MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 49 lectures (2h) | Size: 528.4 MB

Learn powerful Unsupervised Machine Learning techniques like clustering, association mining, outlier detection and more!

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 bner-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
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 .
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 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
Feature Eeering
The Unsupervised ML Workflow
Section 2: Clustering & Sntation
Clustering Basics
K-Means Clustering
WSS & Elbow Plots
Hierarchical Clustering
Interpreting a Dendogram
Section 3: Association Mining
Association Mining Basics
The Apriori Algorithm
Basket Analysis
Minimum Support Thresholds
Infrequent & Multiple Item Sets
Markov Chains
Section 4: Outlier Detection
Outlier Detection Basics
Cross-Sectional Outliers
Nearest Neighbors
-Series Outliers
Residual Distribution
Section 5: Dimensionality Reduction
Dimensionality Reduction Basics
Principle Component Analysis (PCA)
Scree Plots
Advanced Techniques
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 snts, how apriori can be used for basket analysis and recommendation ees, and how outlier detection can spot anomalies in cross-sectional or -series datasets.
If you're ready to build the foundation for a successful career in Data Science, this is the course for you!
-Josh M. (Lead Machine Learning Instructor, Maven Analytics)
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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




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