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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
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 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.
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
.
__________
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
Happy learning!
-Josh
M.
(Lead Machine Learning Instructor,
Maven Analytics
)
__________
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"
Maven Analytics
"
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, and
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