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Python Regression Analysis: Essential Training

Acquire your data science skills with Python regression techniques.

(REG-PYTHON.AJ1) / ISBN : 978-1-61691-688-6
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About This Course

This Regression Analysis with Python course will teach you how to apply regression techniques to solve real-world data problems. You’ll start with the basics of regression analysis and gradually move to advanced methods, learning how to use Python’s libraries. By the end, you’ll be well-prepared to take on any daunting data analysis tasks and decode raw data bravely.

Skills You’ll Get

Learn how to build and interpret Python linear regression models for making data-driven decisions Develop data manipulation skills to organize, monitor, and analyze large datasets  Analyze relationships between multiple variables and improve your predictive modeling skills  Broaden your data science toolkit to tackle classification problems Improve the quality of your data to lead to more accurate and reliable models  Learn techniques to prevent overfitting to make sure your models perform well on new, unseen data  Adapt to different data sizes and learning needs to handle various data scenarios quickly Explore data analysis regression Python methods like Bayesian and tree-based models

Download Course Outline

1

Preface

  • What this course covers
  • What you need for this course
  • Who this course is for
  • Conventions
2

Regression – The Workhorse of Data Science

  • Regression analysis and data science
  • Python for data science
  • Python packages and functions for linear models
  • Summary
3

Approaching Simple Linear Regression

  • Defining a regression problem
  • Starting from the basics
  • Extending to linear regression
  • Minimizing the cost function
  • Summary
4

Multiple Regression in Action

  • Using multiple features
  • Revisiting gradient descent
  • Estimating feature importance
  • Interaction models
  • Polynomial regression
  • Summary
5

Logistic Regression

  • Defining a classification problem
  • Defining a probability-based approach
  • Revisiting gradient descent
  • Multiclass Logistic Regression
  • An example
  • Summary
6

Data Preparation

  • Numeric feature scaling
  • Qualitative feature encoding
  • Numeric feature transformation
  • Missing data
  • Outliers
  • Summary
7

Achieving Generalization

  • Checking on out-of-sample data
  • Greedy selection of features
  • Regularization optimized by grid-search
  • Stability selection
  • Summary
8

Online and Batch Learning

  • Batch learning
  • Online mini-batch learning
  • Summary
9

Advanced Regression Methods

  • Least Angle Regression
  • Bayesian regression
  • SGD classification with hinge loss
  • Regression trees (CART)
  • Bagging and boosting
  • Gradient Boosting Regressor with LAD
  • Summary
10

Real-world Applications for Regression Models

  • Downloading the datasets
  • A regression problem
  • An imbalanced and multiclass classification problem
  • A ranking problem
  • A time series problem
  • Summary

Approaching Simple Linear Regression

  • Creating a One-Column Matrix Structure
  • Visualizing the Distribution of Errors
  • Plotting a Normal Distribution Graph
  • Plotting a Scatterplot
  • Standardizing a Variable
  • Showing Regression Analysis Parameters
  • Showing the Summary of Regression Analysis
  • Printing the Residual Sum of Squared Errors
  • Plotting Standardized Residuals
  • Predicting with a Regression Model
  • Regressing with Scikit-learn
  • Using the fmin Minimization Procedure
  • Finding Mean and Median
  • Obtaining the Inverse of a Matrix

Multiple Regression in Action

  • Printing Eigenvalues
  • Visualizing the Correlation Matrix
  • Obtaining the Correlation Matrix
  • Standardizing Using the Scikit-learn Preprocessing Module
  • Printing Standardized Coefficients
  • Obtaining the R-squared Baseline
  • Recording Coefficient of Determination Using R-squared
  • Reporting All R-squared Increment Above 0.03
  • Representing LSTAT Using the Scatterplot
  • Testing Degree of a Polynomial

Logistic Regression

  • Creating a Dummy Dataset
  • Obtaining a Classification Report
  • Representing a Confusion Matrix Using Heatmap
  • Creating a Confusion Matrix
  • Plotting the sigmoid Function
  • Fitting a Multiple Linear Regressor
  • Creating and Fitting a Logistic Regressor Classifier
  • Obtaining the Feature Vector and its Original and Predicted Labels
  • Visualizing Multiclass Logistic Regressor
  • Creating a Dummy Four-Class Dataset

Data Preparation

  • Centering the Variables
  • Demonstrating the Logistic Regression
  • Analyzing Qualitative Data Using Logit
  • Transforming Qualitative Data
  • Using LabelBinarizer
  • Using the Hashing Trick
  • Obtaining Residuals
  • Replacing Missing Values With the Mean Value
  • Representing Outliers Among Predictors
  • Showing Outliers

Achieving Generalization

  • Splitting a Dataset
  • Bootstrapping a Dataset
  • Applying Third-Degree Polynomial Expansion
  • Plotting the Distribution of Scores
  • Demonstrating Working of Recursive Elimination
  • Implementing L2 Regularization
  • Performing Random Grid Search

Online and Batch Learning

  • Demonstrating Mini-Batch Learning

Advanced Regression Methods

  • Obtaining LARS Coefficients
  • Using Bayesian Regression
  • Using the SGDClassifier Class With the hinge Loss
  • Implementing SVR
  • Implementing CART
  • Implementing Random Forest Regressor
  • Implementing Bagging
  • Implementing Boosting
  • Implementing Gradient Boosting Regressor with LAD

Any questions?
Check out the FAQs

Find answers to the most pressing questions about our Python regression course here. 

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You should have a basic understanding of Python programming, data structures, and statistical concepts. Familiarity with libraries such as NumPy and Pandas will be beneficial.

Regression analysis is used in various fields. For example: 

  • Finance for stock price prediction 
  • marketing for sales forecasting 
  • Healthcare for predicting patient outcomes

After completing this regression analysis in Python course, you’ll have the skills to pursue a promotion or a new senior role. Career opportunities include roles such as Data Analyst, Data Scientist, Machine Learning Engineer, and Business Analyst. 

You will use Python along with libraries like NumPy, Pandas, Matplotlib, and Scikit-learn. These tools are essential for performing data manipulation, analysis, and visualization tasks covered in this course. 

To seek help or ask questions, you can buy an AI Tutor to assist you throughout the course or you can contact our support team at support@ucertify.com. 

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