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Building Data Science Solutions with Anaconda

You're reading from  Building Data Science Solutions with Anaconda

Product type Book
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Dan Meador Dan Meador
Profile icon Dan Meador

Table of Contents (16) Chapters

Preface Part 1: The Data Science Landscape – Open Source to the Rescue
Chapter 1: Understanding the AI/ML landscape Chapter 2: Analyzing Open Source Software Chapter 3: Using the Anaconda Distribution to Manage Packages Chapter 4: Working with Jupyter Notebooks and NumPy Part 2: Data Is the New Oil, Models Are the New Refineries
Chapter 5: Cleaning and Visualizing Data Chapter 6: Overcoming Bias in AI/ML Chapter 7: Choosing the Best AI Algorithm Chapter 8: Dealing with Common Data Problems Part 3: Practical Examples and Applications
Chapter 9: Building a Regression Model with scikit-learn Chapter 10: Explainable AI - Using LIME and SHAP Chapter 11: Tuning Hyperparameters and Versioning Your Model Other Books You May Enjoy

Classification

Being able to put things into certain classes might be the most common type of ML application that you see in the world, and has been a staple of the industry for a long time.

There are two main types of classification: binary classification and multi-class classification. As the names indicate, binary classification is when the outcome only has two possible options. It's very common to have a true or false outcome in this setup.

Multi-class classification is when there are more than two possible classes. This could be for a variety of scenarios, such as movie genre. The approaches taken for them are very similar to a binary classification problem.

Let's check out some examples that might help you get a better grasp on problems that fall into the classification bucket:

Whether emails are spam or not (binary)

  • Whether you would survive the Titanic sinking (binary)
  • Identifying the type of flower (multi-class)
  • Labeling handwritten...
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