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Master Regression & Prediction with Pandas and Python [2024]

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Henrik Johansson

32:06:02

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  • 1 - Introduction.mp4
    18:00
  • 2 - Setup of the Anaconda Cloud Notebook.mp4
    14:03
  • 3 - Download and installation of the Anaconda Distribution optional.mp4
    21:05
  • 4 - The Conda Package Management System optional.mp4
    35:00
  • 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
  • 24 - Python Object Oriented Programming III Files and Tables.mp4
    27:17
  • 25 - Python Object Oriented Programming IV Recap and More.mp4
    58:21
  • 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
  • 41 - Pandas Operations Techniques Joining DataFrames.mp4
    19:30
  • 42 - Pandas Operations Techniques Merging DataFrames.mp4
    30:48
  • 43 - Pandas Operations Techniques Transpose Pivot Functions.mp4
    34:31
  • 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
  • 49 - Pandas Data Preparation VI Indicator Features Extra Video.mp4
    33:01
  • 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
  • Files.zip
  • 60 - Regression Prediction and Supervised Learning Section Overview I.mp4
    10:15
  • 61 - The Traditional Simple Regression Model II.mp4
    35:08
  • 62 - The Traditional Simple Regression Model III.mp4
    38:00
  • 63 - Some practical and useful modelling concepts IV.mp4
    13:01
  • 64 - Some practical and useful modelling concepts V.mp4
    13:01
  • 65 - Linear Multiple Regression model VI.mp4
    57:00
  • 66 - Linear Multiple Regression model VII.mp4
    36:24
  • 67 - Multivariate Polynomial Multiple Regression models VIII.mp4
    10:13
  • 68 - Multivariate Polynomial Multiple Regression models VIIII.mp4
    01:06:05
  • 69 - Regression Regularization Lasso and Ridge models X.mp4
    01:29:52
  • 70 - Decision Tree Regression models.mp4
    24:57
  • 71 - Random Forest Regression.mp4
    41:08
  • 72 - Voting Regression.mp4
    32:00
  • Files.zip
  • 73 - Overview.mp4
    02:45
  • 74 - Artificial Neural Networks Feedforward Networks and the MultiLayer Perceptron.mp4
    19:10
  • 75 - Feedforward MultiLayer Perceptrons for Prediction tasks.mp4
    28:50

    • Description

    Learn to Master Regression and Prediction with Pandas and Python for Data Science and Machine Learning

    What You'll Learn?

    • Master Regression and Prediction both in theory and practice
    • Master Regression models from simple Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression models
    • Use Machine Learning Automatic Model Creation and Feature Selection
    • Use Regularization of Regression models with Lasso Regression and Ridge Regression
    • Use Decision Tree, Random Forest, and Voting Regression models
    • Use Feedforward Multilayer Networks and Advanced Regression model Structures
    • Use effective advanced Residual analysis and tools to judge models goodness-of-fit plus residual distributions
    • Use the Statsmodels and Scikit-learn libraries for Regression 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 to handle all aspects of creating, changing, modifying, and selecting Data from a Pandas D
    • 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
    • [Bonus] 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

    Who is this for?

  • anyone who wants to learn to master Regression and Prediction
  • anyone who wants to learn to Master Python 3 from scratch or the beginner level
  • anyone who wants to learn to Master Python 3 and knows another programming language
  • anyone who wants to reach the Master/intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
  • anyone who wants to learn to Master the Pandas library
  • anyone who wants to learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
  • anyone 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 Regression & Prediction with Pandas and Python!

    This three-in-one master class video course will teach you to master Regression, Prediction, Python 3, Pandas 2 + 3, and advanced Data Handling.

    You will learn to master Regression and Prediction with a large number of advanced Regression techniques for purposes of Prediction and Automatic Model Creation or so-called true machine intelligence or AI. You will learn to handle advanced model structures for prediction tasks.

    Python 3 is one of the most popular and useful programming languages in the world, and Pandas 2 and future version 3 is the most powerful, efficient, and useful Data Handling library in existence.

    You will learn to master Python's native building blocks and powerful object-oriented programming. You will design your own advanced constructions of Python’s building blocks and execute detailed Data Handling tasks with Python.

    You will learn to master the Pandas library and to use its powerful Data Handling techniques for advanced Data Science and Machine Learning Data Handling tasks. The Pandas library is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation tool, which is directly usable with the Python programming language.


    You will learn to:

    • Master Regression and Prediction both in theory and practice

    • Master Regression models from simple linear Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression models

    • Use Machine Learning Automatic Model Creation and Feature Selection

    • Use Regularization of Regression models with Lasso Regression and Ridge Regression

    • Use Decision Tree, Random Forest, and Voting Regression models

    • Use Feedforward Multilayer Networks and Advanced Regression model Structures

    • Use effective advanced Residual analysis and tools to judge models goodness-of-fit plus residual distributions.

    • Use the Statsmodels and Scikit-learn libraries for Regression 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 to 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

    • [Bonus] 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 – golden nuggets to improve your quality of work life.

    • And much more…


    This course is an excellent way to learn to master Regression, Prediction, Python, Pandas and Data Handling!

    Regression and Prediction are the most important and used tools for modeling, AI, and forecasting. Data Handling is the process of making data useful and usable for regression, prediction, and data analysis.

    Most Data Scientists and Machine Learning Engineers spends about 80% of their working efforts and time on Data Handling tasks. Being good at Python, Pandas, and Data Handling are extremely useful and time-saving skills that functions as a force multiplier for productivity.


    This course is designed for everyone who wants to

    • learn to master Regression and Prediction

    • 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 Regression, Prediction, Python, Pandas, and Data Handling.


    Enroll now to receive 30+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!

    Who this course is for:

    • anyone who wants to learn to master Regression and Prediction
    • anyone who wants to learn to Master Python 3 from scratch or the beginner level
    • anyone who wants to learn to Master Python 3 and knows another programming language
    • anyone who wants to reach the Master/intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
    • anyone who wants to learn to Master the Pandas library
    • anyone who wants to learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
    • anyone who wants to learn advanced Data Handling and improve their capabilities and productivity

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    Category

    Python

    Regression Analysis

    Henrik Johansson
    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.
    Udemy
    Udemy
    View courses Udemy
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 75
    • duration 32:06:02
    • Release Date 2024/06/16

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