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Building Data Science Solutions with Anaconda

You're reading from   Building Data Science Solutions with Anaconda A comprehensive starter guide to building robust and complete models

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Length 330 pages
Edition 1st Edition
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Author (1):
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Dan Meador Dan Meador
Author Profile Icon Dan Meador
Dan Meador
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Table of Contents (16) Chapters Close

Preface 1. Part 1: The Data Science Landscape – Open Source to the Rescue
2. Chapter 1: Understanding the AI/ML landscape FREE CHAPTER 3. Chapter 2: Analyzing Open Source Software 4. Chapter 3: Using the Anaconda Distribution to Manage Packages 5. Chapter 4: Working with Jupyter Notebooks and NumPy 6. Part 2: Data Is the New Oil, Models Are the New Refineries
7. Chapter 5: Cleaning and Visualizing Data 8. Chapter 6: Overcoming Bias in AI/ML 9. Chapter 7: Choosing the Best AI Algorithm 10. Chapter 8: Dealing with Common Data Problems 11. Part 3: Practical Examples and Applications
12. Chapter 9: Building a Regression Model with scikit-learn 13. Chapter 10: Explainable AI - Using LIME and SHAP 14. Chapter 11: Tuning Hyperparameters and Versioning Your Model 15. Other Books You May Enjoy

Setting up and understanding the problem space

They say the quickest way to become a millionaire is to open a winery… you just have to be a billionaire first. We want to avoid this assumption that we will always lose money by creating the world's finest wine. We want to take an analytical approach in order to have more success than others.

Our goal is to find what attributes it takes to make great wine in order to maximize our efforts. We'll work through this as a data scientist would from beginning to end and determine what matters most in our final quality score.

Wine is one of the oldest beverages in the world. Created around 6,000 BC, it's an alcoholic drink crafted from fermented grapes. Similar to AI/ML, there are people who spend their whole life trying to perfect and master just a subset of this domain and there is still much that could be explored. It's always good to start by making sure you understand what you are working with. This will...

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