Hands-On Unsupervised Learning with Python

Hands-On Unsupervised Learning with Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 34m | 804 MB

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.

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
Table of Contents

01 The Course Overview
02 Benefits of Unsupervised Learning
03 How Market Basket Analysis Works
04 How Market Basket Analysis Works (Continued)
05 The Apriori Algorithm – Preparing the Data
06 Understanding and Implementing the Apriori Algorithm
07 Finding Association Rules
08 Visualizing and Interpreting Association Rules
09 Unsupervised Learning and the Curse of Dimensionality
10 Approaches to Dimensionality Reduction
11 The Key Ideas Behind PCA
12 The Key Ideas Behind PCA (Continued)
13 The Linear Algebra Behind PCA
14 The Linear Algebra Behind PCA (Continued)
15 PCA in Practice
16 PCA in Practice (Continued)
17 Clustering – Key Concepts
18 Clustering Algorithm in Practice
19 Evaluate Clustering Results
20 Case Study – K-Means and Wholesale Data
21 Case Study – K-Means and Wholesale Data (Continued)