Python Data Science: Unsupervised Machine Learning
Python Data Science: Unsupervised Machine Learning, available at $54.99, has an average rating of 4.74, with 202 lectures, 7 quizzes, based on 94 reviews, and has 1294 subscribers.
You will learn about Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders Prepare data for modeling by applying feature engineering, selection, and scaling Fit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCAN Use unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detection Apply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNE Build recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD) This course is ideal for individuals who are Data scientists who want to learn how to build and interpret unsupervised learning models in Python or Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role or Anyone interested in learning one of the most popular open source programming languages in the world It is particularly useful for Data scientists who want to learn how to build and interpret unsupervised learning models in Python or Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role or Anyone interested in learning one of the most popular open source programming languages in the world.
Enroll now: Python Data Science: Unsupervised Machine Learning
Summary
Title: Python Data Science: Unsupervised Machine Learning
Price: $54.99
Average Rating: 4.74
Number of Lectures: 202
Number of Quizzes: 7
Number of Published Lectures: 202
Number of Published Quizzes: 7
Number of Curriculum Items: 209
Number of Published Curriculum Objects: 209
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will Learn
- Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders
- Prepare data for modeling by applying feature engineering, selection, and scaling
- Fit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCAN
- Use unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detection
- Apply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNE
- Build recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD)
Who Should Attend
- Data scientists who want to learn how to build and interpret unsupervised learning models in Python
- Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role
- Anyone interested in learning one of the most popular open source programming languages in the world
Target Audiences
- Data scientists who want to learn how to build and interpret unsupervised learning models in Python
- Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role
- Anyone interested in learning one of the most popular open source programming languages in the world
This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python.
We’ll start by reviewing the Python 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:
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Intro to Data Science in Python
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Introduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflow
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Unsupervised Learning 101
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Review the basics of unsupervised learning, including key concepts, types of techniques and applications, and its place in the data science workflow
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Pre-Modeling Data Prep
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Recap the data prep steps required to apply unsupervised learning models, including restructuring data, engineering & scaling features, and more
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Clustering
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Apply three different clustering techniques in Python and learn to interpret their results using metrics, visualizations, and domain expertise
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Anomaly Detection
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Understand where anomaly detection fits in the data science workflow, and apply techniques like Isolation Forests and DBSCAN in Python
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Dimensionality Reduction
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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
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Recommenders
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Recognize the variety of approaches for creating recommenders, then apply unsupervised learning techniques in Python, including Cosine Similarity and Singular Vector Decomposition (SVD)
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__________
Ready to dive in? Join today and get immediate, LIFETIME access to the following:
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16.5 hours of high-quality video
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22 homework assignments
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7 quizzes
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3 projects
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Python Data Science: Unsupervised Learning ebook (350+ pages)
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Downloadable project files & solutions
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Expert support and Q&A forum
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30-day Udemy satisfaction guarantee
If you’re a business intelligence professional or 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)
__________
Looking for our full business intelligence stack? Search for “Maven Analytics“ to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!
See why our courses are among the TOP-RATED on Udemy:
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Course Curriculum
Chapter 1: Getting Started
Lecture 1: Course Introduction
Lecture 2: About This Series
Lecture 3: Course Structure & Outline
Lecture 4: READ ME: Important Notes for New Students
Lecture 5: DOWNLOAD: Course Resources
Lecture 6: Introducing the Course Project
Lecture 7: Setting Expectations
Lecture 8: Jupyter Installation & Launch
Chapter 2: Intro to Data Science
Lecture 1: Section Introduction
Lecture 2: What is Data Science?
Lecture 3: Data Science Skill Set
Lecture 4: What is Machine Learning?
Lecture 5: Common Machine Learning Algorithms
Lecture 6: Data Science Workflow
Lecture 7: Step 1: Scoping a Project
Lecture 8: Step 2: Gathering Data
Lecture 9: Step 3: Cleaning Data
Lecture 10: Step 4: Exploring Data
Lecture 11: Step 5: Modeling Data
Lecture 12: Step 6: Sharing Insights
Lecture 13: Unsupervised Learning
Lecture 14: Key Takeaways
Chapter 3: Unsupervised Learning 101
Lecture 1: Section Introduction
Lecture 2: Unsupervised Learning 101
Lecture 3: Unsupervised Learning Techniques
Lecture 4: Unsupervised Learning Applications
Lecture 5: Structure of This Course
Lecture 6: Unsupervised Learning Workflow
Lecture 7: Key Takeaways
Chapter 4: Pre-Modeling Data Prep
Lecture 1: Section Introduction
Lecture 2: Data Prep for Unsupervised Learning
Lecture 3: Setting the Correct Row Granularity
Lecture 4: DEMO: Group By
Lecture 5: DEMO: Pivot
Lecture 6: ASSIGNMENT: Setting the Correct Row Granularity
Lecture 7: SOLUTION: Setting the Correct Row Granularity
Lecture 8: Preparing Columns for Modeling
Lecture 9: Identifying Missing Data
Lecture 10: Handling Missing Data
Lecture 11: Converting to Numeric
Lecture 12: Converting to DateTime
Lecture 13: Extracting DateTime
Lecture 14: Calculating Based on a Condition
Lecture 15: Dummy Variables
Lecture 16: ASSIGNMENT: Preparing Columns for Modeling
Lecture 17: SOLUTION: Preparing Columns for Modeling
Lecture 18: Feature Engineering
Lecture 19: Feature Engineering During Data Prep
Lecture 20: Applying Calculations
Lecture 21: Binning Values
Lecture 22: Identifying Proxy Variables
Lecture 23: Feature Engineering Tips
Lecture 24: ASSIGNMENT: Feature Engineering
Lecture 25: SOLUTION: Feature Engineering
Lecture 26: Excluding Identifiers From Modeling
Lecture 27: Feature Selection
Lecture 28: ASSIGNMENT: Feature Selection
Lecture 29: SOLUTION: Feature Selection
Lecture 30: Feature Scaling
Lecture 31: Normalization
Lecture 32: Standardization
Lecture 33: ASSIGNMENT: Feature Scaling
Lecture 34: SOLUTION: Feature Scaling
Lecture 35: Key Takeaways
Chapter 5: Clustering
Lecture 1: Section Introduction
Lecture 2: Clustering Basics
Lecture 3: K-Means Clustering
Lecture 4: K-Means Clustering in Python
Lecture 5: DEMO: K-Means Clustering in Python
Lecture 6: Visualizing K-Means Clustering
Lecture 7: Interpreting K-Means Clustering
Lecture 8: Visualizing Cluster Centers
Lecture 9: ASSIGNMENT: K-Means Clustering
Lecture 10: SOLUTION: K-Means Clustering
Lecture 11: Inertia
Lecture 12: Plotting Inertia in Python
Lecture 13: DEMO: Plotting Inertia in Python
Lecture 14: ASSIGNMENT: Inertia Plot
Lecture 15: SOLUTION: Inertia Plot
Lecture 16: Tuning a K-Means Model
Lecture 17: DEMO: Tuning a K-Means Model
Lecture 18: ASSIGNMENT: Tuning a K-Means Model
Lecture 19: SOLUTION: Tuning a K-Means Model
Lecture 20: Selecting the Best Model
Lecture 21: DEMO: Selecting the Best Model
Lecture 22: ASSIGNMENT: Selecting the Best K-Means Model
Lecture 23: SOLUTION: Selecting the Best K-Means Model
Lecture 24: Hierarchical Clustering
Lecture 25: Dendrograms in Python
Lecture 26: Agglomerative Clustering in Python
Lecture 27: DEMO: Agglomerative Clustering in Python
Lecture 28: Cluster Maps in Python
Instructors
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Maven Analytics
Empowering everyday people with life-changing data skills -
Alice Zhao
Data Science Instructor at Maven Analytics
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 23 votes
- 5 stars: 69 votes
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