Master Regression & Prediction with Pandas and Python [2025]
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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 and eXtreme Gradient Boosting Regression 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:
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Master Regression, Regression analysis and Prediction both in theory and practice
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Master Regression models from simple linear Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression models plus XGBoost Regression.
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Use Machine Learning Automatic Model Creation and Feature Selection
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Use Regularization of Regression models with Lasso Regression and Ridge Regression
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Use Decision Tree, Random Forest, XGBoost, and Voting Regression models
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Use Feedforward Multilayer Networks and Advanced Regression model Structures
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Use effective advanced Residual analysis and tools to judge models goodness-of-fit plus residual distributions.
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Use the Statsmodels and Scikit-learn libraries for Regression supported by Matplotlib, Seaborn, Pandas, and Python
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Master Python 3 programming with Python’s native data structures, data transformers, functions, object orientation, and logic
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Use and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File Handling
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Use Python’s advanced object-oriented programming and make your own custom objects, functions and how to generalize functions
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Manipulate data and use advanced multi-dimensional uneven data structures
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Master the Pandas 2 and 3 library for Advanced Data Handling
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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
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Use file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methods
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Perform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of data
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Make advanced data descriptions and statistics with Pandas. Rank, sort, cross-tabulate, pivot, melt, transpose, and group data
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[Bonus] Make advanced Data Visualizations with Pandas, Matplotlib, and Seaborn
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Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources.
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Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
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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.
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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
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learn to master Regression and Prediction
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learn to Master Python 3 from scratch or the beginner level
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learn to Master Python 3 and knows another programming language
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reach the Master – intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
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learn to Master the Pandas library
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learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
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learn advanced Data Handling and improve their capabilities and productivity
Requirements:
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Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
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Access to a computer with an internet connection
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Programming experience is not needed and you will be taught everything you need
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The course only uses costless software
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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!
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1IntroductionVídeo Aula
This video provides an overview of the course contents, a presentation of the instructor, background information about the course, and the course curriculum
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2Setup of the Anaconda Cloud NotebookVídeo Aula
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
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3Download and installation of the Anaconda Distribution (optional)Vídeo Aula
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
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4The Conda Package Management System (optional)Vídeo Aula
This video describes the Conda Package Management System
Conda requires internet access
Video is optional
Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures
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5Overview of Python for Data HandlingVídeo Aula
This video provides an overview of "Python for data handling", teaches you some Python and Data Handling theory, and presents a table of contents for Python for Data Handling as well as some basic information about the Jupyter IDE with dynamic typing, Python programs organization, and some fundamental Python language syntax
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6Python IntegersVídeo Aula
Learn to use Python Integers
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7Python FloatsVídeo Aula
Learn to use Python Floats
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8Python StringsVídeo Aula
Learn to use Python Strings
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9Python String MethodsVídeo Aula
Learn to use some Python string methods to test, search, transform, change, and manipulate string data
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10Python Strings and DateTime ObjectsVídeo Aula
Learn to use date and time data with Python's Datetime module. Learn to calculate time durations and time event data. Learn advanced knowledge about date and time data plus how computers and Python handle datetime data
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11Overview of Python Native Data Storage StructuresVídeo Aula
This video provides an overview of the part of this section about Python's data storage abstractions, the set, tuple, dictionary, and the list
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12Python SetVídeo Aula
Learn to use Python's Set
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13Python TupleVídeo Aula
Learn to use Python's native Tuple and how to unpack Tuples
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14Python DictionaryVídeo Aula
Learn to use Python's native Dictionary
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15Python ListVídeo Aula
Learn to use Python's native List
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16Overview of Python Data Transformers and FunctionsVídeo Aula
An overview of the contents of this subpart of the section, Python's data transformers, and functions
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17Python While-loopVídeo Aula
Learn to use Python's native while-loop with some practical examples
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18Python For-loopVídeo Aula
Learn to use Python's native for-loop with some practical examples
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19Python Logic Operators and conditional code branchingVídeo Aula
Learn to use some of Python's logic operators and conditional code branching. Use your learned knowledge to edit and tailor basic descriptive statistics at a detailed level
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20Python Functions I: Some theoryVídeo Aula
This video lecture describes the theoretical advantages of Python's functions
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21Python Functions II: create your own functionsVídeo Aula
Learn practical coding with Python's functions. You are introduced to functions and basic protections for functions. You will learn how to create functions from code-examples from earlier video lectures, and you will learn how to generalize functions up to advanced uneven-multitype-object 2-dimensional list of lists.
Learn to create your own functions!
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22Python Object Oriented Programming I: Some theoryVídeo Aula
Learn Python OOP theory relevant for data handling tasks and how object-oriented data structures may affect data handling
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23Python Object Oriented Programming II: create your own custom objectsVídeo Aula
Learn to code object-oriented programming with Python, and to handle Python object-oriented code and custom objects within the ambit of data handling
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24Python Object Oriented Programming III: Files and TablesVídeo Aula
Learn to save files in Python and the practical process of converting custom Python objects to tabular form and saving these into .csv, and Excel files and to load files to Pandas Data Frames
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25Python Object Oriented Programming IV: Recap and MoreVídeo Aula
This video lecture is a recap and extension of earlier video lectures. You will assemble knowledge from earlier lectures into more powerful knowledge. You will learn to construct a tabular data form with additional calculated variables and how to use the tabular data form for plotting, etc. You will learn how Data Handling fits with advanced object-oriented program structures.
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26Master Pandas for Data Handling: OverviewVídeo Aula
This video provides and introduction and overview of this section of the video course. "Master Pandas for Data Handling" is updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version.
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27Pandas theory and terminologyVídeo Aula
Learn the fundamental concepts and language of the Pandas DataFrame, the Pandas Series, and the data or object content of a DataFrame/Series object.
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28Creating a Pandas DataFrame from scratchVídeo Aula
Learn to create Pandas DataFrame from scratch using Python and Pandas. You will learn how to create Pandas DataFrames using Python Dictionaries, Lists, and lots more.
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29Pandas File Handling: OverviewVídeo Aula
This video contains an overview of the Pandas File Handing part of this section.
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30Pandas File Handling: The .csv file formatVídeo Aula
Learn to load and save files from/to Pandas DataFrames from .csv files.
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31Pandas File Handling: The .xlsx file formatVídeo Aula
Learn to load and save files from/to Pandas DataFrames from .xlsx files and hierarchical .xlsx files.
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32Pandas File Handling: SQL-database files and Pandas DataFrameVídeo Aula
Learn to load and save files from/to Pandas Dataframes from a SQL-database file.
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33Pandas Operations & Techniques: OverviewVídeo Aula
This video contains an overview of the Pandas Operations and Techniques part of this section.
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34Pandas Operations & Techniques: Object InspectionVídeo Aula
Learn to inspect Pandas Dataframes and Dataframe content with Pandas .info() method, Python's .type() method, and more
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35Pandas Operations & Techniques: DataFrame InspectionVídeo Aula
Learn to inspect the contents of large-sized Pandas DataFrames. Learn to use the .head, .tail, and other general methods to inspect the contents of a DataFrame
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36Pandas Operations & Techniques: Column SelectionsVídeo Aula
Learn to select subsets of Columns from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
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37Pandas Operations & Techniques: Row SelectionsVídeo Aula
Learn to select subsets of Rows from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
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38Pandas Operations & Techniques: Conditional SelectionsVídeo Aula
Learn to make conditional selections of subsets from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
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39Pandas Operations & Techniques: Scalers and StandardizationVídeo Aula
Learn about Scalers, Normalization, and Standardization. Learn to use mean-correction, normalization, and zero-one unity-based normalization
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40Pandas Operations & Techniques: Concatenate DataFramesVídeo Aula
Learn to Concatenate Pandas DataFrames. Learn to use Pandas .concat() function to add DataFrames together horizontally and vertically. Learn to use the .concat() function with Inner and Outer joins
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41Pandas Operations & Techniques: Joining DataFramesVídeo Aula
Learn to join Pandas DataFrames. Learn to use Pandas DataFrames .join() method. Learn to use "left joins", "right joins", "inner joins", "outer joins", and "cross joins"
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42Pandas Operations & Techniques: Merging DataFramesVídeo Aula
Learn to merge Pandas DataFrames. Learn to use Pandas DataFrames .merge() method. Learn to use "left joins", "right joins", "inner joins", and "outer joins" to merge different DataFrames on column variables
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43Pandas Operations & Techniques: Transpose & Pivot FunctionsVídeo Aula
Learn to Transpose and Pivot Pandas DataFrames. Learn to use the transpose, pivot, pivot_table, and melt functions
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44Pandas Data Preparation I: Overview & workflowVídeo Aula
This video has an overview of the Data Preparation part of the course and includes a workflow for Data preparation or so-called data cleaning
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45Pandas Data Preparation II: Edit DataFrame labelsVídeo Aula
Learn to edit Pandas DataFrame column names, index, and index labels
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46Pandas Data Preparation III: DuplicatesVídeo Aula
Learn about Duplicates. Duplicate rows or observations may impact the quality of data products. Learn how to properly handle Duplicates with Pandas functionality
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47Pandas Data Preparation IV: Missing Data & ImputationVídeo Aula
Learn to handle Missing data and Missing values with Pandas functionality. Learn Imputation and to augment Pandas with scikit-learn to use advanced model-based imputation of missing data
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48Pandas Data Preparation V: Data Binnings [Extra Video]Vídeo Aula
Learn Data Binning with Pandas. Learn to use Administrative Data Binning, Algorithmic Data Binning, and subjective Data Binning. Learn to use Pandas .qcut() and .cut() functions.
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49Pandas Data Preparation VI: Indicator Features [Extra Video]Vídeo Aula
Learn to create Indicator Features or Dummy Features with Pandas
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50Pandas Data Description I: OverviewVídeo Aula
This video provides an overview of the part of this section about Pandas Data Description
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51Pandas Data Description II: Sorting and RankingVídeo Aula
Learn to use Pandas functions for Sorting and Ranking data
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52Pandas Data Description III: Descriptive StatisticsVídeo Aula
Learn to create useful descriptive statistics with Pandas .agg() and .describe() functions. Learn to augment Pandas functions with the powerful .apply() and .value_counts() functions
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53Pandas Data Description IV: Crosstabulations & GroupingsVídeo Aula
Learn to create crosstabulations with Pandas .crosstab() function and to use the powerful Pandas .groupby() operation. Learn to augment these functions with a selection of Pandas functionality
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54Pandas Data Visualization I: OverviewVídeo Aula
This video contains an overview of Pandas Data Visualization and gives an overview of the contents of this part of the section Master Pandas for Data Handling
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55Pandas Data Visualization II: HistogramsVídeo Aula
Learn to make Histograms with Pandas, Matplotlib, and Seaborn. You will learn to make simple Histograms, advanced Histograms, multi-dimensional Histograms, and advanced Jointgrid Histograms
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56Pandas Data Visualization III: BoxplotsVídeo Aula
Learn to make traditional and modern Boxplots with Pandas, Matplotlib, and Seaborn. You will learn to make Boxplots, Boxenplots, Violinplots, Swarmplots and to create graphs consisting of many types of boxplots
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57Pandas Data Visualization IV: ScatterplotsVídeo Aula
Learn to make scatterplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple scatterplots, advanced scatterplots, advanced multi-scatterplots, and advanced pairplots of scatterplots
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58Pandas Data Visualization V: Pie ChartsVídeo Aula
Learn to make Pie Charts with Pandas, Matplotlib, and support from Seaborn. You will learn to make Pie Charts, detailed Pie Charts, multiple Pie Charts, and how to properly use Pie Charts for effect
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59Pandas Data Visualization VI: Line plotsVídeo Aula
Learn to make Lineplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple Lineplots, advanced Lineplots, advanced Line-area plots, and advanced multidimensional Line-area plots
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60Regression, Prediction, and Supervised Learning. Section Overview (I)Vídeo Aula
This video provides an overview of this section with a table of contents. The concepts of Regression, Prediction, and Supervised Learning are described.
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61The Traditional Simple Regression Model (II)Vídeo Aula
Learn to use the traditional simple regression model, some fundamental theory and to create a regression model in a theoretically correct environment with the Scikit-learn and Statsmodels libraries
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62The Traditional Simple Regression Model (III)Vídeo Aula
Learn to use the traditional simple regression model, more fundamental theory, and tools to check and inspect model-fit-to-data, and model assumptions. Learn to create powerful residual plots with Pandas and Matplotlib, and learn to use the R-squared and Durbin-Watson statistics from the Statsmodels summary output.
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63Some practical and useful modelling concepts (IV)Vídeo Aula
Learn some practical and useful modeling concepts. Learn about Overfitting, Underfitting, and the Bias-Variance tradeoff.
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64Some practical and useful modelling concepts (V)Vídeo Aula
Learn some practical and useful modeling concepts. Learn to use Generalizations with Interpolation and extrapolation. Learn about model interpretation and learn about the fake sample or non-causality concept and about simple or advanced models.
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65Linear Multiple Regression model (VI)Vídeo Aula
Create a Linear Multiple Regression Model using correlation matrixes and heatmaps. Learn model Diagnostics and Residual Analysis using both standard package Residual plots and more advanced designed Residual plots.
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66Linear Multiple Regression model (VII)Vídeo Aula
Deepen your knowledge about Linear Multiple Regression Models. Introduction to Machine Learning Automatic Model Creation with Forward Selection and Probability-Values.
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67Multivariate Polynomial Multiple Regression models (VIII)Vídeo Aula
Learn theory about Multivariate Polynomial Regression Models and Regression terminology. Learn some theory about Automatic model creation (AI) using Machine Learning backward elimination and Regression Models
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68Multivariate Polynomial Multiple Regression models (VIIII)Vídeo Aula
Learn to code Multivariate Polynomial Multiple Regression Models combined with the Backward Elimination Feature Selection Algorithm for Machine Learning Automatic Model Creation. Learn to make Feature transformations, Residual Analysis, and some about how to plot advanced high-dimensional model predictions in low dimensional spaces, in a simplified fashion.
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69Regression Regularization, Lasso and Ridge models (X)Vídeo Aula
Learn about Regularization and to Regularize regression models using Lasso and Ridge Regression. Example regularizing an overfit Polynomial Multiple Regression Model.
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70Decision Tree Regression models (XI)Vídeo Aula
Learn Decision Tree Regression theory and to implement and regularize Decision Tree Regression models with Scikit-learn. Learn to prepare a dataset for use with Decision Tree Regression models and how to plot Decision Tree graphs and the output of Decision Tree Regression models.
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71Random Forest Regression (XII)Vídeo Aula
Learn to use Random Forest Regression / Ensembles for Prediction and Regularization. Learn to use importances for model creation and feature selection. Learn how importances change over different subsets of a dataset.
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72Voting Regression (XIII)Vídeo Aula
Learn to use the Voting Ensemble Regression model for prediction. Learn to use Voting Regression to augment and modify standard Regression models for extended functionality and advanced prediction
