Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter. You will complete the course by challenging yourself through various interesting activities such as performing a market basket analysis and identifying relationships between different merchandises.
By the end of this course, you will have the skills you need to confidently build your own models using Python.
- Understand the basics and importance of clustering
- Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages
- Explore dimensionality reduction and its applications
- Use scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset
- Employ Keras to build autoencoder models for the CIFAR-10 dataset
- Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data
We recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 4 GB RAM
- Storage: 5 GB available space
You'll need the following software installed in advance:
- Operating systems: Windows 7, or 8.1, or 10 64-bit, macOS, Linux
- Python: 3.6.5 or later is preferred; available through https://www.python.org/downloads/release/python-371/
- Anaconda: This is for the basemap module of
mlp_toolkits; go to https://www.anaconda.com/distribution/, download the latest version, and follow the instructions to install it.