Master Regression & Prediction with Pandas and Python [2024]
Master Regression & Prediction with Pandas and Python [2024], available at $54.99, has an average rating of 4.9, with 75 lectures, based on 63 reviews, and has 181 subscribers.
You will learn about Master Regression, Regression analysis, 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 This course is ideal for individuals who are anyone who wants to learn to master Regression and Prediction or anyone who wants to learn to Master Python 3 from scratch or the beginner level or anyone who wants to learn to Master Python 3 and knows another programming language or 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 or anyone who wants to learn to Master the Pandas library or 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 or anyone who wants to learn advanced Data Handling and improve their capabilities and productivity It is particularly useful for anyone who wants to learn to master Regression and Prediction or anyone who wants to learn to Master Python 3 from scratch or the beginner level or anyone who wants to learn to Master Python 3 and knows another programming language or 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 or anyone who wants to learn to Master the Pandas library or 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 or anyone who wants to learn advanced Data Handling and improve their capabilities and productivity.
Enroll now: Master Regression & Prediction with Pandas and Python [2024]
Summary
Title: Master Regression & Prediction with Pandas and Python [2024]
Price: $54.99
Average Rating: 4.9
Number of Lectures: 75
Number of Published Lectures: 75
Number of Curriculum Items: 75
Number of Published Curriculum Objects: 75
Original Price: $124.99
Quality Status: approved
Status: Live
What You Will Learn
- Master Regression, Regression analysis, 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 Should Attend
- 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
Target Audiences
- 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
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, Regression analysis, 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, Regression analysis 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!
Course Curriculum
Chapter 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)
Chapter 2: Master Python for Data Handling
Lecture 1: Overview of Python for Data Handling
Lecture 2: Python Integer
Lecture 3: Python Float
Lecture 4: Python Strings I
Lecture 5: Python Strings II: Intermediate String Methods
Lecture 6: Python Strings III: DateTime Objects and Strings
Lecture 7: Overview of Python Native Data Storage Structures
Lecture 8: Python Set
Lecture 9: Python Tuple
Lecture 10: Python Dictionary
Lecture 11: Python List
Lecture 12: Overview of Python Data Transformers and Functions
Lecture 13: Python While-loop
Lecture 14: Python For-loop
Lecture 15: Python Logic Operators and conditional code branching
Lecture 16: Python Functions I: Some theory
Lecture 17: Python Functions II: create your own functions
Lecture 18: Python Object Oriented Programming I: Some theory
Lecture 19: Python Object Oriented Programming II: create your own custom objects
Lecture 20: Python Object Oriented Programming III: Files and Tables
Lecture 21: Python Object Oriented Programming IV: Recap and More
Chapter 3: Master Pandas for Data Handling
Lecture 1: Master Pandas for Data Handling: Overview
Lecture 2: Pandas theory and terminology
Lecture 3: Creating a Pandas DataFrame from scratch
Lecture 4: Pandas File Handling: Overview
Lecture 5: Pandas File Handling: The .csv file format
Lecture 6: Pandas File Handling: The .xlsx file format
Lecture 7: Pandas File Handling: SQL-database files and Pandas DataFrame
Lecture 8: Pandas Operations & Techniques: Overview
Lecture 9: Pandas Operations & Techniques: Object Inspection
Lecture 10: Pandas Operations & Techniques: DataFrame Inspection
Lecture 11: Pandas Operations & Techniques: Column Selections
Lecture 12: Pandas Operations & Techniques: Row Selections
Lecture 13: Pandas Operations & Techniques: Conditional Selections
Lecture 14: Pandas Operations & Techniques: Scalers and Standardization
Lecture 15: Pandas Operations & Techniques: Concatenate DataFrames
Lecture 16: Pandas Operations & Techniques: Joining DataFrames
Lecture 17: Pandas Operations & Techniques: Merging DataFrames
Lecture 18: Pandas Operations & Techniques: Transpose & Pivot Functions
Lecture 19: Pandas Data Preparation I: Overview & workflow
Lecture 20: Pandas Data Preparation II: Edit DataFrame labels
Lecture 21: Pandas Data Preparation III: Duplicates
Lecture 22: Pandas Data Preparation IV: Missing Data & Imputation
Lecture 23: Pandas Data Preparation V: Data Binnings [Extra Video]
Lecture 24: Pandas Data Preparation VI: Indicator Features [Extra Video]
Lecture 25: Pandas Data Description I: Overview
Lecture 26: Pandas Data Description II: Sorting and Ranking
Lecture 27: Pandas Data Description III: Descriptive Statistics
Lecture 28: Pandas Data Description IV: Crosstabulations & Groupings
Lecture 29: Pandas Data Visualization I: Overview
Lecture 30: Pandas Data Visualization II: Histograms
Lecture 31: Pandas Data Visualization III: Boxplots
Lecture 32: Pandas Data Visualization IV: Scatterplots
Lecture 33: Pandas Data Visualization V: Pie Charts
Lecture 34: Pandas Data Visualization VI: Line plots
Chapter 4: Master Regression Models for Prediction
Lecture 1: Regression, Prediction, and Supervised Learning. Section Overview (I)
Lecture 2: The Traditional Simple Regression Model (II)
Lecture 3: The Traditional Simple Regression Model (III)
Lecture 4: Some practical and useful modelling concepts (IV)
Lecture 5: Some practical and useful modelling concepts (V)
Lecture 6: Linear Multiple Regression model (VI)
Lecture 7: Linear Multiple Regression model (VII)
Lecture 8: Multivariate Polynomial Multiple Regression models (VIII)
Lecture 9: Multivariate Polynomial Multiple Regression models (VIIII)
Lecture 10: Regression Regularization, Lasso and Ridge models (X)
Lecture 11: Decision Tree Regression models (XI)
Lecture 12: Random Forest Regression (XII)
Lecture 13: Voting Regression (XIII)
Chapter 5: Feedforward Networks and Advanced Regression Models
Lecture 1: Overview
Lecture 2: Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron
Lecture 3: Feedforward Multi-Layer Perceptrons for Prediction tasks
Instructors
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Henrik Johansson
Instructor at Udemy
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 3 votes
- 5 stars: 60 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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