Python Data Science: Unsupervised Machine Learning

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Maven Analytics,Alice Zhao

16:42:30

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1. Getting Started

1. Course Introduction.mp4

02:57

2. About This Series.mp4

00:47

3. Course Structure & Outline.mp4

04:18

4. READ ME Important Notes for New Students.html

5.1 Data Science in Python - Unsupervised Learning.pdf

5.2 Data Science in Python - Unsupervised Learning.zip

5. DOWNLOAD Course Resources.html

6. Introducing the Course Project.mp4

01:02

7. Setting Expectations.mp4

01:40

8. Jupyter Installation & Launch.mp4

08:15

2. Intro to Data Science

1. Section Introduction.mp4

01:09

2. What is Data Science.mp4

01:03

3. Data Science Skill Set.mp4

02:13

4. What is Machine Learning.mp4

01:55

5. Common Machine Learning Algorithms.mp4

03:03

6. Data Science Workflow.mp4

01:00

7. Step 1 Scoping a Project.mp4

01:25

8. Step 2 Gathering Data.mp4

01:07

9. Step 3 Cleaning Data.mp4

01:20

10. Step 4 Exploring Data.mp4

01:12

11. Step 5 Modeling Data.mp4

01:29

12. Step 6 Sharing Insights.mp4

01:22

13. Unsupervised Learning.mp4

00:50

14. Key Takeaways.mp4

01:47

15. Intro to Data Science.html

3. Unsupervised Learning 101

1. Section Introduction.mp4

00:45

2. Unsupervised Learning 101.mp4

04:39

3. Unsupervised Learning Techniques.mp4

03:39

4. Unsupervised Learning Applications.mp4

02:17

5. Structure of This Course.mp4

01:39

6. Unsupervised Learning Workflow.mp4

04:56

7. Key Takeaways.mp4

01:39

8. Unsupervised Learning 101.html

4. Pre-Modeling Data Prep

1. Section Introduction.mp4

01:24

2. Data Prep for Unsupervised Learning.mp4

02:34

3. Setting the Correct Row Granularity.mp4

06:28

4. DEMO Group By.mp4

06:37

5. DEMO Pivot.mp4

04:25

6. ASSIGNMENT Setting the Correct Row Granularity.mp4

02:21

7. SOLUTION Setting the Correct Row Granularity.mp4

05:29

8. Preparing Columns for Modeling.mp4

02:04

9. Identifying Missing Data.mp4

06:35

10. Handling Missing Data.mp4

08:09

11. Converting to Numeric.mp4

08:04

12. Converting to DateTime.mp4

06:55

13. Extracting DateTime.mp4

05:23

14. Calculating Based on a Condition.mp4

03:49

15. Dummy Variables.mp4

05:41

16. ASSIGNMENT Preparing Columns for Modeling.mp4

00:58

17. SOLUTION Preparing Columns for Modeling.mp4

02:25

18. Feature Engineering.mp4

03:17

19. Feature Engineering During Data Prep.mp4

02:29

20. Applying Calculations.mp4

04:49

21. Binning Values.mp4

03:42

22. Identifying Proxy Variables.mp4

05:01

23. Feature Engineering Tips.mp4

01:48

24. ASSIGNMENT Feature Engineering.mp4

00:48

25. SOLUTION Feature Engineering.mp4

01:42

26. Excluding Identifiers From Modeling.mp4

02:34

27. Feature Selection.mp4

04:47

28. ASSIGNMENT Feature Selection.mp4

00:45

29. SOLUTION Feature Selection.mp4

02:14

30. Feature Scaling.mp4

02:18

31. Normalization.mp4

07:46

32. Standardization.mp4

05:12

33. ASSIGNMENT Feature Scaling.mp4

00:41

34. SOLUTION Feature Scaling.mp4

03:09

35. Key Takeaways.mp4

01:38

36. Pre-Modeling Data Prep.html

5. Clustering

1. Section Introduction.mp4

01:16

2. Clustering Basics.mp4

04:21

3. K-Means Clustering.mp4

06:25

4. K-Means Clustering in Python.mp4

07:53

5. DEMO K-Means Clustering in Python.mp4

10:06

6. Visualizing K-Means Clustering.mp4

07:14

7. Interpreting K-Means Clustering.mp4

07:36

8. Visualizing Cluster Centers.mp4

09:16

9. ASSIGNMENT K-Means Clustering.mp4

01:26

10. SOLUTION K-Means Clustering.mp4

07:23

11. Inertia.mp4

05:45

12. Plotting Inertia in Python.mp4

02:46

13. DEMO Plotting Inertia in Python.mp4

11:25

14. ASSIGNMENT Inertia Plot.mp4

01:07

15. SOLUTION Inertia Plot.mp4

06:04

16. Tuning a K-Means Model.mp4

04:53

17. DEMO Tuning a K-Means Model.mp4

08:23

18. ASSIGNMENT Tuning a K-Means Model.mp4

01:04

19. SOLUTION Tuning a K-Means Model.mp4

09:17

20. Selecting the Best Model.mp4

06:03

21. DEMO Selecting the Best Model.mp4

13:55

22. ASSIGNMENT Selecting the Best K-Means Model.mp4

01:23

23. SOLUTION Selecting the Best K-Means Model.mp4

13:44

24. Hierarchical Clustering.mp4

13:25

25. Dendrograms in Python.mp4

10:53

26. Agglomerative Clustering in Python.mp4

03:59

27. DEMO Agglomerative Clustering in Python.mp4

06:11

28. Cluster Maps in Python.mp4

03:20

29. DEMO Cluster Maps in Python.mp4

12:03

30. ASSIGNMENT Hierarchical Clustering.mp4

01:26

31. SOLUTION Hierarchical Clustering.mp4

07:22

32. DBSCAN.mp4

08:49

33. DBSCAN in Python.mp4

04:27

34. Silhouette Score.mp4

06:16

35. Silhouette Score in Python.mp4

01:52

36. DEMO DBSCAN and Silhouette Score in Python.mp4

19:07

37. ASSIGNMENT DBSCAN.mp4

01:08

38. SOLUTION DBSCAN.mp4

04:45

39. Comparing Clustering Algorithms.mp4

07:56

40. Clustering Next Steps.mp4

03:53

41. DEMO Compare Clustering Models.mp4

05:18

42. DEMO Label Unseen Data.mp4

14:19

43. Key Takeaways.mp4

02:13

44. Clustering.html

6. PROJECT Clustering Clients

1. Project Overview.mp4

02:05

2. SOLUTION Data Prep.mp4

04:26

3. SOLUTION K-Means Clustering.mp4

16:54

4. SOLUTION Hierarchical Clustering.mp4

15:56

5. SOLUTION DBSCAN.mp4

04:52

6. SOLUTION Compare, Recommend and Predict.mp4

08:40

7. Anomaly Detection

1. Section Introduction.mp4

00:47

2. Anomaly Detection Basics.mp4

02:29

3. Anomaly Detection Approaches.mp4

06:24

4. Anomaly Detection Workflow.mp4

02:16

5. Isolation Forests.mp4

09:33

6. Isolation Forests in Python.mp4

07:57

7. Visualizing Anomalies.mp4

06:59

8. Tuning and Interpreting Isolation Forests.mp4

07:56

9. ASSIGNMENT Isolation Forests.mp4

01:15

10. SOLUTION Isolation Forests.mp4

10:37

11. DBSCAN for Anomaly Detection.mp4

01:23

12. DBSCAN for Anomaly Detection in Python.mp4

08:13

13. Visualizing DBSCAN Anomalies.mp4

05:32

14. ASSIGNMENT DBSCAN for Anomaly Detection.mp4

00:46

15. SOLUTION DBSCAN for Anomaly Detection.mp4

06:57

16. Comparing Anomaly Detection Algorithms.mp4

03:45

17. RECAP Clustering and Anomaly Detection.mp4

01:56

18. Key Takeaways.mp4

02:00

19. Anomaly Detection.html

8. Dimensionality Reduction

1. Section Introduction.mp4

01:21

2. Dimensionality Reduction Basics.mp4

03:03

3. Why Reduce Dimensions.mp4

08:46

4. Dimensionality Reduction Workflow.mp4

03:17

5. Principal Component Analysis.mp4

15:18

6. Principal Component Analysis in Python.mp4

04:39

7. Explained Variance Ratio.mp4

03:38

8. DEMO PCA and Explained Variance Ratio in Python.mp4

07:24

9. ASSIGNMENT Principal Component Analysis.mp4

00:54

10. SOLUTION Principal Component Analysis.mp4

03:11

11. Interpreting PCA.mp4

06:33

12. DEMO Interpreting PCA.mp4

12:55

13. ASSIGNMENT Interpreting PCA.mp4

00:55

14. SOLUTION Interpreting PCA.mp4

07:33

15. Feature Selection vs Feature Extraction.mp4

03:59

16. PCA Next Steps.mp4

04:47

17. T-SNE.mp4

18:09

18. T-SNE in Python.mp4

10:08

19. ASSIGNMENT T-SNE.mp4

00:30

20. SOLUTION T-SNE.mp4

02:51

21. PCA vs t-SNE.mp4

03:31

22. DEMO Dimensionality Reduction and Clustering.mp4

09:26

23. ASSIGNMENT T-SNE & K-Means Clustering.mp4

00:31

24. SOLUTION T-SNE & K-Means Clustering.mp4

04:36

25. Key Takeaways.mp4

02:42

26. Dimensionality Reduction.html

9. Recommenders

1. Section Introduction.mp4

01:22

2. Recommenders Basics.mp4

04:51

3. Content-Based Filtering.mp4

02:03

4. Cosine Similarity.mp4

07:11

5. Cosine Similarity in Python.mp4

14:33

6. Making a Content Based Filtering Recommendation.mp4

08:36

7. ASSIGNMENT Content-Based Filtering.mp4

01:03

8. SOLUTION Content-Based Filtering.mp4

08:40

9. Collaborative Filtering.mp4

04:00

10. User-Item Matrix.mp4

07:46

11. ASSIGNMENT User-Item Matrix.mp4

00:45

12. SOLUTION User-Item Matrix.mp4

04:21

13. Singular Value Decomposition.mp4

08:42

14. Singular Value Decomposition in Python.mp4

13:05

15. ASSIGNMENT Singular Value Decomposition.mp4

00:47

16. SOLUTION Singular Value Decomposition.mp4

04:08

17. Choosing the Number of Components.mp4

04:26

18. DEMO Choosing the Number of Components.mp4

12:47

19. ASSIGNMENT Choosing the Number of Components.mp4

00:54

20. SOLUTION Choosing the Number of Components.mp4

07:25

21. Making a Collaborative Filtering Recommendation.mp4

08:17

22. DEMO Making a Collaborative Filtering Recommendation.mp4

11:57

23. ASSIGNMENT Collaborative Filtering.mp4

01:13

24. SOLUTION Collaborative Filtering.mp4

11:31

25. Recommender Next Steps.mp4

06:23

26. DEMO Hybrid Approach.mp4

04:51

27. Key Takeaways.mp4

02:38

28. Recommenders.html

10. PROJECT Recommending Restaurants

1. Project Overview.mp4

01:51

2. SOLUTION Data Prep.mp4

01:56

3. SOLUTION TruncatedSVD.mp4

11:22

4. SOLUTION Cosine Similarity.mp4

05:45

5. SOLUTION Recommendations.mp4

04:28

11. Unsupervised Learning Review

1. Section Introduction.mp4

01:04

2. Unsupervised Learning Flow Chart.mp4

04:02

3. Unsupervised Learning Techniques & Applications.mp4

05:45

4. Unsupervised Learning in the Data Science Workflow.mp4

03:46

5. Key Takeaways.mp4

02:14

12. Final Project

1. Final Project Overview.mp4

02:16

2. SOLUTION Data Prep & EDA.mp4

10:19

3. SOLUTION Clustering.mp4

03:47

4. SOLUTION PCA.mp4

03:44

5. SOLUTION Clustering (Round 2).mp4

03:33

6. SOLUTION PCA (Round 2).mp4

04:41

7. SOLUTION EDA on Clusters.mp4

04:04

8. SOLUTION Recommendations.mp4

02:27

13. Next Steps

1. BONUS LESSON.html

Description

Learn Python for data science & machine learning, and build unsupervised learning models w/ a top Python instructor!

What You'll 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 is this for?

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

What You Need to Know?

We strongly recommend taking our Data Prep & EDA course before this one

Jupyter Notebooks (free download, we'll walk through the install)

Familiarity with base Python and Pandas is recommended, but not required

More details

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

Weâ€™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. Youâ€™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. Weâ€™ll start with K-Means Clustering, learn to interpret the outputâ€™s cluster centers, and use inertia plots to select the right number of clusters. Next, weâ€™ll cover Hierarchical Clustering, where weâ€™ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, weâ€™ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.

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

Next, weâ€™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. Weâ€™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, weâ€™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.

COURSEÂ OUTLINE:

Intro to Data Science in Python
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 todayÂ and get immediate, LIFETIME accessÂ to the following:

16.5 hours of high-quality video
22 homework assignments
7 quizzes
3 projects
Python Data Science: Unsupervised Learning ebook (350+Â pages)
Downloadable project files &Â solutions
Expert support and Q&AÂ forum
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"Â to browse our full course library, including Excel, Power BI, MySQL,Â TableauÂ and Machine Learning courses!

See why our courses are among the TOP-RATEDÂ on Udemy:

"Some of the BESTÂ courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C.

"This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M.

"Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.
Who this course is for:
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

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Maven Analytics helps individuals and teams build expert-level analytics & business intelligence skills. We've helped more than 1,000,000 students around the world build job-ready skills, master sought-after tools like Excel, SQL, Power BI, Tableau & Python, and build the foundation for a successful career in data. At Maven Analytics, we empower everyday people to change the world with data.

Alice Zhao

Instructor's Courses

Alice Zhao is a data scientist who is passionate about teaching and making complex things easy to understand. She is the author of the book, SQL Pocket Guide, 4th Edition (O'Reilly).She has 15+ years of experience in the data field, and has taught numerous courses in Python, SQL and R as a data science instructor at Maven Analytics and Metis, and as a co-founder of Best Fit Analytics.She has the most popular Natural Language Processing in Python tutorial on YouTube, with over 1,200,000 views. In her spare time, she writes about pop culture and data analysis on her blog, A Dash of Data.She has an M.S. in Analytics and B.S. in Electrical Engineering, both from Northwestern University.

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