Machine Learning 4: Unsupervised Learning
Learn the basics of Unsupervised ML, including cluster analysis, association mining, outlier detection & dimensionality reduction.
This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of Machine Learning:
- QA & Data Profiling
- Regression & Forecasting
- Unsupervised Learning
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; 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.
As always, 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.
NOTE: This is NOT a coding course, and doesn't cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
- Course Structure & Outline
- About this Series
- DOWNLOAD: Course Resources
- Setting Expectations
- Supervised vs. Unsupervised Learning
- Common Unsupervised Techniques
- Unsupervised ML Workflow
- Feature Engineering
- KEY TAKEAWAYS: Intro to Unsupervised ML
- QUIZ: Intro to Unsupervised ML
- Clustering Basics
- Intro to K-Means
- WSS & Elbow Plots
- K-Means FAQs
- CASE STUDY: K-Means
- Intro to Hierarchical Clustering
- Anatomy of a Dendogram
- Hierarchical Clustering FAQs
- KEY TAKEAWAYS: Clustering & Segmentation
- QUIZ: Clustering & Segmentation
- Association Mining Basics
- The Apriori Algorithm
- Basket Analysis Examples
- Minimum Support Thresholds
- Infrequent Itemsets
- Multiple Item Sets
- Markov Chains
- CASE STUDY: Markov Chains
- KEY TAKEAWAYS: Association Mining
- QUIZ: Association Mining
- Outlier Detection Basics
- Cross-Sectional Outliers
- Nearest Neighbors
- CASE STUDY: Outlier Detection
- Time-Series Anomalies
- KEY TAKEAWAYS: Outlier Detection
- QUIZ: Outlier Detection
- Dimensionality Reduction Use Cases
- Principle Component Analysis
- PCA Example
- Interpreting Components
- Scree Plots
- Advanced Techniques
- KEY TAKEAWAYS: Dimensionality Reduction
- QUIZ: Dimensionality Reduction
- Series Conclusion
- Course Feedback Survey
- Share the love!
- Next Steps
WHO SHOULD TAKE THIS COURSE?
Data Analysts or BI experts looking to transition into a data science role or build a fundamental understanding of core ML topics
R or Python users seeking a deeper understanding of the models and algorithms behind their code
Anyone looking to learn the basics of machine learning through hands-on demos and intuitive, crystal clear explanations
WHAT ARE THE COURSE REQUIREMENTS?
- We'll use Microsoft Excel (Office 365 Pro Plus) for demos, but you are not required to follow along
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Every subscription includes access to the following course materials
- Interactive Project files
- Downloadable e-books
- Graded quizzes and assessments
- 1-on-1 Expert support
- 100% satisfaction guarantee
- Verified credentials & accredited badges
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