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Introduction to Unsupervised Machine Learning

Apply the foundational concepts of unsupervised machine learning with Python to cluster and extract insights from data.
Who Is This For?

Data Professionals that are interested in building a foundational understanding of unsupervised machine learning and a skill set to build unsupervised ML models with Python.

Any prerequisites?

Python

• A developed understanding of syntax, data structures, Pandas DataFrames, NumPy and Matplotlib.

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Machine Learning

• Knowledge of linear and logistic regression and basic principles of machine learning, including familiarity with supervised learning algorithms, identifying regression vs. classification ML problems, understanding of loss functions and gradient descent, and model evaluation methods.

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Linear Algebra

• Knowledge of topics such as range, basis, nullspace, eigenvalues, eigenvectors, singular value decomposition, least squares.

What will I be able to do after this Course?

Apply various clustering algorithms on data, such as hierarchical and k-means clustering, to identify groupings, perform automatic customer segmentation, spot anomalies, and more.

Reimbursement FAQ

Course Overview

Sprint 1: K-Means Clustering
Learn how to perform customer segmentation using K-Means, choose an appropriate K value, and visualize and describe the results. You’ll apply what you’ve learned by helping a grocery store identify groups of customers and their spending behaviors to assist with better-targeted advertising.
Sprint 2: Hierarchical Clustering
Learn how to fit a hierarchical clustering model, and visualize, interpret, and describe the resulting clusters. You’ll apply what you’ve learned by trying a new approach to helping the grocery store identify and group customers, and comparing the results to your previous attempt.
Sprint 3: Mixed Data Types
Learn how to perform EDA of categorical and mixed data types using K-Modes and hierarchical clustering, and evaluate the results. You’ll apply what you’ve learned by testing and analyzing the performance of several unsupervised models on new data from the grocery store. Then, you’ll write a summary of your findings with recommendations for the business.

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