This is the code repository for Mastering 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.
Taking this course will make you a Master of Advanced Unsupervised Learning concepts, will enable you to solve new problems, and will provide you with new tools to approach Supervised Learning more effectively. Start for instance with topic modeling, before becoming a developer of apps that can recommend interesting articles from a given data set.
- Master the Unsupervised Learning landscape and apply Deep Learning
- Use alternatives to K-Means and Gaussian Mixture Models for your data analysis
- Compare and evaluate the results of different data analyses to determine the quality of clusters, time, and memory usage
- Use the bag-of-words model to convert text to features to preprocess text
- Apply algorithms such as LSA, LSI, and LDA to model topics using gensim and sklearn
- Compare T-SNE and UMAP with PCA and ICA, in the context of how different algorithms work and when to apply them
- Learn the Python application of TSNE and UMAP to image data using sklearn and umap
- Leverage Unsupervised Learning to assess the difficulty of your Supervised Learning algorithms on a dataset
- Evaluate the results of analysis applied to various datasets using Unsupervised Learning
To fully benefit from the coverage included in this course, you will need:
This course targets all analysts and data scientists keen to master applications of Unsupervised Learning from a conceptual and practical point of view. Prior python programming experience is a requirement, and experience with data analysis and machine learning analysis will be helpful; a basic knowledge of Unsupervised Learning algorithms such as Clustering and Dimensionality Reduction is expected.
This course has the following software requirements:
SETUP INFORMATION
Setup and Installation
This will vary on a product-by-product basis, but should be a standard PI element for ILT products. This example is relatively basic.
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