Master Regression & Prediction With Pandas And Python [2024]

Master Regression & Prediction With Pandas And Python [2024]
Published 5/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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

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

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

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 59 Pandas Data Visualization VI: Line plots

Section 4: Master Regression Models for Prediction

Lecture 60 Regression, Prediction, and Supervised Learning. Section Overview (I)

Lecture 61 The Traditional Simple Regression Model (II)

Lecture 62 The Traditional Simple Regression Model (III)

Lecture 63 Some practical and useful modelling concepts (IV)

Lecture 64 Some practical and useful modelling concepts (V)

Lecture 65 Linear Multiple Regression model (VI)

Lecture 66 Linear Multiple Regression model (VII)

Lecture 69 Regression Regularization, Lasso and Ridge models (X)

Lecture 70 Decision Tree Regression models

Lecture 71 Random Forest Regression

Lecture 72 Voting Regression

Section 5: Feedforward Networks and Advanced Regression Models

Lecture 73 Overview

Lecture 75 Feedforward Multi-Layer Perceptrons for Prediction tasks

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