Data Mining and the Analytics Workflow
This course explains the continuing relevance of Data Mining today, in the context of applying machine learning techniques to big data. It covers conceptual and practical details of powerful techniques such as Association Rules learning and the industry-standard CRISP-DM methodology for data mining workflows.
What you'll learn
Data Mining is an umbrella term used for techniques that find patterns in large datasets. Simply put, data mining is the application of machine learning techniques on big data. The popularity of the term Data Mining peaked some years ago, but in substance, data mining is perhaps more relevant today than it has ever been.
In this course, Data Mining and the Analytics Workflow you will gain the ability to formulate your use-case as a Data Mining problem, and then apply a classic process, the CRISP-DM methodology, to solve it.
First, you will learn how association rules learning works, and why it is considered a classic data mining application, predating the explosion in the popularity of ML. You will see the similarities and contrasts between association rules learning and recommender systems.
Next, you will discover how big data and machine learning both squarely lie within the ambit of data mining, even as more traditional data mining links to statistics and information retrieval continue to exist.
Finally, you will round out your knowledge by learning about an industry-standard process for building data mining applications, know as the CRISP-DM. This technique is about two decades old but has retained its relevance, and closely mirrors the classic machine learning workflow in wide use today.
When you’re finished with this course, you will have the skills and knowledge to design and implement the right data mining solution, one that applies machine learning on big data, for your use-case.
Table of contents
- Module Overview 1m
- Finding Patterns in Data 3m
- Association Rules for Market Basket Analysis 5m
- Frequent Itemsets and Support 4m
- Confidence, Lift, and Conviction 4m
- The Apriori Algorithm 4m
- Demo: Exploratory Data Analysis on Bakery Transactions 6m
- Demo: Setting up the Data for Association Rule Mining 2m
- Demo: Using the Apriori Algorithm to Generate Frequent Itemsets 5m
- Demo: Association Rule Mining 4m
- Clustering 3m
- Demo: Preparing Data for Cluster Analysis 3m
- Demo: Performing Cluster Analysis 5m
- Module Summary 2m
- Module Overview 2m
- Rule-based vs. ML-based Models 4m
- Demo: Rule Based Classification of Animal Species 4m
- Demo: Rule Based Classification of Iris Flowers 7m
- Overview of Logistic Regression, Support Vector Machines, and Naive Bayes for Classification 7m
- Demo: ML-based Classification of Gender Voices 8m
- Module Summary 1m
- Module Overview 2m
- Building Regression Models 3m
- Demo: Simple Regression 4m
- Demo: Preparing Data for Regression 6m
- Demo: Multiple Regression 2m
- Demo: Hierarchical Regression 5m
- Demo: Stepwise Regression Using Recursive Feature Elimination 4m
- Demo: Stepwise Regression Using Forward and Backward Selection 4m
- Demo: Setwise Regression 3m
- Module Summary 2m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more