About the course
Use Python to apply market basket analysis, PCA and dimensionality reduction, as well as cluster algorithms
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.
What You Will Learn
- 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
Stefan Jansen is a data scientist with over 10 years of industry experience in fintech, investment, and as an advisor to Fortune 500 companies and startups, focusing on data strategy, predictive analytics, and machine and deep learning. He has used Unsupervised Learning extensively to segment large customer bases, detect anomalies, apply topic modeling to large volumes of legal documents to automate due diligence, and to facilitate image recognition. He holds master degrees from Harvard University and Free University Berlin, a CFA charter, and has been teaching data science and statistics for several years.
Hands-on Machine Learning for Data Mining [Video]