Machine Learning 4: Unsupervised Learning

This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of Machine Learning:

1. QA & Data Profiling
2. Classification
3. Regression & Forecasting
4. 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 CURRICULUM:

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