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Course Details

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Course Image Data Science in Python: Unsupervised Learning - Free Course Pro
Author Logo Maven Analytics

Data Science in Python: Unsupervised Learning

Course Info

Category

Development

Total Lectures

202

Total Duration

17 hours

Views

22

Requirements

We strongly recommend taking our Data Prep & EDA course before this oneJupyter Notebooks (free download, we'll walk through the install)Familiarity with base Python and Pandas is recommended, but not required
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Course Description

This is a hands-on, project-based course designed to help you master the foundations for unsupervised learning in Python.


We’ll start by reviewing the data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.


From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.


We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.


Next, we’ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We’ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.


Last but not least, we’ll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.


Throughout the course you'll play the role of an Associate Data Scientist for the HR Analytics team at a software company trying to increase employee retention. Using the skills you learn throughout the course, you'll use Python to segment the employees, visualize the clusters, and recommend next steps to increase retention.


COURSE OUTLINE:


  • Intro to Data Science

    • Introduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflow


  • Unsupervised Learning 101

    • Review the basics of unsupervised learning, including key concepts, types of techniques and applications, and its place in the data science workflow


  • Pre-Modeling Data Prep

    • Recap the data prep steps required to apply unsupervised learning models, including restructuring data, engineering & scaling features, and more


  • Clustering

    • Apply three different clustering techniques in Python and learn to interpret their results using metrics, visualizations, and domain expertise


  • Anomaly Detection

    • Understand where anomaly detection fits in the data science workflow, and apply techniques like Isolation Forests and DBSCAN in Python


  • Dimensionality Reduction

    • Use techniques like Principal Component Analysis (PCA) and t-SNE in Python to reduce the number of features in a data set without losing information


  • Recommenders

    • Recognize the variety of approaches for creating recommenders, then apply unsupervised learning techniques in Python, including Cosine Similarity and Singular Vector Decomposition (SVD)


__________


Ready to dive in? Join today and get immediate, LIFETIME access to the following:


  • 16.5 hours of high-quality video

  • 22 homework assignments

  • 7 quizzes

  • 3 projects

  • Data Science in Python: Unsupervised Learning ebook (350+ pages)

  • Downloadable project files & solutions

  • Expert support and Q&A forum

  • 30-day Udemy satisfaction guarantee


If you're an aspiring or seasoned data scientist looking for a practical overview of unsupervised learning techniques in Python with a focus on interpretation, this is the course for you.


Happy learning!

-Alice Zhao (Python Expert & Data Science Instructor,Maven Analytics)

What you'll learn

Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommendersPrepare data for modeling by applying feature engineering, selection, and scalingFit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCANUse unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detectionApply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNEBuild recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD)

Who this course is for

Data scientists who want to learn how to build and interpret unsupervised learning models in PythonAnalysts or BI experts looking to learn about unsupervised learning or transition into a data science roleAnyone interested in learning one of the most popular open source programming languages in the world

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