Building Classification Models with scikit-learn from Pluralsight

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.

Perhaps the most ground-breaking advances in machine learning have come from applying machine learning to classification problems.

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

First, you will learn what classification seeks to achieve, and how to evaluate classifiers using accuracy, precision, recall, and ROC curves.

Next, you will discover how to implement various classification techniques such as logistic regression, and Naive Bayes classification.

You will then understand other more advanced forms of classification, 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 classification models possess, and how these can be optimized.

When you’re finished with this course, you will have the skills and knowledge to select the correct classification 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 Classification as a Machine Learning Problem

Building a Simple Classification Model

Performing Classification Using Multiple Techniques

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Read about what's good

what should give you pause

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Develops classification models with scikit-learn, a leading Python machine learning library

Covers logistic regression, Discriminant Analysis, Naive Bayes, Decision Trees, Support Vector Classification and Stochastic Gradient Descent Classification

Taught by Janani Ravi, who has extensive experience in machine learning and data science

Provides hands-on labs and interactive materials that reinforce learning

Builds a solid foundation in classification techniques for both beginners and intermediate learners

Reviews summary

Practical scikit-learn classification for data professionals

According to learners, this course offers a largely positive and hands-on introduction to building classification models using scikit-learn. Students praise its clear explanations of complex concepts and the valuable insights into evaluating classifiers with metrics like ROC curves. The practical code examples and hyperparameter tuning modules are frequently highlighted as strengths, making the course highly relevant for real-world applications. While many find the pace accessible, some more advanced students or those lacking foundational math knowledge occasionally noted that certain topics could benefit from greater depth, requiring supplementary study for a comprehensive theoretical understanding.

Some prior knowledge in math or ML enhances the learning experience.

"I struggled with this course. While the instructor is knowledgeable, the prerequisites were not clearly stated, and I felt lost without a stronger background in linear algebra and calculus."

"The pace was too fast for me in some sections, and the concepts were not always broken down sufficiently."

"It felt more like a quick tour than a deep dive."

Offers a good introduction, but advanced topics could use more depth.

"My main constructive feedback would be that some parts could benefit from a bit more depth, especially for those with a stronger background in ML."

"Good as a starting point, but not comprehensive. I often felt the need to consult external resources for deeper understanding."

"It's a good foundational course, but advanced learners might find it lacking in cutting-edge techniques or optimization strategies."

"The application part is strong, but the theory behind it could be more robust."

Provides crucial insights into optimizing model performance.

"I particularly appreciated the hands-on labs and how the course delves into hyperparameter tuning, which is crucial for real-world applications."

"The hyperparameter tuning part was also very insightful."

"The hyperparameter tuning module was also very useful."

Concepts are explained with remarkable clarity and detail.

"The instructor explains complex concepts with remarkable clarity..."

"Excellent course! The content on logistic regression and SVMs was particularly well-explained."

"The explanations are incredibly clear, and the practical exercises solidify understanding."

"I found the segment on evaluating classifiers using ROC curves and precision-recall very helpful, as these are often glossed over..."

Focuses on hands-on application of classification models.

"The instructor explains complex concepts with remarkable clarity, providing practical code examples that are easy to follow."

"The practical application using scikit-learn was invaluable."

"Fantastic course for anyone wanting to get hands-on with classification in Python. The material is up-to-date, and the code examples work flawlessly."

"The most valuable aspect for me was the practical implementation in scikit-learn."

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 Classification Models with scikit-learn with these activities:

Connect with data scientists on LinkedIn

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Building relationships with experienced data scientists can provide valuable insights and guidance.

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Create a LinkedIn profile that highlights your interest in data science
Search for data scientists in your area and send connection requests
Reach out to potential mentors and express your interest in learning from them

Read Hands-On Machine Learning with scikit-learn, Keras, and TensorFlow

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As a supplementary resource, this book will further expand students' knowledge on the scikit-learn library and other popular machine learning libraries.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read at least 3 chapters in the book

Practice logistic regression

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This activity will reinforce the student's background knowledge in logistic regression, which will be particularly helpful to prepare the student prior to the second module of the course.

Browse courses on Logistic Regression

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Go over your notes from previous course on logistic regression
Review logistic regression video tutorials
Complete at least 5 practice problems on logistic regression
Take a practice quiz on logistic regression

Five other activities

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Create a slide deck on scikit-learn classification module

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This activity will aid as a reference for students who need to refresh their understanding of the scikit-learn library.

Browse courses on scikit-learn

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Go over the official documentation for scikit-learn
Create a slide deck that includes examples of how to use the classification module
Share the slide deck with classmates

Practice Naive Bayes and Decision Tree classification

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This activity will help solidify the skills gained during the third and fourth modules of the course.

Browse courses on Naive Bayes

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Complete 10 practice problems on Naive Bayes
Complete 10 practice problems on Decision Tree
Solve a coding challenge involving both algorithms

Classification Project

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Students will be asked to implement a classification model to solve a real-world problem. This will help cement the skills learned throughout the course.

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Choose a dataset with labeled examples
Prepare the data by cleaning, normalization, and feature selection
Implement several classification algorithms and evaluate their performance
Deploy the model

Volunteer as a data scientist mentor

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This opportunity provides practical experience in the field of data science.

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Contact local organizations that offer data science mentoring programs
Commit to a regular schedule of mentoring sessions

Participate in a data science hackathon

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Hackathons offer opportunities for hands-on experience, problem-solving, and networking with other data scientists.

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Find a hackathon that aligns with your interests
Form a team or work independently
Develop a data-driven solution to the hackathon challenge

Career center

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

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. They need robust modeling skills that can be applied to a variety of problems. This course would be particularly helpful to someone in this field, as it offers instruction on various methods of building models in scikit-learn.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

A Quantitative Analyst develops and uses mathematical and statistical models to make investment decisions. This course would be particularly helpful to a Quantitative Analyst, as it offers instruction for building and interpreting statistical models. These models are foundational to the work of Quantitative Analysts.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

A Data Scientist builds models to solve real-world problems. Several concepts covered in this course are foundational to this role. This course would be particularly helpful to someone in this field, as it offers instruction for building and interpreting statistical models.

See salaries and explore the career path for Data Scientist

Actuary

An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. This course would be particularly helpful to an Actuary, as it offers instruction for building and interpreting statistical models. These models are foundational to the work of actuaries.

See salaries and explore the career path for Actuary

Systems Analyst

A Systems Analyst designs and implements computer systems. This course would be particularly helpful to a Systems Analyst who wants to work on projects involving classification. The course covers a variety of techniques that are essential for building and deploying classification models.

See salaries and explore the career path for Systems Analyst

Data Engineer

A Data Engineer builds and maintains the infrastructure that is used to store and process data. This course would be particularly helpful to a Data Engineer who wants to work on projects involving classification. The course covers a variety of techniques that are essential for building and deploying classification models.

See salaries and explore the career path for Data Engineer

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical methods to improve the efficiency of an organization. This course would be particularly helpful to an Operations Research Analyst who wants to work on projects involving classification. The course covers a variety of techniques that are essential for building and deploying classification models.

See salaries and explore the career path for Operations Research Analyst

Statistician

A Statistician uses data to help make better decisions. This course would be particularly helpful to someone in this field, as it offers instruction for building and interpreting statistical models.

See salaries and explore the career path for Statistician

Software Engineer

A Software Engineer designs, develops, and maintains software. This course would be particularly helpful to a Software Engineer who wants to work on machine learning projects. The course covers a variety of topics that are essential for building and deploying machine learning models.

See salaries and explore the career path for Software Engineer

IT Consultant

An IT Consultant helps organizations to improve their use of technology. This course would be useful to someone in this field, as it offers instruction for building and interpreting statistical models. This is an important aspect of understanding the needs of organizations and making recommendations for improvement.

See salaries and explore the career path for IT Consultant

Marketing Analyst

A Marketing Analyst uses statistical modeling and research to understand the behaviors and motivations of customers. This course would particularly be helpful to someone in this field, as it offers instruction for building and implementing robust statistical models. These models are foundational to the work of marketing analysts.

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Risk Analyst

A Risk Analyst assesses and manages risk. This course would be useful to someone in this field, as it offers instruction for building and interpreting statistical models. This is an important aspect of understanding and mitigating risk.

See salaries and explore the career path for Risk Analyst

Financial Analyst

A Financial Analyst uses data to make recommendations on investments. This course would be useful to someone in this field, as it offers instruction for building and interpreting statistical models. This is an important aspect of understanding financial trends.

See salaries and explore the career path for Financial Analyst

Business Analyst

A Business Analyst identifies areas of improvement for businesses. This course would be useful to someone in this field, as it offers instruction for building and interpreting statistical models. This is an important aspect of identifying problems and coming up with solutions for a business.

See salaries and explore the career path for Business Analyst

Data Analyst

A Data Analyst solves problems through the use of data. It is a suitable course for someone wanting to become a Data Analyst. Not only will it help with building models, but it will help with interpreting them adequately.

See salaries and explore the career path for Data Analyst