2024 Python for Linear Regression in Machine Learning

Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal

What you will learn

Analyse and visualize data using Linear Regression

Plot the graph of results of Linear Regression to visually analyze the results

Learn how to interpret and explain machine learning models

Do in-depth analysis of various forms of Linear and Non-Linear Regression

Use YellowBrick, SHAP, and LIME to interact with predictions of machine learning models

Do feature selection and transformations to fine tune machine learning models

Course contains result oriented algorithms and data explorations techniques

Description

Unlock the power of machine learning with our comprehensive Python course on linear regression. Learn how to use Python to analyze data and build predictive models. This course is perfect for beginners with little or no programming experience and experienced Python developers looking to expand their skill set.

You’ll start with the basics of Python and work your way up to advanced techniques like linear regression, which is a powerful tool for predicting future outcomes based on historical data. Along the way, you’ll gain hands-on experience with popular Python libraries such as NumPy, Pandas, and Matplotlib. We will also cover the important aspect of model optimization, interpretability, and feature selection. You will learn how to optimize your model to improve its performance and how to interpret the model results and understand the underlying relationships in your data. We will also discuss feature selection techniques that are used to identify the most essential features that drive the predictions.

By the end of the course, you’ll have a solid understanding of how to use Python to build linear regression models and make accurate predictions. You’ll also be able to apply your new skills to a wide range of machine learning and data science projects. So, if you’re ready to take your Python skills to the next level and start using machine learning to analyze and predict real-world outcomes, this is the course for you!

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What is covered in this course?

This course teaches you, step-by-step coding for Linear Regression in Python. The Linear Regression model is one of the widely used in machine learning and it is one the simplest ones, yet there is so much depth that we are going to explore in 14+ hours of videos.

Below are the course contents of this course:

Section 1- IntroductionThis section gets you to get started with the setup. Download resources files for code along.
Section 2- Python Crash CourseThis section introduces you to the basics of Python programming.
Section 3- Numpy IntroductionThis section is optional, you may skip it but I would recommend you to watch it if you are not comfortable with NumPy.
Section 4- Pandas IntroductionThis section introduces you to the basic concepts of Pandas. It will help you later in the course to catch up on the coding.
Section 5- Matplotlib IntroductionDo not skip this section. We will be using matplotlib plots extensively in the coming sections. It builds a foundation for a strong visualization of linear regression results.
Section 6- Linear Regression IntroductionWe will kick-start our Linear Regression learning. You will learn the basics of linear regression. You will see some examples so that you can understand how Linear Regression works and how to analyze the results.
Section 7- Data Preprocessing for Linear RegressionThis section is the most important section. DO NOT SKIP IT. It builds the foundation of data preprocessing for linear regression and other linear machine learning models. You will be learning, what are the techniques which we can use to improve the performance of the model. You will also learn how to check if your data is satisfying the coding of Linear Model Assumptions.
Section 8- Machine Learning Models Interpretability and ExplainerThis section teaches you how to open-up any machine learning models. Now you don’t need to treat machine learning models as black-box, you will get to learn how to open this box and how to analyze each and every component of machine learning models.
Section 9- Linear Regression Model OptimizationThis section extensively uses the knowledge of previous sections so don’t skip those. You will learn various techniques to improve model performance. We will show you how to do outliers removal and feature transformations.
Section 10- Feature Selection for Linear RegressionThis section teaches you some of the best techniques of feature selection. Feature selection reduces the model complexity and chances of model overfitting. Sometimes the model also gets trained faster but mostly depends on how many features are selected and the types of machine learning models.
Section 11- Ridge & Lasso Regression, ElasticNet, and Nonlinear RegressionThis section covers, various types of regression techniques. You will be seeing how to achieve the best accuracy by using the above techniques.

By the end of this course, your confidence will boost in creating and analyzing the Linear Regression model in Python. You’ll have a thorough understanding of how to use regression modeling to create predictive models and solve real-world business problems.

English

language

Content

Introduction

Resources Folder | DO NOT SKIP

Install Anaconda and Python 3 on Windows 10

Install Anaconda and Python 3 on Ubuntu Machine

Jupyter Notebook Shortcuts

Python Crash Course

Introduction

Data Types

Variable Assignment

String Assignment

List

Set

Tuple

Dictionary

Boolean and Comparison Operator

Logical Operator

If, Else, Elif

Loops in Python

Methods and Lambda Function

Numpy Introduction [Optional]

Introduction

Array

NaN and INF

Statistical Operations

Shape, Reshape, Ravel, Flatten

Sequence, Repetitions, and Random Numbers

Where(), ArgMax(), ArgMin()

File Read and Write

Concatenate and Sorting

Working with Dates

Pandas Introduction

Introduction

DataFrame and Series

File Reading and Writing

Info, Shape, Duplicated and Drop

Columns

NaN and Null Values

Imputation

Lambda Functions

Matplotlib Introduction

Introduction

Line Plot

Label for X-Axis and Y-Axis

Scatter Plot, Bar Plot, and Hist Plot

Box Plot

Subplot

xlim, ylim, xticks, and yticks

Pie Plot

Pie Plot Text Color

Nested Pie Plot

Labeling a Pie Plot

Bar Chart on Polar Axis

Line Plot on a Polar Axis

Scatter Plot on a Polar Axis

Integral in Calculus Plot as Area Under the Curve

Linear Regression Introduction

Regression Examples

Types of Linear Regression

Assessing the performance of the model

Bias-Variance tradeoff

What is sklearn and train-test-split

Python Package Upgrade and Import

Load Boston Housing Dataset

Dataset Analysis

Exploratory Data Analysis- Pair Plot

Exploratory Data Analysis- Hist Plot

Exploratory Data Analysis- Heatmap

Train Test Split and Model Training

How to Evaluate the Regression Model Performance

Plot True House Price vs Predicted Price

Plotting Learning Curves Part 1

Plotting Learning Curves Part 2

Machine Learning Model Interpretability- Residuals Plot

Machine Learning Model Interpretability- Prediction Error Plot

Data Preprocessing for Linear Regression

Linear Model Assumption for Linear Regression

Definitions of Linear Model Assumptions

Load Boston Dataset

Create Reference Data

Check Linear Assumption for Boston Dataset Part 1

Check Linear Assumption for Boston Dataset Part 2

Log Transformation of Variables

Types of Variable Transformations

Reciprocal Transformation

sqrt and exp Transformation

Box-Cox Transformation

Yeo-Johnson Transformation

Check Variables Normality with Histogram

Check Variables Normality with Q-Q Plot

Variable Transformation for Normality

Check Variables Homocedasticity

Variable Transformation for Homoscedasticity Part 1

Variable Transformation for Homoscedasticity Part 2

How to Check Multicolinearity

Normalization and Standardization Introduction

Normalization and Standardization Coding

Machine Learning Models Interpretability and Explainer

Machine Learning Models Interpretability

Recap

Prediction Error Plot

Residuals Plot

Explain Machine Learning Models with LIME Part 1

Explain Machine Learning Models with LIME Part 2

Explain Machine Learning Models with LIME Part 3

Machine Learning Models Explainer Summary with SHAP

Explain Machine Learning Models with Dependence Plot

Explain Machine Learning Models with The Individual Force Plot

Explain Machine Learning Models with The Collective Force Plot

Explain Machine Learning Models with Shap Heatmap

Explain Machine Learning Models with with SHAP Waterfall Plots

Explain Feature Importance in Machine Learning Models

Explain Feature Selection with SHAP

Linear Regression Model Optimization

Recap

Check Linear Model Assumptions on Selected Features

Check Linear Model Assumptions on Selected Features Part 2

Detect Outliers in Machine Learning Datasets

Outliers Visualization Plot

Outlier Detection for Normal Variables

Outlier Detection for Skewed Variables

Types of Outliers Removal Techniques

Outliers Removal by Using Feature-Engine

Model Evaluation After Removing the Outliers

Model Evaluation After Feature Transformations and Outliers Removal

Feature Selection for Linear Regression

Introduction to Feature Selection

Introduction to A Python API for Intelligent Visual Discovery

Recap

Visualization of Data with LUX API

Select Most Correlated Features with House Price Part 1

Select Most Correlated Features with House Price Part 2

Model Performance Evaluation

Remove Correlated Input Features (Multicollinearity)

Recursive Feature Elimination (RFE) Introduction

10 Recursive Feature Elimination (RFE) Coding

Increamental RFE

Exhaustive Feature Selection (EFS)

Feature Selection by Linear Regression Coefficients

Ridge & Lasso Regression, ElasticNet, and Nonlinear Regression

Introduction to Regularization

Recap

Ridge Regression

Lasso Regression

Elastic Net

Polynomial Regression

Polynomial Regression with Variable Transformations

Polynomial Regression with Feature Selection

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