Master Cluster Analysis With Python And Pandas [2024]

Master Cluster Analysis and Unsupervised Learning with Pandas and Python for Data Science and Machine Learning[2024]

What you’ll learn
Master Cluster Analysis and Unsupervised Learning both in theory and practice
Master simple and advanced Cluster Analysis models
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

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

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 practiceMaster simple and advanced Cluster Analysis modelsEvaluate Cluster Analysis models using many different toolsLearn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated SimulationsGain Understanding of concepts such as truth, predicted truth or model-based conditional truthUse effective advanced graphical tools to judge models’ performanceUse the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and PythonMaster Python 3 programming with Python’s native data structures, data transformers, functions, object orientation, and logicUse and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File HandlingUse Python’s advanced object-oriented programming and make your own custom objects, functions and how to generalize functionsManipulate data and use advanced multi-dimensional uneven data structuresMaster the Pandas 2 and 3 library for Advanced Data HandlingUse the language and fundamental concepts of the Pandas library and handle all aspects of creating, changing, modifying, and selecting Data from a Pandas DataFrame objectUse file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methodsPerform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of dataMake 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 SeabornCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resourcesOption: 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 – golden nuggets to improve your quality of work life.And much more…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 tolearn to Master Cluster Analysis and Unsupervised Learninglearn to Master Python 3 from scratch or the beginner levellearn to Master Python 3 and knows another programming languagereach the Master – intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learninglearn to Master the Pandas librarylearn Data Handling skills that work as a force multiplier and that they will have use of in their entire careerlearn advanced Data Handling and improve their capabilities and productivityRequirements:Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionProgramming experience is not needed and you will be taught everything you needThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedThis 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!

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Setup of the Anaconda Cloud Notebook

Lecture 3 Download and installation of the Anaconda Distribution (optional)

Lecture 4 The Conda Package Management System (optional)

Section 2: Master Python for Data Handling

Lecture 5 Overview of Python for Data Handling

Lecture 6 Python Integer

Lecture 7 Python Float

Lecture 8 Python Strings I

Lecture 9 Python Strings II: Intermediate String Methods

Lecture 10 Python Strings III: DateTime Objects and Strings

Lecture 11 Overview of Python Native Data Storage Structures

Lecture 12 Python Set

Lecture 13 Python Tuple

Lecture 14 Python Dictionary

Lecture 15 Python List

Lecture 16 Overview of Python Data Transformers and Functions

Lecture 17 Python While-loop

Lecture 18 Python For-loop

Lecture 19 Python Logic Operators and conditional code branching

Lecture 20 Python Functions I: Some theory

Lecture 21 Python Functions II: create your own functions

Lecture 22 Python Object Oriented Programming I: Some theory

Lecture 23 Python Object Oriented Programming II: create your own custom objects

Lecture 24 Python Object Oriented Programming III: Files and Tables

Lecture 25 Python Object Oriented Programming IV: Recap and More

Section 3: Master Pandas for Data Handling

Lecture 26 Master Pandas for Data Handling: Overview

Lecture 27 Pandas theory and terminology

Lecture 28 Creating a Pandas DataFrame from scratch

Lecture 29 Pandas File Handling: Overview

Lecture 30 Pandas File Handling: The .csv file format

Lecture 31 Pandas File Handling: The .xlsx file format

Lecture 32 Pandas File Handling: SQL-database files and Pandas DataFrame

Lecture 33 Pandas Operations & Techniques: Overview

Lecture 34 Pandas Operations & Techniques: Object Inspection

Lecture 35 Pandas Operations & Techniques: DataFrame Inspection

Lecture 36 Pandas Operations & Techniques: Column Selections

Lecture 37 Pandas Operations & Techniques: Row Selections

Lecture 38 Pandas Operations & Techniques: Conditional Selections

Lecture 39 Pandas Operations & Techniques: Scalers and Standardization

Lecture 40 Pandas Operations & Techniques: Concatenate DataFrames

Lecture 41 Pandas Operations & Techniques: Joining DataFrames

Lecture 42 Pandas Operations & Techniques: Merging DataFrames

Lecture 43 Pandas Operations & Techniques: Transpose & Pivot Functions

Lecture 44 Pandas Data Preparation I: Overview & workflow

Lecture 45 Pandas Data Preparation II: Edit DataFrame labels

Lecture 46 Pandas Data Preparation III: Duplicates

Lecture 47 Pandas Data Preparation IV: Missing Data & Imputation

Lecture 48 Pandas Data Preparation V: Data Binnings[Extra Video]

Lecture 49 Pandas Data Preparation VI: Indicator Features[Extra Video]

Lecture 50 Pandas Data Description I: Overview

Lecture 51 Pandas Data Description II: Sorting and Ranking

Lecture 52 Pandas Data Description III: Descriptive Statistics

Lecture 53 Pandas Data Description IV: Crosstabulations & Groupings

Lecture 54 Pandas Data Visualization I: Overview

Lecture 55 Pandas Data Visualization II: Histograms

Lecture 56 Pandas Data Visualization III: Boxplots

Lecture 57 Pandas Data Visualization IV: Scatterplots

Lecture 58 Pandas Data Visualization V: Pie Charts

Lecture 59 Pandas Data Visualization VI: Line plots

Section 4: Master Cluster Analysis and Unsupervised Learning

Lecture 60 Overview

Lecture 61 K-Means Cluster Analysis

Lecture 62 Auto-updated K-Means Cluster Analysis, introduction and simulation

Lecture 63 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Lecture 64 Four Hierarchical Clustering algorithms

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