Building Regression Models with scikit-learn from Pluralsight

This course covers important techniques such as ordinary least squares regression, moving on to lasso, ridge, and Elastic Net, and advanced techniques such as Support Vector Regression and Stochastic Gradient Descent Regression.

Regression is one of the most widely used modeling techniques and is much beloved by everyone ranging from business professionals to data scientists. Using scikit-learn, you can easily implement virtually every important type of regression with ease.

In this course, Building Regression Models with scikit-learn, you will gain the ability to enumerate the different types of regression algorithms and correctly implement them in scikit-learn.

First, you will learn what regression seeks to achieve, and how the ubiquitous Ordinary Least Squares algorithm works under the hood. Next, you will discover how to implement other techniques that mitigate overfittings such as Lasso, Ridge and Elastic Net regression. You will then understand other more advanced forms of regression, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent. Finally, you will round out the course by understanding the hyperparameters that these various regression models possess, and how these can be optimized. When you are finished with this course, you will have the skills and knowledge to select the correct regression algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.

What's inside

Syllabus

Course Overview

Understanding Linear Regression as a Machine Learning Problem

Building a Simple Linear Model

Building Regularized Regression Models

Performing Regression Using Multiple Techniques

Hyperparameter Tuning for Regression Models

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Tailored to data scientists and business specialists alike, inclusive of both novices and seasoned experts

Includes principle regression algorithms and their applications, from basic to advanced, exploring Ordinary Least Squares (OLS), Lasso, Ridge, Elastic Net, Support Vector Machines, Decision Trees, and Stochastic Gradient Descent

Hands-on examples and interactive materials provide experiential learning opportunities

Primarily focuses on scikit-learn library, ensuring immediate applicability in real-world projects

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Regression Models with scikit-learn with these activities:

Review of Ordinary Least Squares Regression

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Refresher on the fundamental concepts of ordinary least squares regression.

Browse courses on Linear Regression

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Recall the assumptions and mathematical formulation of OLS regression.

Review Linear Algebra Concepts

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Refresher on linear algebra concepts essential for understanding regression models.

Browse courses on Linear Algebra

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Revisit basic concepts of matrices, vectors, and vector spaces.

Kaggle Tutorial on Regression

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Learn from a case study-based tutorial to apply regression techniques in a practical setting.

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Review Kaggle beginner's tutorial on regression.
Practice implementing different regression models using the provided dataset.

Five other activities

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Participate in a Study Group

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Engage with peers to reinforce understanding of concepts and problem-solving techniques.

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Join or form a study group with other learners taking the course.
Meet regularly to discuss course material, work through problems, and share insights.

Practice gradient descent on simple datasets

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Reinforce the concept of gradient descent and its application.

Browse courses on Gradient Descent

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Implement gradient descent on a simple linear regression model.
Experiment with different learning rates and batch sizes.

Regression Model Evaluation

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More practice with evaluating regression models and selecting the best model.

Browse courses on Model Evaluation

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Implement metrics for evaluating regression models, such as R-squared and mean squared error.
Train and evaluate multiple regression models using cross-validation.
Compare the performance of different regression models and select the best model for a given dataset.

Develop a Regression Model for a Business Case

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Apply regression techniques to address a real-world business problem and demonstrate understanding of the concepts learned in the course.

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Identify a business problem that can be solved using regression analysis.
Collect and preprocess data related to the problem.
Train and evaluate different regression models using the data.
Deploy the best-performing model to make predictions.

Elements of Statistical Learning

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Expand knowledge of regression models and statistical learning concepts.

View The Elements of Statistical Learning: Data... on Amazon

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Read selected chapters related to regression.
Work through the exercises and examples provided in the book.

Career center

Learners who complete Building Regression Models with scikit-learn will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists develop, build, and maintain analytical models and algorithms to extract insights and solve complex problems. These professionals use their knowledge of statistics, modeling, and programming to analyze data, uncover patterns, and make predictions. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which are commonly used in data science.

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Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models and algorithms. These professionals use their knowledge of statistics, modeling, and programming to develop and implement solutions to complex problems. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which are commonly used in machine learning.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. They use their knowledge of statistics, modeling, and programming to develop and implement solutions to complex problems. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which are commonly used in statistics.

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Data Analyst

Data Analysts translate raw data into actionable insights for businesses. Ultimately, they tell stories from the data, using statistics and various analytical techniques. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which are commonly used in data analysis.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze and forecast financial data. They use their knowledge of statistics, modeling, and programming to develop and implement solutions to complex problems. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which are commonly used in quantitative analysis.

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Economist

Economists use data and analytics to understand economic trends and forecast economic growth. They work with stakeholders to understand economic needs, and then use their knowledge of data and analytics to develop and implement economic policies. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Market Researcher

Market Researchers collect and analyze data to understand market trends and consumer behavior. They use their knowledge of data and analytics to develop and implement solutions to marketing problems. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Business Analyst

Business Analysts use data and analytics to identify and solve business problems. They work with stakeholders to understand business needs, and then use their knowledge of data and analytics to develop and implement solutions. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Operations Research Analyst

Operations Research Analysts use data and analytics to improve business processes. They work with stakeholders to understand business needs, and then use their knowledge of data and analytics to develop and implement solutions. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Biostatistician

Biostatisticians use data and analytics to understand health trends and forecast disease outbreaks. They work with stakeholders to understand health needs, and then use their knowledge of data and analytics to develop and implement health policies. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Risk Analyst

Risk Analysts use data and analytics to identify and manage risks. They work with stakeholders to understand business needs, and then use their knowledge of data and analytics to develop and implement risk management strategies. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with stakeholders to understand customer needs, and then use their knowledge of data and analytics to develop and implement product strategies. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work with stakeholders to understand business needs, and then use their knowledge of programming and software development to develop and implement software solutions. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Data Architect

Data Architects design and implement data management solutions. They work with stakeholders to understand business needs, and then use their knowledge of data management and architecture to develop and implement data management strategies. Building Regression Models with scikit-learn may be useful in this career as it can help build a foundation in statistical modeling and regression techniques, which can be used to analyze data and identify trends.

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Database Administrator

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See salaries and explore the career path for Database Administrator