PAC | Hands-On Unsupervised Learning With Python (2018 EN)

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    Company: Packt Publishing
    Author: Stefan Jansen
    Full Title: Hands-On Unsupervised Learning With Python
    Year: 2018
    Language: English
    Genre: Educational: Application Development
    Skill Level: -
    Price: €124.99
    Files: MP4 (+ Code Files)
    Time: 03:34:14
    Video: AVC, 1920 x 1080 (1.778) at 30.000 fps, 300 kbps
    Audio: AAC at 159 Kbps, 2 channels, 48.0 KHz

    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.

    ✓ 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

    ✓ Select and apply key Unsupervised Learning methods to discover hidden structure in data, in particular: Conduct, interpret and visualize market basket analysis on transaction data
    ✓ Understand how Principal Component Analysis works and apply dimensionality reduction using scikit-learn
    ✓ Implement, evaluate and visualize the results of cluster algorithms

    1. Goals of Unsupervised Learning
    01. The Course Overview
    02. Benefits of Unsupervised Learning
    2. Building a Recommendation Engine
    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
    3. Extracting the Signal from the Noise
    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)
    4. Optimize Market Targeting
    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)