Demystify the world of machine learning & build core data science skills, without writing a single line of code
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 correlati
- This is a beginner-friendly course (no prior knowledge or math/stats background required)
- We'll use Microsoft Excel (Office 365) for some course demos, but participation is optional
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 Profiling
- PART 2: Classification
- PART 3: Regression & Forecasting
- PART 4: Unsupervised Learning
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 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.
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 today and get immediate, lifetime access to the following:
- High-quality, on-demand video
- Machine Learning: Data Profiling ebook
- Downloadable Excel project file
- Expert Q&A forum
- 30-day money-back guarantee
-Josh M. (Lead Machine Learning Instructor, Maven Analytics)
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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.
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Who this course is for:
- Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanations
- Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning
- R or Python users seeking a deeper understanding of the models and algorithms behind their code
