![]() Section 1: Machine Learning Intro & Landscape We’ll 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: In this Part 1 course, we’ll 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. Unlike most Data Science and Machine Learning courses, you won't write a SINGLE LINE of code. 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. This 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. This course is PART 1 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning: ![]()
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