Expires in:
Expires in:
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Self-paced course
Self-paced course
Machine Learning 4: Unsupervised Learning
Machine Learning 4: Unsupervised Learning
Rating 4.5
95 reviews
95 reviews
95 reviews
Course Description
This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of Machine Learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
We’ll start by reviewing the Machine Learning landscape, exploring the differences between supervised and unsupervised learning, and introducing several of the most common unsupervised techniques: cluster analysis, association mining, outlier detection, and dimensionality reduction.
Throughout the course, we’ll focus on breaking down each concept in plain and simple language to help you build an intuition for how these models actually work, from k-means and apriori to outlier detection, principal component analysis, and more.
As always, we’ll introduce unique demos and real-world case studies to help solidify key concepts along the way. You’ll see how k-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Course Description
This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of Machine Learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
We’ll start by reviewing the Machine Learning landscape, exploring the differences between supervised and unsupervised learning, and introducing several of the most common unsupervised techniques: cluster analysis, association mining, outlier detection, and dimensionality reduction.
Throughout the course, we’ll focus on breaking down each concept in plain and simple language to help you build an intuition for how these models actually work, from k-means and apriori to outlier detection, principal component analysis, and more.
As always, we’ll introduce unique demos and real-world case studies to help solidify key concepts along the way. You’ll see how k-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Course Content
5.25 video hours
Skills you'll learn in this course
Implement clustering algorithms like k‑means & DBSCAN in Python
Detect anomalies & customer segments using unsupervised methods
Reduce dimensions with PCA & autoencoders for clear visuals
Link discovered patterns to actionable business strategies
Meet your instructors
Josh MacCarty
Lead Machine Learning Instructor
Josh brings over a decade of applied Machine Learning experience to the Maven team, specializing in forecasting, predictive modeling, natural language processing, cluster analysis, and pricing optimization. He has a Bachelor's degree in Economics and was a Graduate Fellow for his Master's degree in Global Political Economy.
Student reviews
My experience with Maven Analytics as been fantastic! The completion of their Machine Learning courses and learning path marks a significant achievement in my personal journey. The courses were not only comprehensive but also incredibly engaging, making the learning process enjoyable every step of the way. Maven Analytics' commitment to providing top-tier educational resources shines through in every aspect of their platform. I'm grateful for the invaluable skills I've gained and the confidence I've built in tackling complex machine learning concepts. Thank you, Maven Analytics, for empowering learners like me to achieve our goals and thrive in the world of data analysis!
Andrew Hubbard
I am to have completely gone through the machine learning course. I hope to move unto where the statistical analysis tools learnt here would be fully utilized in both excel and python. I love the very simplified descriptions of topics here, making it look much less mathematical.
Ukachi F. Onyema
Great Machine Learning Courses!! Great for anyone interested in learning more about profiling, classification, regression & unsupervised learning and so much more.. Check it out!!! Thank you Maven Analytics & Josh MacCarty
Sean Tom Ryan (STR)
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Machine Learning 4: Unsupervised Learning
Machine Learning 4: Unsupervised Learning
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
Course Description
This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of Machine Learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
We’ll start by reviewing the Machine Learning landscape, exploring the differences between supervised and unsupervised learning, and introducing several of the most common unsupervised techniques: cluster analysis, association mining, outlier detection, and dimensionality reduction.
Throughout the course, we’ll focus on breaking down each concept in plain and simple language to help you build an intuition for how these models actually work, from k-means and apriori to outlier detection, principal component analysis, and more.
As always, we’ll introduce unique demos and real-world case studies to help solidify key concepts along the way. You’ll see how k-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Course Description
This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of Machine Learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
We’ll start by reviewing the Machine Learning landscape, exploring the differences between supervised and unsupervised learning, and introducing several of the most common unsupervised techniques: cluster analysis, association mining, outlier detection, and dimensionality reduction.
Throughout the course, we’ll focus on breaking down each concept in plain and simple language to help you build an intuition for how these models actually work, from k-means and apriori to outlier detection, principal component analysis, and more.
As always, we’ll introduce unique demos and real-world case studies to help solidify key concepts along the way. You’ll see how k-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Curriculum
1
Getting Started
1
Getting Started
1
Getting Started
2
Intro to Unsupervised ML
2
Intro to Unsupervised ML
2
Intro to Unsupervised ML
3
Clustering & Segmentation
3
Clustering & Segmentation
3
Clustering & Segmentation
4
Association Mining
4
Association Mining
4
Association Mining
5
Outlier Detection
5
Outlier Detection
5
Outlier Detection
6
Dimensionality Reduction
6
Dimensionality Reduction
6
Dimensionality Reduction
7
Course Feedback & Next Steps
7
Course Feedback & Next Steps
7
Course Feedback & Next Steps
Meet your instructors
Josh MacCarty
Lead Machine Learning Instructor
Josh brings over a decade of applied Machine Learning experience to the Maven team, specializing in forecasting, predictive modeling, natural language processing, cluster analysis, and pricing optimization. He has a Bachelor's degree in Economics and was a Graduate Fellow for his Master's degree in Global Political Economy.
Student reviews
My experience with Maven Analytics as been fantastic! The completion of their Machine Learning courses and learning path marks a significant achievement in my personal journey. The courses were not only comprehensive but also incredibly engaging, making the learning process enjoyable every step of the way. Maven Analytics' commitment to providing top-tier educational resources shines through in every aspect of their platform. I'm grateful for the invaluable skills I've gained and the confidence I've built in tackling complex machine learning concepts. Thank you, Maven Analytics, for empowering learners like me to achieve our goals and thrive in the world of data analysis!
Andrew Hubbard
I am to have completely gone through the machine learning course. I hope to move unto where the statistical analysis tools learnt here would be fully utilized in both excel and python. I love the very simplified descriptions of topics here, making it look much less mathematical.
Ukachi F. Onyema
Great Machine Learning Courses!! Great for anyone interested in learning more about profiling, classification, regression & unsupervised learning and so much more.. Check it out!!! Thank you Maven Analytics & Josh MacCarty
Sean Tom Ryan (STR)
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Machine Learning 4: Unsupervised Learning
Machine Learning 4: Unsupervised Learning
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
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