Hands-On Unsupervised Learning with Python [Video]

More Information
  • Utilize Unsupervised Learning for your real-world analysis needs
  • Explore various Python libraries, including numpy, pandas, scikit-learn, matplotlib, seaborn and plotly
  • Understand how the Apriori Algorithm computes Association Rules
  • Build a Recommendation Engine using association rules
  • Utilize market basket analysis to recommend favourite products
  • Gain in-depth knowledge of Principle Component Analysis and use it to effectively manage noisy datasets
  • Learn how key clustering algorithms like K-Means and Gaussian Mixture Models work
  • Discover the power of PCA and K-Means for discovering patterns and customer profiles by analyzing wholesale product data
  • Visualize, interpret, and evaluate the quality of the analysis done using Unsupervised Learning

This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code.

Say you have millions of transaction data on products purchased at a retailer. Which individual products or product categories are most likely to be purchased together? How about a large number of survey responses – which answers were most often given together, for all or some subset of respondents? Association Rules provide answers to these questions, and they are most frequently used in Market Basket Analysis. The Apriori Algorithms solves the formidable computational challenges of calculating Association Rules. After taking this course, you will be understanding and be able to apply the Apriori Algorithm to calculate, interpret and create interactive visualizations of association rules.

Suppose you are a nutritionist trying to explore the nutritional content of food. What is the best way to differentiate food items? By vitamin content? Protein levels? Or perhaps a combination of both? Use Deep Learning and Unsupervised Learning to find out.

This course will allow you to utilize Principal Component Analysis, and to visualize and interpret the results of your datasets such as the ones in the above description. You will also be able to apply hard and soft clustering methods (k-Means and Gaussian Mixture Models) to assign segment labels to customers categorized in your sample data sets.

After watching this course, you will know how to apply the basic principles of Unsupervised Learning using Python. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Hands-on-Unsupervised-Learning-with-Python

Style and Approach

This friendly course takes you through the basics of Unsupervised Learning. It is packed with step-by-step instructions and working examples. This comprehensive course is divided into clear bite-size chunks, so you can learn at your own pace and focus on the areas of most interest to you.

  • Select and apply key Unsupervised Learning methods to discover hidden structure in data, in particular: Conduct, interpret and visualize market basket analysis on transaction data
  • Understand how Principal Component Analysis works and apply dimensionality reduction using scikit-learn
  • Implement, evaluate and visualize the results of cluster algorithms
Course Length 3 hours 34 minutes
ISBN 9781788992855
Date Of Publication 28 Jun 2018


Stefan Jansen

Stefan Jansen, CFA is Founder and Lead Data Scientist at Applied AI where he advises Fortune 500 companies and startups across industries on translating business goals into a data and AI strategy, builds data science teams and develops ML solutions. Before his current venture, he was Managing Partner and Lead Data Scientist at an international investment firm where he built the predictive analytics and investment research practice. He was also an executive at a global fintech startup operating in 15 markets, worked for the World Bank, advised Central Banks in emerging markets, and has worked in 6 languages on four continents. Stefan holds Master's from Harvard and Berlin University and teaches data science at General Assembly and Datacamp.