One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use R and RStudio to run these algorithms and communicate the results using R notebooks.
Machine Learning Algorithms with Python in Business Analytics
This course is part of Business Analytics Specialization
Instructors: Ronald Guymon
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What you'll learn
Implement a conceptual framework of machine learning algorithms for finding actionable insight to business problems.
Apply a conceptual foundation for interpreting machine learning results from regression, classification, and clustering algorithms.
Apply machine learning algorithms to business data.
Skills you'll gain
- Predictive Modeling
- Unsupervised Learning
- Machine Learning Algorithms
- Data Analysis
- Data Transformation
- Predictive Analytics
- Regression Analysis
- Machine Learning
- Exploratory Data Analysis
- Scikit Learn (Machine Learning Library)
- R Programming
- Business Analytics
- Classification And Regression Tree (CART)
- Supervised Learning
Details to know
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There are 4 modules in this course
In this module, we will examine why exploratory data analysis alone may not yield actionable business insights, evaluate the key steps of the machine learning workflow, and apply preprocessing techniques using scikit-learn to prepare data for analysis with various algorithms.
What's included
18 videos7 readings2 assignments1 discussion prompt1 plugin
In this module, we will learn how regression can be used for prediction, relationship exploration, and inference in business contexts by applying scikit-learn and scipy, and will evaluate the effectiveness of regression models across different applications.
What's included
12 videos3 readings1 assignment1 peer review
In this module, we will deepen our understanding of how classification algorithms are applied in business by running and interpreting K-nearest neighbors and decision tree models.
What's included
11 videos3 readings1 assignment
In this module, we will gain a deeper understanding of how clustering algorithms are used in business by running and interpreting K-means and DBSCAN models.
What's included
10 videos5 readings1 assignment1 discussion prompt1 plugin
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This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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