REG-PYTHON.AJ1
Regression Analysis with Python
Acquire your data science skills with Python regression techniques.
- Practice in 61 Laboratorios prácticos — nothing to install
- 10 Lecciones interactivas y 52 topics mapped to the official exam objectives
- 157 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso 4.6/5 (267 Revisar)
61 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
10Lecciones interactivas
52Topics
61Laboratorio en vivo
157Preguntas del examen de práctica
38Tarjetas didácticas
38Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
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 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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61 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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157 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
10 Lecciones interactivas · 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 Laboratorio en vivo +
- Defining a regression problem
- Starting from the basics
- Extending to linear regression
- Minimizing the cost function
- Summary
14 Laboratorio en vivo in this lesson — see the labs panel →
04 Multiple Regression in Action 6 topics · 10 Laboratorio en vivo +
- Using multiple features
- Revisiting gradient descent
- Estimating feature importance
- Interaction models
- Polynomial regression
- Summary
10 Laboratorio en vivo in this lesson — see the labs panel →
05 Logistic Regression 6 topics · 10 Laboratorio en vivo +
- Defining a classification problem
- Defining a probability-based approach
- Revisiting gradient descent
- Multiclass Logistic Regression
- An example
- Summary
10 Laboratorio en vivo in this lesson — see the labs panel →
06 Data Preparation 6 topics · 10 Laboratorio en vivo +
- Numeric feature scaling
- Qualitative feature encoding
- Numeric feature transformation
- Missing data
- Outliers
- Summary
10 Laboratorio en vivo in this lesson — see the labs panel →
07 Achieving Generalization 5 topics · 7 Laboratorio en vivo +
- Checking on out-of-sample data
- Greedy selection of features
- Regularization optimized by grid-search
- Stability selection
- Summary
7 Laboratorio en vivo in this lesson — see the labs panel →
08 Online and Batch Learning 3 topics · 1 Laboratorio en vivo +
- Batch learning
- Online mini-batch learning
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Advanced Regression Methods 7 topics · 9 Laboratorio en vivo +
- Least Angle Regression
- Bayesian regression
- SGD classification with hinge loss
- Regression trees (CART)
- Bagging and boosting
- Gradient Boosting Regressor with LAD
- Summary
9 Laboratorio en vivo 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
Laboratorios prácticos Our edge
61 Laboratorio en vivos- Creating a One-Column Matrix Structure
- Visualizing the Distribution of Errors
- Trazar un gráfico de distribución normal
- Trazar un diagrama de dispersión
- 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
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
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 año de acceso completo
- 61 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito