Machine Learning for Data Analysis: Classification Modeling

Build foundational machine learning data science skills, without writing complex codeUse intuitive, user-friendly tools like Microsoft Excel to introduce demystify machine learning tools techniquesEnrich datasets by using feature engineering techniques like one-hot encoding, scaling, and discretizationPredict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, decision trees, and moreApply techniques for selecting tuning classification models to optimize performance, reduce bias, and minimize driftCalculate metrics like accuracy, precision and recall to measure model performanceIf you’re excited to explore Data Science Machine Learning but anxious about learning complex programming languages or intimidated by terms like naive bayes, logistic regression, KNN and decision trees, you’re in the right place.This course is PART 2 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 2 course, we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.From there we’ll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.Section 1: Intro to ClassificationSupervised Learning landscapeClassification workflowFeature engineeringData splittingOverfitting  UnderfittingSection 2: Classification ModelsK-Nearest NeighborsNaïve BayesDecision TreesRandom ForestsLogistic RegressionSentiment AnalysisSection 3: Model Selection TuningHyperparameter tuningImbalanced classesConfusion matricesAccuracy, Precision  recallModel selection driftThroughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.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: Classification 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 predictive analytics