Machine Learning for BI, PART 1: Data Profiling | Business Analytics & Intelligence Online Course by Udemy

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Demystify the world of machine learning & build core data science skills, without writing a single line of code

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Machine Learning for BI, PART 1: Data Profiling

If 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 1 of a 4-PART SERIES designed to help you build a strong, foundational understanding of machine learning: PART 1:QA & Data ProfilingPART2: ClassificationPART3: 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 1 course, well introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more. Well cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation: Section 1:Machine Learning Intro & LandscapeMachine learning process, definition, and landscapeSection 2:Preliminary Data QAVariable types, empty values, range & count calculations, left/right censoring, etc. Section 3:Univariate ProfilingHistograms, frequency tables, mean, median, mode, variance, skewness, etc. Section 4:Multivariate ProfilingViolin & box plots, kernel densities, heat maps, correlation, etc. Throughout the course well introduce real-world scenarios designed to help solidify key concepts and tie them back to actual business intelligence case studies. Youll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and much more. If youre 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: Data ProfilingebookDownloadableExcel 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.

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