel! Regression models for advanced data analysis & business intelligence (no code!) What you'll learn:

Build foundational machine learning & data science skills, without writing complex codeUse intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniquesPredict numerical outcomes using regression modeling and time-series forecasting techniquesCalculate diagnostic metrics like R-Squared, Mean Error, F-Significance and P-Values to diagnose model qualityExplore unique, hands-on case studies to see how regression analysis can be applied to real-world business intelligence use cases HEADS UP!This course is now part of The Complete Visual Guide to Machine Learning &Data Science, which combines all 4 Machine Learning courses from Maven Analytics.

This course, along with the other individual courses in the series, will be retired soon.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 ProfilingPART2:

Classification ModelingPART3:

Regression & ForecastingPART4:

Unsupervised LearningThis 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 SINGLELINEof code.COURSEOUTLINE:

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 RegressionSupervised Learning landscapeRegression vs.

ClassificationFeature engineeringOverfitting &UnderfittingPrediction vs. Root-Cause AnalysisSection 2:

Regression Modeling 101Linear RelationshipsLeast Squared Error (SSE)Univariate RegressionMultivariate RegressionNonlinear TransformationSection 3:

Model DiagnosticsR-SquaredMean Error Metrics (MSE, MAE, MAPE)Null HypothesisF-SignificanceT-Values &P-ValuesHomoskedasticityMulticollinearitySection 4:

Time-Series ForecastingSeasonalityAuto Correlation Function (ACF)Linear TrendingNon-Linear Models (Gompertz)Intervention AnalysisThroughout 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!__________Join todayand get immediate, lifetime accessto the following:

High-quality, on-demand videoMachine Learning:

Regression &ForecastingebookDownloadableExcel project fileExpertQ&Aforum30-day money-back guaranteeHappy learning!-Josh M. (Lead Machine Learning Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for "Maven Analytics"to browse our full course library, including Excel, Power BI, MySQL, andTableaucourses!See why our courses are among the TOP-RATEDon Udemy:

"Some of the BESTcourses I've ever taken.

I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M."Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.

Syllabus

• Introduction to Machine Learning for Data Analysis
Overview of Machine Learning
Importance of Data Analysis in Business and Research
Types of Machine Learning: Supervised vs Unsupervised Learning
• Fundamentals of Regression Analysis
Introduction to Regression
Linear Regression
Assumptions of Linear Regression
Evaluating Linear Models (R², RMSE, MAE)
Multiple Linear Regression
Polynomial Regression
• Advanced Regression Techniques
Ridge Regression
Lasso Regression
Elastic Net Regression
Non-linear Regression Models
Support Vector Regression (SVR)
• Time Series Analysis and Forecasting
Introduction to Time Series Data
Components of Time Series: Trend, Seasonality, and Noise
Time Series Decomposition
Moving Averages and Smoothing Techniques
Autoregressive Models (AR, MA, ARMA)
ARIMA Models and Seasonal ARIMA (SARIMA)
Evaluating Forecast Models (MAPE, AIC, BIC)
• Machine Learning for Time Series Forecasting
Introduction to Machine Learning in Time Series
Feature Engineering for Time Series
Regression Models for Time Series
Tree-based Models: Decision Trees, Random Forests, and Gradient Boosting
Neural Networks for Time Series Forecasting
• Model Evaluation and Selection
Cross-Validation Techniques
Overfitting and Underfitting
Hyperparameter Tuning using Grid Search and Random Search
Model Deployment Considerations
• Tools and Libraries for Regression & Forecasting
Python Libraries: NumPy, pandas, scikit-learn
Time Series-specific Tools: statsmodels, Prophet
Introduction to R for Time Series Analysis
• Case Studies and Applications
Academic Case Study: Housing Price Prediction
Business Case Study: Sales Forecasting Using Historical Data
Environmental Case Study: Climate Data Analysis
• Project: Developing a Forecasting Model
Project Introduction and Requirements
Data Collection and Preprocessing
Model Selection and Training
Evaluation and Presentation of Results
• Conclusion and Further Reading
Review of Key Concepts
Recommended Resources for Further Learning
Future Trends in Machine Learning for Data Analysis

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Taught by

Maven Analytics and Joshua MacCarty

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Subjects

Data Science