REG-PYTHON.AJ1
Regression Analysis with Python
Acquire your data science skills with Python regression techniques.
- Practice in 61 Hands-On Labs — nothing to install
- 10 Interactive Lessons and 52 topics mapped to the official exam objectives
- 157 Practice Test Questions
Intermediate Self-paced · 1 year access 4.6/5 (267 Reviews)
61 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
10Interactive Lessons
52Topics
61LiveLab
157Practice Test Questions
38Flashcards
38Glossary of terms
01 / Skills you'll get
What you will be able to do
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No credit card required
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.
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
Course Highlights
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10 Structured Lessons Comprehensive coverage of core course objectives
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61 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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157 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
10 Interactive Lessons · 52 topics01 Preface 4 topics +
- What this course covers
- What you need for this course
- Who this course is for
- Conventions
02 Regression – The Workhorse of Data Science 4 topics +
- Regression analysis and data science
- Python for data science
- Python packages and functions for linear models
- Summary
03 Approaching Simple Linear Regression 5 topics · 14 LiveLab +
- Defining a regression problem
- Starting from the basics
- Extending to linear regression
- Minimizing the cost function
- Summary
14 LiveLab in this lesson — see the labs panel →
04 Multiple Regression in Action 6 topics · 10 LiveLab +
- Using multiple features
- Revisiting gradient descent
- Estimating feature importance
- Interaction models
- Polynomial regression
- Summary
10 LiveLab in this lesson — see the labs panel →
05 Logistic Regression 6 topics · 10 LiveLab +
- Defining a classification problem
- Defining a probability-based approach
- Revisiting gradient descent
- Multiclass Logistic Regression
- An example
- Summary
10 LiveLab in this lesson — see the labs panel →
06 Data Preparation 6 topics · 10 LiveLab +
- Numeric feature scaling
- Qualitative feature encoding
- Numeric feature transformation
- Missing data
- Outliers
- Summary
10 LiveLab in this lesson — see the labs panel →
07 Achieving Generalization 5 topics · 7 LiveLab +
- Checking on out-of-sample data
- Greedy selection of features
- Regularization optimized by grid-search
- Stability selection
- Summary
7 LiveLab in this lesson — see the labs panel →
08 Online and Batch Learning 3 topics · 1 LiveLab +
- Batch learning
- Online mini-batch learning
- Summary
1 LiveLab in this lesson — see the labs panel →
09 Advanced Regression Methods 7 topics · 9 LiveLab +
- Least Angle Regression
- Bayesian regression
- SGD classification with hinge loss
- Regression trees (CART)
- Bagging and boosting
- Gradient Boosting Regressor with LAD
- Summary
9 LiveLab in this lesson — see the labs panel →
10 Real-world Applications for Regression Models 6 topics +
- Downloading the datasets
- A regression problem
- An imbalanced and multiclass classification problem
- A ranking problem
- A time series problem
- Summary
Hands-On Labs Our edge
61 LiveLabs- 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
- 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
- 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
- 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
- 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
- Demonstrating Mini-Batch Learning
- 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
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What are the prerequisites for this regression analysis in Python course? +
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.
How can I use regression analysis in real-world applications? +
Regression analysis is used in various fields. For example:
- Finance for stock price prediction
- marketing for sales forecasting
- Healthcare for predicting patient outcomes
Which roles can I pursue after completing this course? +
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.
What tools and libraries will I use in this course? +
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.
How can I ask questions or seek help during the 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.
Refine Your Data Science Skills
Join our hands-on course to enhance your skills in advanced regression analysis techniques using Python.
- 1 year of full access
- 61 LiveLab included
- Certificate of completion
No credit card required