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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
Part 1: The Data Science Landscape – Open Source to the Rescue
Free Chapter
2
Chapter 1: Understanding the AI/ML landscape
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Chapter 1: Understanding the AI/ML landscape
Introducing Artificial Intelligence (AI)
Understanding the current state of AI and ML
Understanding the massive generation of new data
Evaluating how AI delivers business value
Understanding the main types of ML models
Dealing with out-of-date models
Installing packages with Anaconda
Summary
3
Chapter 2: Analyzing Open Source Software
Chapter 2: Analyzing Open Source Software
Technical requirements
Understanding open source
Understanding the top four OSS licenses
Evaluating a new tool or library
Importing packages with Anaconda and conda-forge
Evaluating and using scikit-learn
Summary
4
Chapter 3: Using the Anaconda Distribution to Manage Packages
Chapter 3: Using the Anaconda Distribution to Manage Packages
Technical requirements
Learning how dependency resolution works
Discovering what conda environments are and how to use them
Managing channels with Anaconda Navigator and conda
Using advanced conda info and settings
Conda cheat sheet
Summary
5
Chapter 4: Working with Jupyter Notebooks and NumPy
Chapter 4: Working with Jupyter Notebooks and NumPy
Technical requirements
Working with Jupyter notebooks
Using NumPy to perform calculations quickly
Summary
6
Part 2: Data Is the New Oil, Models Are the New Refineries
Part 2: Data Is the New Oil, Models Are the New Refineries
7
Chapter 5: Cleaning and Visualizing Data
Chapter 5: Cleaning and Visualizing Data
Technical requirements
Cleaning data with pandas
Visualization with Matplotlib
Summary
8
Chapter 6: Overcoming Bias in AI/ML
Chapter 6: Overcoming Bias in AI/ML
Technical requirements
Defining bias versus discrimination
Overcoming proxy bias
Overcoming sample bias
Overcoming exclusion bias
Overcoming measurement bias
Overcoming societal AI bias
Finding bias in an example
Summary
9
Chapter 7: Choosing the Best AI Algorithm
Chapter 7: Choosing the Best AI Algorithm
Technical requirements
Defining your problem
Understanding regression problems with examples
Classification
Anomaly detection
Clustering problems
Summary
10
Chapter 8: Dealing with Common Data Problems
Chapter 8: Dealing with Common Data Problems
Technical requirements
Dealing with too much data
Finding and correcting data entries
Working with categorical values with one-hot encoding
Feature scaling
Working with date formats
Summary
11
Part 3: Practical Examples and Applications
Part 3: Practical Examples and Applications
12
Chapter 9: Building a Regression Model with scikit-learn
Chapter 9: Building a Regression Model with scikit-learn
Technical requirements
Walking through the data science workflow
Setting up and understanding the problem space
Exploring and cleaning the data
Creating and evaluating regression algorithms
Evaluating potential models using MSE and R2 scores
Summary
13
Chapter 10: Explainable AI - Using LIME and SHAP
Chapter 10: Explainable AI - Using LIME and SHAP
Technical requirements
Understanding the value of interpretation
Understanding models that are interpretable by design
Explaining a model's outcome with LIME
Explaining a model's outcome with SHAP
Summary
14
Chapter 11: Tuning Hyperparameters and Versioning Your Model
Chapter 11: Tuning Hyperparameters and Versioning Your Model
Technical requirements
Creating a scikit-learn pipeline
Finding optimal hyperparameters with GridSearchCV
Versioning and storing your model
Summary
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Summary

As you can see, there is a lot to cover across the AI/ML world, and we have just scratched the surface of what lies beneath.

In this chapter, we covered a bit of the history of AI/ML in order to understand how we got to this point. We also saw how a huge amount of data is feeding the AI revolution with cheap storage and more sources creating it. Business value is also a main driving force pushing this tech forward, and we saw how some companies such as Netflix and Google are leveraging it to solve different problems. Moving on to out-of-date models, we learned what drift is, and how it can impact your ability to deliver consistent predictions on production data as the real-world changes. Finally, we got a bit more hands-on by installing packages with Anaconda's package manager, conda.

With this knowledge, you're now equipped to be able to distinguish between different models and problem types to use in certain scenarios, including supervised and unsupervised. You'll also be able to know whether the trade-offs for online versus batch learning are worth it and be able to determine which is right for you. Lastly, you should now be able to install Anaconda Individual Edition and use it to create an environment and download the packages that you will need for the rest of this book.

In our next chapter, you'll learn everything you need to know about the bedrock of AI: open source. You'll get hands-on experience with the most popular tools, NumPy and pandas, learn about the open source community and how to take part, and practice how to import anything you need using conda and the conda-forge community into your project in seconds.

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