What Is Sales Analytics and How Does It Benefit My Business?
March 4, 2024
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Instructors: Mark J Grover
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This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
11 videos2 readings3 assignments2 app items
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
7 videos1 reading3 assignments2 app items
There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will allow you to reuse your data to use more samples for training and testing.
6 videos1 reading2 assignments2 app items1 plugin
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
10 videos1 reading3 assignments1 app item
In this section, you will understand the relationship between the loss function and the different regularization types.
5 videos1 reading2 assignments2 app items
In this section you will test everything you learned
2 readings1 peer review1 app item
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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University of Washington
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Alberta Machine Intelligence Institute
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681 reviews
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Reviewed on Apr 9, 2021
Very well presented. This is without doubt the best series for Machine Learning on Coursera.
Reviewed on Oct 18, 2023
The course is extremely good in understanding the concepts of regressions. Great work
Reviewed on Nov 6, 2020
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
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