Machine Learning for Data Analysis: Regression & Forecasting

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 casesThis 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 ProfilingPART 2: Classification ModelingPART 3: Regression ForecastingPART 4: 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 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 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 today and get immediate, lifetime access to the following:High-quality, on-demand videoMachine Learning: Regression  Forecasting ebookDownloadable Excel project fileExpert QA forum30-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, and Tableau courses!See why our courses are among the TOP-RATED on Udemy:Some of the BEST courses 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.Who this course is for:Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanationsData Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learningR or Python users seeking a deeper understanding of the models and algorithms behind their codeExcel users who want to learn powerful tools for forecasting predictive analytics