This is the code repository for Hands-On Unsupervised Learning with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Use Unsupervised Learning tools like Market Basket Analysis, Principal Component Analysis, and Clustering algorithms to discover and extract hidden yet valuable structure in your customer data. Start by building your own recommendation engine using association rules that result from market basket analysis. Extract informative signals from noisy data using principal component analysis. Capitalize on the ability of cluster algorithms to identify natural groupings of your data to optimize, for instance, the targeting of your marketing efforts.
After watching this course and experimenting with the provided code, you will have required the requisite skills to apply key principles of Unsupervised Learning using Python.
- 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
To fully benefit from the coverage included in this course, you will need:
Prior Python programming experience is a requirement, and experience with data analysis and machine learning analysis will be helpful.
This course has the following software requirements:
Minimum Hardware Requirements
For successful completion of this course, students will require the computer systems with at least the following:
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
- Processor: Intel Core i5 or equivalent
- Memory: 4 GB RAM
- Storage: 35 GB available space
Recommended Hardware Requirements
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
- Processor: Intel Core i7 or equivalent
- Memory: 8 GB RAM
- Storage: 35 GB available space
Software Requirements
- OS: Windows 7 or Windows 10
- Browser: Google Chrome, Latest Version
- Code Editor: Atom IDE, Latest Version, spaCy
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