Machine Learning for Data Analysis: Unsupervised Learning
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
- Explore powerful techniques for clustering, association mining, outlier detection, and dimensionality reduction
- Learn how ML models like K-Means, Apriori, Markov and Principal Component Analysis actually work
- Enjoy unique, hands-on demos to see how Unsupervised ML can be applied to real-world Business Intelligence projects
- 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
- This is PART 4 of our Machine Learning for BI series (we recommend taking Parts 1, 2 & 3 first)
This course is PART 4 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 Modeling
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.
In this course, we’ll start by reviewing the Machine Learning landscape, exploring the differences between Supervised and Unsupervised Learning, and introducing several of the most common unsupervised techniques, including cluster analysis, association mining, outlier detection, and dimensionality reduction.
Throughout the course, we'll focus on breaking down each concept in plain and simple language to help you build an intuition for how these models actually work, from K-Means and Apriori to outlier detection, Principal Component Analysis, and more.
Section 1: Intro to Unsupervised Machine Learning
Unsupervised Learning Landscape
Common Unsupervised Techniques
The Unsupervised ML Workflow
Section 2: Clustering & Segmentation
WSS & Elbow Plots
Interpreting a Dendogram
Section 3: Association Mining
Association Mining Basics
The Apriori Algorithm
Minimum Support Thresholds
Infrequent & Multiple Item Sets
Section 4: Outlier Detection
Outlier Detection Basics
Section 5: Dimensionality Reduction
Dimensionality Reduction Basics
Principle Component Analysis (PCA)
Throughout the course, we'll introduce unique demos and real-world case studies to help solidify key concepts along the way.
You'll see how k-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.
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: Unsupervised Learning 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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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
- Analytics professionals who want to learn powerful tools for clustering, association mining, basket analysis and outlier detection
Maven Analytics transforms everyday people into data rockstars by streamlining, simplifying, and personalizing the online learning experience.
Since 2014 we've helped students and teams across 150+ countries develop the most sought-after analytics and business intelligence skills, through on-demand courses, skills assessments, curated learning paths, and enterprise training.
Learning new skills shouldn’t be complicated. Think of Maven as your personal team of instructors, experts, mentors and guides, helping you navigate the learning process and develop the skills you need, on-demand.
Josh has 10+ Years of applying machine learning and data science to challenging business problems like marketing mix and pricing optimization, forecasting, clustering, natural language processing, and predictive modeling. He is passionate about breaking down seemingly complex machine learning topics and explaining them in business context. He believes that diving into machine learning should be accessible to everyone.