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Self-paced course
Self-paced course
Machine Learning 2: Classification
Machine Learning 2: Classification
Rating 4.7
60 reviews
60 reviews
60 reviews
Course Description
This course is PART 2 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.
From there we’ll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.
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 2 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.
From there we’ll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.
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
6.0 video hours
Skills you'll learn in this course
Compare logistic, SVM, KNN & tree‑based classifiers
Handle imbalanced data with resampling & cost‑sensitive methods
Evaluate models with ROC, precision‑recall & confusion matrices
Build end‑to‑end classification pipelines ready for deployment
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
I am incredibly pleased with my experience at Maven Analytics! The courses are of exceptional quality, the guided projects are well-crafted, and the learning paths are thoughtfully designed. The Data Playground is a standout feature, providing a hands-on approach to applying what I've learned. The content is comprehensive, and the engaging format has truly elevated my skills in [mention the specific course]. I want to express my sincere gratitude to Maven Analytics for consistently delivering top-notch educational resources. I enthusiastically recommend their courses for anyone looking to excel in the world of data analysis!
Andrew Hubbard
It's been a unique approach to learning Statistics in modern times. Saves time and gives you a direct practical insight to things previously considered bookish. I like the explicit descriptions that would make even a child understand the topics completely.
Ukachi Onyema
I would have never thought that ML can be explained using Excel. But it can - and it demystifies the algorithms very well, making them very easy to understand. I took some data science / ML courses before but was very quickly discouraged by sheer volume of information that was not explained to me. It is easy to throw python scipts at a student, but it is quite difficult (or so it seemed until I took this course) to explain the basics. Sadly, majority of online courses do not believe in hand holding, which for certain subject is a must. ML is definitely one of these subjects. Thanks a lot Maven Analytics team for taking your time and preparing this very interesting course.
Ewa
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Machine Learning 2: Classification
Machine Learning 2: Classification
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 2 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.
From there we’ll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.
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 2 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.
From there we’ll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.
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 Classification
2
Intro to Classification
2
Intro to Classification
3
Classification Models
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
3
Classification Models
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
3
Classification Models
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Model Selection & Tuning
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Model Selection & Tuning
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Model Selection & Tuning
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Course Feedback & Next Steps
5
Course Feedback & Next Steps
5
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
I am incredibly pleased with my experience at Maven Analytics! The courses are of exceptional quality, the guided projects are well-crafted, and the learning paths are thoughtfully designed. The Data Playground is a standout feature, providing a hands-on approach to applying what I've learned. The content is comprehensive, and the engaging format has truly elevated my skills in [mention the specific course]. I want to express my sincere gratitude to Maven Analytics for consistently delivering top-notch educational resources. I enthusiastically recommend their courses for anyone looking to excel in the world of data analysis!
Andrew Hubbard
It's been a unique approach to learning Statistics in modern times. Saves time and gives you a direct practical insight to things previously considered bookish. I like the explicit descriptions that would make even a child understand the topics completely.
Ukachi Onyema
I would have never thought that ML can be explained using Excel. But it can - and it demystifies the algorithms very well, making them very easy to understand. I took some data science / ML courses before but was very quickly discouraged by sheer volume of information that was not explained to me. It is easy to throw python scipts at a student, but it is quite difficult (or so it seemed until I took this course) to explain the basics. Sadly, majority of online courses do not believe in hand holding, which for certain subject is a must. ML is definitely one of these subjects. Thanks a lot Maven Analytics team for taking your time and preparing this very interesting course.
Ewa
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Machine Learning 2: Classification
Machine Learning 2: Classification
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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