Course Description

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. 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.

Course Outline

• Lesson 1: Introduction to Clustering
  • Introduction
  • Unsupervised Learning versus Supervised Learning
  • Clustering
  • Introduction to k-means Clustering
• Lesson 2: Hierarchical Clustering
  • Introduction
  • Clustering Refresher
  • The Organization of Hierarchy
  • Introduction to Hierarchical Clustering
  • Linkage
  • Agglomerative versus Divisive Clustering
  • k-means versus Hierarchical Clustering
• Lesson 3: Neighborhood Approaches and DBSCAN
  • Introduction
  • Introduction to DBSCAN
  • DBSCAN Versus k-means and Hierarchical Clustering
• Lesson 4: Dimension Reduction and PCA
  • Introduction
  • Overview of Dimensionality Reduction Techniques
  • PCA
• Lesson 5: Autoencoders
  • Introduction
  • Fundamentals of Artificial Neural Networks
  • Autoencoders
• Lesson 6: t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Introduction
  • Stochastic Neighbor Embedding (SNE)
  • Interpreting t-SNE Plots
• Lesson 7: Topic Modeling
  • Introduction
  • Cleaning Text Data
  • Latent Dirichlet Allocation
  • Non-Negative Matrix Factorization
• Lesson 8: Market Basket Analysis
  • Introduction
  • Market Basket Analysis
  • Characteristics of Transaction Data
  • Apriori Algorithm
  • Association Rules
• Lesson 9: Hotspot Analysis
  • Introduction
  • Kernel Density Estimation
  • Hotspot Analysis

Learner Outcomes

By the end of this program, you will be able to:

• Explain 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 theCIFAR-10 dataset
• Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data

Recommendations

Applies Towards the Following Certificates

• Data Scientist-Data Automation : Data Scientist-Data Automation