Master Cluster Analysis with Python and Pandas [2025]

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Henrik Johansson

26:41:13

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1 - Introduction

1 - Introduction.mp4

18:32

2 - Setup of the Anaconda Cloud Notebook.mp4

14:22

3 - Download and installation of the Anaconda Distribution optional.mp4

21:05

4 - The Conda Package Management System optional.mp4

35:00

2 - Master Python for Data Handling

5 - Overview of Python for Data Handling.mp4

28:25

6 - Python Integer.mp4

14:12

7 - Python Float.mp4

10:51

8 - Python Strings I.mp4

15:03

9 - Python Strings II Intermediate String Methods.mp4

22:37

10 - Python Strings III DateTime Objects and Strings.mp4

27:34

11 - Overview of Python Native Data Storage Structures.mp4

03:00

12 - Python Set.mp4

15:20

13 - Python Tuple.mp4

27:35

14 - Python Dictionary.mp4

30:00

15 - Python List.mp4

33:57

16 - Overview of Python Data Transformers and Functions.mp4

03:06

17 - Python Whileloop.mp4

19:20

18 - Python Forloop.mp4

17:02

19 - Python Logic Operators and conditional code branching.mp4

31:00

20 - Python Functions I Some theory.mp4

03:20

21 - Python Functions II create your own functions.mp4

33:53

22 - Python Object Oriented Programming I Some theory.mp4

14:10

23 - Python Object Oriented Programming II create your own custom objects.mp4

39:20

23 - python-oop-ii.zip

24 - Python Object Oriented Programming III Files and Tables.mp4

27:17

24 - python-oop3.zip

25 - Python Object Oriented Programming IV Recap and More.mp4

58:21

25 - Text-file.txt

25 - python-oop4-recap.zip

3 - Master Pandas for Data Handling

26 - Master Pandas for Data Handling Overview.mp4

11:21

27 - Pandas theory and terminology.mp4

11:13

28 - Creating a Pandas DataFrame from scratch.mp4

30:47

29 - Pandas File Handling Overview.mp4

02:51

30 - Pandas File Handling The csv file format.mp4

18:48

31 - Pandas File Handling The xlsx file format.mp4

23:20

32 - Pandas File Handling SQLdatabase files and Pandas DataFrame.mp4

15:08

33 - Pandas Operations Techniques Overview.mp4

03:11

34 - Pandas Operations Techniques Object Inspection.mp4

19:34

35 - Pandas Operations Techniques DataFrame Inspection.mp4

18:53

36 - Pandas Operations Techniques Column Selections.mp4

21:04

37 - Pandas Operations Techniques Row Selections.mp4

21:11

38 - Pandas Operations Techniques Conditional Selections.mp4

21:27

39 - Pandas Operations Techniques Scalers and Standardization.mp4

23:08

40 - Pandas Operations Techniques Concatenate DataFrames.mp4

29:21

40 - dataframes.zip

41 - Pandas Operations Techniques Joining DataFrames.mp4

19:30

41 - pandas-ops-tech-join-df.zip

42 - Pandas Operations Techniques Merging DataFrames.mp4

30:48

42 - pandas-ops-tech-merge-dataset.zip

43 - Pandas Operations Techniques Transpose Pivot Functions.mp4

34:31

43 - pandas-t-pivot-dataset.zip

44 - Pandas Data Preparation I Overview workflow.mp4

05:23

45 - Pandas Data Preparation II Edit DataFrame labels.mp4

20:16

46 - Pandas Data Preparation III Duplicates.mp4

22:23

47 - Pandas Data Preparation IV Missing Data Imputation.mp4

54:35

48 - Pandas Data Preparation V Data Binnings Extra Video.mp4

46:33

48 - geyser.csv

48 - geyser.xlsx

49 - Pandas Data Preparation VI Indicator Features Extra Video.mp4

33:01

49 - indicator-features.zip

49 - insurance-data.csv

50 - Pandas Data Description I Overview.mp4

02:35

51 - Pandas Data Description II Sorting and Ranking.mp4

26:51

52 - Pandas Data Description III Descriptive Statistics.mp4

31:40

53 - Pandas Data Description IV Crosstabulations Groupings.mp4

30:06

54 - Pandas Data Visualization I Overview.mp4

03:35

55 - Pandas Data Visualization II Histograms.mp4

42:34

56 - Pandas Data Visualization III Boxplots.mp4

33:00

57 - Pandas Data Visualization IV Scatterplots.mp4

40:00

58 - Pandas Data Visualization V Pie Charts.mp4

45:40

59 - Pandas Data Visualization VI Line plots.mp4

50:24

4 - Master Cluster Analysis and Unsupervised Learning

60 - Overview.mp4

22:30

61 - KMeans Cluster Analysis.mp4

47:15

61 - iris.csv

61 - kmeans-cluster-analysis.zip

62 - Autoupdated KMeans Cluster Analysis introduction and simulation.mp4

01:06:20

62 - au-kmeans-cluster-analysis-simulation.zip

62 - penguins.csv

63 - DensityBased Spatial Clustering of Applications with Noise DBSCAN.mp4

34:46

63 - dbscan.zip

64 - Four Hierarchical Clustering algorithms.mp4

21:18

64 - agglo-clustering.zip

Description

Master Cluster Analysis and Unsupervised Learning with Pandas and Python for Data Science and Machine Learning [2025]

What You'll Learn?

Master Cluster Analysis and Unsupervised Learning both in theory and practice
Master simple and advanced Cluster Analysis models
Use K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Principal Component Analysis, and more…
Evaluate Cluster Analysis models using many different tools
Learn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated Simulations
Gain Understanding of concepts such as truth, predicted truth or model-based conditional truth
Use effective advanced graphical tools to judge models’ performance
Use the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and Python
Master Python 3 programming with Python’s native data structures, data transformers, functions, object orientation, and logic
Use and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File Handling
Use Python’s advanced object-oriented programming and make your own custom objects, functions and how to generalize functions
Manipulate data and use advanced multi-dimensional uneven data structures
Master the Pandas 2 and 3 library for Advanced Data Handling
Use the language and fundamental concepts of the Pandas library and handle all aspects of creating, modifying, and selecting Data from a Pandas DataFrame
Use file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methods
Perform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of data
Make advanced data descriptions and statistics with Pandas. Rank, sort, cross-tabulate, pivot, melt, transpose, and group data
[Extra Video] Make advanced Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Who is this for?

Everyone who wants to Master Cluster Analysis and Unsupervised Learning

Everyone who wants to Master Python 3 from scratch or the beginner level

Everyone who wants to Master Python 3 and knows another programming language

Everyone who wants to reach the Master - intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning

Everyone who wants to Master the Pandas library

Everyone who wants to learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career

Everyone who wants to learn advanced Data Handling and improve their capabilities and productivity

What You Need to Know?

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended

Access to a computer with an internet connection

Programming experience is not needed and you will be taught everything you need

The course only uses costless software

Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

More details

Description
Welcome to the course Master Cluster Analysis and Unsupervised Learning with Pandas and Python!
Cluster Analysis and Unsupervised learning are one of the most important and defining tasks within machine learning and data science. Cluster Analysis and Unsupervised learning are one of the main methods for data scientists, analysts, A.I., and machine intelligences to create new insights, information or knowledge from data.
This course is a practical and exciting hands-on 3-in-1 master class video course about mastering Cluster Analysis and Unsupervised Learning with Advanced Data Handling using the Python 3 programming language combined with the powerful Pandas 2 + 3 library.
You will be taught to master some of the most useful and powerful Cluster Analysis and unsupervised learning techniques available and you will learn to master the Python programming language and the Pandas library for advanced Data Handling.

You will learn to:
Master Cluster Analysis and Unsupervised Learning both in theory and practice
Master simple and advanced Cluster Analysis models
Use K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Principal Component Analysis, and moreâ€¦
Evaluate Cluster Analysis models using many different tools
Learn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated Simulations
Gain Understanding of concepts such as truth, predicted truth or model-based conditional truth
Use effective advanced graphical tools to judge modelsâ€™ performance
Use the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and Python
Master Python 3 programming with Pythonâ€™s native data structures, data transformers, functions, object orientation, and logic
Use and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File Handling
Use Pythonâ€™s advanced object-oriented programming and make your own custom objects, functions and how to generalize functions
Manipulate data and use advanced multi-dimensional uneven data structures
Master the Pandas 2 and 3 library for Advanced Data Handling
Use the language and fundamental concepts of the Pandas library and handle all aspects of creating, changing, modifying, and selecting Data from a Pandas DataFrame object
Use file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methods
Perform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of data
Make advanced data descriptions and statistics with Pandas. Rank, sort, cross-tabulate, pivot, melt, transpose, and group data
[Extra Video] Make advanced Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages â€“ golden nuggets to improve your quality of work life.
And much moreâ€¦

This course is an excellent way to learn to master Cluster Analysis, Unsupervised Learning, Python, Pandas and Advanced Data Handling!
Cluster Analysis and Unsupervised Learning are considered exploratory types of data analysis and are useful for discovering new information and knowledge. Unsupervised Learning and Cluster Analysis are often viewed as one of the few ways for artificial intelligences and machine intelligences to create new knowledge or data information without human assistance or supervision, so-called supervised learning.
Data Handling is the process of making data useful for analysis. Most Data Scientists and Machine Learning Engineers spends about 80% of their working efforts and time on Data Handling tasks. Mastering Data Handling with Python and Pandas is an extremely useful and time-saving skill that functions as a force multiplier for productivity.
This course provides you with the option to use Cloud Computing with the Anaconda Cloud Notebook and to learn to use Cloud Computing resources, or you may use any Python capable environment of your choice.

This course is designed for everyone who wants to
learn to Master Cluster Analysis and Unsupervised Learning
learn to Master Python 3 from scratch or the beginner level
learn to Master Python 3 and knows another programming language
reach the Master - intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
learn to Master the Pandas library
learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
learn advanced Data Handling and improve their capabilities and productivity

Requirements:
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
Programming experience is not needed and you will be taught everything you need
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Cluster Analysis, Unsupervised Learning, Python, Pandas, and Data Handling.

Enroll now to receive 25+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
Who this course is for:
Everyone who wants to Master Cluster Analysis and Unsupervised Learning
Everyone who wants to Master Python 3 from scratch or the beginner level
Everyone who wants to Master Python 3 and knows another programming language
Everyone who wants to reach the Master - intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
Everyone who wants to Master the Pandas library
Everyone who wants to learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
Everyone who wants to learn advanced Data Handling and improve their capabilities and productivity

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Python

Henrik Johansson

Instructor's Courses

Henrik has a wide instructor/lecturer experience with more than 20 years in roles ranging from University teacher to sports coach to leadership roles in the private and public sectors.Henrik has experience teaching students from all walks of life, from the poor to royalty, and has taught students from nearly all educational backgrounds, from high school to Ph.D.s.Courses given by Henrik are intended to have unique content, and will teach you many new things.

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