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Before you enroll

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Your receipt

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Classify Data and Make Predictions: KNN and SVM

Solve classification problems with two popular supervised machine learning algorithms: k-nearest neighbors and Support Vector Machines.
Who Is This For?

Data Professionals interested in building on their foundational supervised ML knowledge by learning new models for classifying data and making predictions.

Any prerequisites?

Machine Learning

• Knowledge of linear and logistic regression and basic principles of machine learning.

• Familiarity with supervised learning algorithms.

• Identifying regression vs. classification ML problems.

• Model evaluation methods including train/test split and statistics such as mean absolute error, accuracy, precision, recall, and F1 score.

• The concept of overfitting and underfitting.

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Python

• Strong familiarity with Python, including data structures, loops, functions, code debugging, and reading error messages.

• Experience with data manipulation using the Pandas library.

What will I be able to do after this Course?

By the end of the course, you’ll know how to train, test, and tune KNN and SVM models to solve classification and prediction problems, and reliably choose the right algorithm for the job.

Reimbursement FAQ

Course Overview

Sprint 1: K-Nearest Neighbors
Learn how to implement an ML pipeline using a k-nearest neighbors model and understand the key concepts behind the KNN algorithm. You then apply what you’ve learned to help a bank identify and reach out to customers that are likely to cancel their credit cards.
Sprint 2: Support Vector Machines
Learn how to implement a machine learning pipeline using a Support Vector Machine model and understand the key components of an SVM. You’ll apply what you’ve learned by trying a new approach to helping the bank identify customers that are likely to cancel their credit cards and comparing the results.
Sprint 3: Comparing Models and Making Predictions
Learn about the strengths and weaknesses of KNN and SVM models and compare their performance. You’ll apply what you’ve learned by helping the bank run a promotional campaign, identifying current at risk customers and writing a summary of how they differ from the average customer.

What’s in a Modal course?

1:1 Coaching
Receive personal guidance, instruction, and motivation from real, practicing industry experts.
Real-world simulations
Practical coursework blends simulated and real-world projects, ensuring you are building job-ready skills.
Integrated code editor
In-browser coding environment mitigates challenges while enabling paired programming and inline feedback.
Structure & flexibility
Engage with content when your schedule allows. Our assignments and deadlines help you stay on track and our coaches keep you accountable.
Individual guidance
Courses for a variety of career goals, skill needs, and company objectives, ensuring learning is both relevant and productive.
Capstones projects
Challenging and satisfying capstone projects allow you to demonstrate the skills you’ve learned, while reinforcing collaboration and business skills.

Meet our coaches

Linda Liu
Director, Data Science & Analytics

Working with the learners makes it an incredibly rewarding journey. The shared excitement and collaborative growth highlight the entire fulfilling coaching experience!

Nataliia Maksimova
Director, Business Intelligence

It's incredibly inspiring to introduce people to the fascinating world of data. Sharing my passion for data and showing that it's not just dry numbers but a creative field where you can grow and innovate is deeply rewarding.

Udit Mehrotra
Senior Data Scientist

Seeing the growth in my learners is not only heartening but also assuring because I know I had a significant role to play in shaping their journey.

Interested in buying multiple seats for your team?
Contact us