We may earn an affiliate commission when you visit our partners.

Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Building Machine Learning Models in Python with scikit-learn is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Udemy ★ 5.0

Master Regression and Feedforward Networks [2024]

Read Reviews → Enroll Active

Coursera ★ 5.0

Python and Rust with Linux Command Line Tools

By Duke University

Read Reviews → Enroll Active

Udemy ★ 5.0

Real-Time People Counting with YOLOv8, OpenCV, and Python

Read Reviews → Enroll Active

Building Machine Learning Models in Python with scikit-learn

Janani Ravi

This course course will help engineers and data scientists learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. No prior experience with ML needed, only basic Python programming knowledge.

The Python scikit-learn library is extremely popular for building traditional ML models i.e. those models that do not rely on neural networks.

In this course, Building Machine Learning Models in Python with scikit-learn, you will see how to work with scikit-learn, and how it can be used to build a variety of machine learning models.

The Python scikit-learn library is extremely popular for building traditional ML models i.e. those models that do not rely on neural networks.

In this course, Building Machine Learning Models in Python with scikit-learn, you will see how to work with scikit-learn, and how it can be used to build a variety of machine learning models.

First, you will learn how to use libraries for working with continuous, categorical, text as well as image data.

Next, you will get to go beyond ordinary regression models, seeing how to implement specialized regression models such as Lasso and Ridge regression using the scikit-learn libraries. Finally, in addition to supervised learning techniques, you will also understand and implement unsupervised models such as clustering using the mean-shift algorithm and dimensionality reduction using principal components analysis.

At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered and you will be extremely comfortable using the Python scikit-learn library to build and train your models. Software required: scikit-learn, Python 3.x.

This course is no longer available. Find something similar by browsing:

Machine Learning scikit-learn Python Regression Classification Clustering Dimensionality Reduction

What's inside

Syllabus

Course Overview

Processing Data with scikit-learn

Building Specialized Regression Models in scikit-learn

Building SVM and Gradient Boosting Models in scikit-learn

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops a strong foundation in traditional machine learning models using scikit-learn

Explores specialized regression models, which are commonly used in industry

Builds proficiency in unsupervised learning techniques, such as clustering and dimensionality reduction

Taught by Janani Ravi, an experienced instructor in machine learning

Requires only basic Python programming knowledge, making it accessible to beginners

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Hands-on ml with python & scikit-learn

Learners say this course provides a solid practical foundation for building machine learning models with scikit-learn in Python. It's particularly well-suited for those with basic Python knowledge but no prior ML experience, offering clear explanations and engaging hands-on labs. Many appreciate the focus on real-world application of concepts like regression, classification, and unsupervised learning. While some find the pace fast or certain theoretical explanations superficial for advanced learners, the consensus is that it delivers a robust introduction to the library. A few recent comments suggest some content might feel slightly outdated, and code comments could be improved for better clarity.

Concepts are clearly explained but some topics might feel rushed or superficial.

"The instructor explains complex concepts clearly and the coverage of regression and classification models was thorough for a beginner."

"Good overview, but I felt some concepts flew by too fast, and it assumes more prior stats knowledge than advertised."

"My main critique is that it could benefit from more advanced topics or a deeper dive into model tuning and evaluation beyond the basics."

Ideal for those new to ML, but might be too basic for experienced learners.

"Excellent course for getting started with scikit-learn. I had basic Python knowledge and found the pace just right."

"As a beginner to ML, this course was a godsend. I found it well-structured and accessible even without prior ML experience."

"I felt the course was too basic for someone with a statistics background. Great for absolute beginners, but not for me."

Emphasizes hands-on coding and real-world model building with scikit-learn.

"The hands-on labs are super helpful. Highly recommend for practical application."

"This course provided a fantastic practical foundation in scikit-learn. I particularly appreciated the focus on applying models to real-world data."

"I learned how to apply practical tools and strategies immediately to my work."

Generally clear, but some code examples lack comments; some content feels slightly outdated.

"The code examples sometimes lacked sufficient comments, making them hard to follow without constant re-watching."

"I felt some parts were a bit outdated, and the instructor's delivery could be more engaging."

"I found myself needing to supplement heavily with external resources, which made me wonder about its currency."

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 Machine Learning Models in Python with scikit-learn with these activities:

Review scikit-learn basics

Show steps

Refreshes the student's foundational understanding of scikit-learn for improved understanding of this course.

Browse courses on scikit-learn

Show steps

Review the scikit-learn documentation
Complete a scikit-learn tutorial
Practice using scikit-learn on a small dataset

Compile a scikit-learn resource list

Show steps

Assists students in organizing and expanding their scikit-learn knowledge base, providing easy access to valuable resources.

Browse courses on scikit-learn

Show steps

Gather scikit-learn resources, such as tutorials, articles, and documentation
Organize the resources into a logical structure or format
Share the resource list with other scikit-learn learners

Follow scikit-learn tutorials

Show steps

Provides students with a structured way to learn about scikit-learn's capabilities and how to use it effectively.

Browse courses on scikit-learn

Show steps

Identify a specific scikit-learn topic you want to learn more about
Find a tutorial on that topic from a reputable source
Follow the tutorial step-by-step
Apply what you learned to a small dataset

Four other activities

Expand to see all activities and additional details

Show all seven activities

Complete scikit-learn exercises

Show steps

Provides students with hands-on practice using scikit-learn, reinforcing their understanding and improving their skills.

Browse courses on scikit-learn

Show steps

Find a set of scikit-learn exercises or problems
Solve the exercises or problems using scikit-learn
Review your solutions and identify areas for improvement

Mentor junior scikit-learn learners

Show steps

Provides students with an opportunity to reinforce their understanding of scikit-learn while helping others, fostering a sense of community and support.

Browse courses on scikit-learn

Show steps

Identify a junior scikit-learn learner who needs help
Offer your assistance and provide guidance on scikit-learn concepts and usage
Review their work and provide constructive feedback
Encourage them to ask questions and seek further support when needed

Build a scikit-learn project

Show steps

Allows students to apply their scikit-learn skills to a real-world problem, deepening their understanding and showcasing their abilities.

Browse courses on scikit-learn

Show steps

Identify a problem or dataset that you can use scikit-learn to solve
Design and implement a solution using scikit-learn
Evaluate the performance of your solution
Document your project and share it with others

Participate in a scikit-learn competition

Show steps

Challenges students to apply their scikit-learn skills in a competitive environment, fostering innovation and problem-solving abilities.

Browse courses on scikit-learn

Show steps

Find a scikit-learn competition that aligns with your interests and skill level
Form a team or work individually on the competition
Develop and implement a solution using scikit-learn
Submit your solution and compete against other teams or individuals

Career center

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

Machine Learning Engineer

Machine Learning Engineers design, build, and deploy machine learning models. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring machine learning engineers. The course's focus on practical skills, such as building specialized regression models and implementing clustering and dimensionality reduction, is particularly relevant to this role.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning and other techniques to extract insights from data. This course provides a comprehensive overview of machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring data scientists. The course's focus on building specialized regression models, SVM and gradient boosting models, and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts, also known as "quants," use mathematical and statistical modeling, including machine learning, to analyze and predict financial data. This course provides a strong foundation in these techniques, making it a valuable resource for aspiring quants. The course's focus on building machine learning models in Python with scikit-learn is particularly relevant to this role, as Python and scikit-learn are widely used in the financial industry.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts use data to solve business problems. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring data analysts. The course's focus on practical skills, such as building specialized regression models and implementing clustering and dimensionality reduction, is particularly relevant to this role.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians use mathematical and statistical models to analyze data and draw conclusions. This course provides a strong foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring statisticians. The course's focus on building specialized regression models, SVM and gradient boosting models, and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts use data to make investment decisions. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring financial analysts. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Financial Analyst

Software Developer

Software Developers design, build, and maintain software applications. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for software developers who want to incorporate machine learning into their applications. The course's focus on practical skills, such as building specialized regression models and implementing clustering and dimensionality reduction, is particularly relevant to this role.

See salaries and explore the career path for Software Developer

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring operations research analysts. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Operations Research Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring actuaries. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Actuary

Urban Planner

Urban Planners use data to make decisions about land use and development. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring urban planners. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Urban Planner

Market Researcher

Market Researchers use data to understand consumer behavior. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring market researchers. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Market Researcher

Biostatistician

Biostatisticians use mathematical and statistical models to analyze biological data. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring biostatisticians. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Biostatistician

Epidemiologist

Epidemiologists use data to investigate the causes of disease. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring epidemiologists. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Epidemiologist

Health Policy Analyst

Health Policy Analysts use data to make decisions about health care. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring health policy analysts. The course's focus on building specialized regression models and implementing clustering and dimensionality reduction is particularly relevant to this role.

See salaries and explore the career path for Health Policy Analyst

Business Analyst

Business Analysts use data to identify and solve business problems. This course provides a foundation in machine learning modeling in Python with scikit-learn, making it a valuable resource for aspiring business analysts. The course's focus on practical skills, such as building specialized regression models and implementing clustering and dimensionality reduction, is particularly relevant to this role.

See salaries and explore the career path for Business Analyst

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Building Machine Learning Models in Python with scikit-learn.

Python Machine Learning

Save

Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, from basic concepts to advanced techniques, and includes numerous hands-on examples using scikit-learn.

Top Active Alternatives

Building Machine Learning Models in Python with scikit-learn

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Hands-on ml with python & scikit-learn

Activities

Career center

Reading list

Share

Similar courses

Pluralsight Buyer Protection & Guarantee

10-Day Free Trial

Cancel Anytime (Non-Refundable Once Billed)

Verified Academic & Industry Partner