What you'll learn:
- Build foundational machine learning & data science skills, without writing complex code
- Use intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques
- Prepare raw data for analysis using QA tools like variable types, range calculations & table structures
- Analyze datasets using common univariate & multivariate profiling metrics
- Describe & visualize distributions with histograms, kernel densities, heat maps and violin plots
- Explore multivariate relationships with scatterplots and correlation
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 1 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:
PART 1:QA & Data Profiling
PART2:Classification Modeling
PART3:Regression & Forecasting
PART4:Unsupervised Learning
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.
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, 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.
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:
Section 1:Machine Learning Intro &Landscape
Machine learning process,definition, and landscape
Section 2:Preliminary Data QA
Variable types, empty values, range &count calculations, left/right censoring, etc.
Section 3:Univariate Profiling
Histograms, frequency tables, mean, median, mode, variance, skewness, etc.
Section 4:Multivariate Profiling
Violin &box plots, kernel densities, heat maps, correlation, etc.
Throughout the course we’ll introduce real-world scenarios designed to help solidify key concepts and tie them back to actual business intelligence case studies. You’ll 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 you’re ready to build the foundation for a successful career in Data Science, this is the course for you.
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Join todayand get immediate, lifetime accessto the following:
High-quality, on-demand video
Machine Learning:Data Profilingebook
DownloadableExcel project file
ExpertQ&Aforum
30-day money-back guarantee
Happy learning!
-Josh M. (Lead Machine Learning Instructor, Maven Analytics)
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