Building Data Science Solutions with Anaconda: A comprehensive starter guide to building robust and complete models

Length: 330 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-05-27
ISBN-10: 1800568789
ISBN-13: 9781800568785
Sales Rank: #91699 (See Top 100 Books)

The missing manual to becoming a successful data scientist―develop the skills to use key tools and the knowledge to thrive in the AI/ML landscape

Key Features

Learn from an AI patent-holding engineering manager with deep experience in Anaconda tools and OSS
Get to grips with critical aspects of data science such as bias in datasets and interpretability of models
Gain a deeper understanding of the AI/ML landscape through real-world examples and practical analogies

Book Description

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.

What you will learn

Install packages and create virtual environments using conda
Understand the landscape of open source software and assess new tools
Use scikit-learn to train and evaluate model approaches
Detect bias types in your data and what you can do to prevent it
Grow your skillset with tools such as NumPy, pandas, and Jupyter Notebooks
Solve common dataset issues, such as imbalanced and missing data
Use LIME and SHAP to interpret and explain black-box models

Who this book is for

If you’re a data analyst or data science professional looking to make the most of Anaconda’s capabilities and deepen your understanding of data science workflows, then this book is for you. You don’t need any prior experience with Anaconda, but a working knowledge of Python and data science basics is a must.

Building Data Science Solutions with Anaconda
Foreword
Contributors
About the author
About the reviewers
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
    Share Your Thoughts
Part 1: The Data Science Landscape – Open Source to the Rescue
Chapter 1: Understanding the AI/ML landscape
    Introducing Artificial Intelligence (AI)
        Defining AI
        Defining a data scientist
    Understanding the current state of AI and ML
        Knowing the difference between AI and ML
    Understanding the massive generation of new data
    Evaluating how AI delivers business value
    Understanding the main types of ML models
        Supervised learning
        Unsupervised learning
        Reinforcement learning
        Evaluating the problem type
    Dealing with out-of-date models
        Difference between online and batch learning
        How models become stale: model drift
    Installing packages with Anaconda
        How to use Anaconda Individual Edition to download packages
        How to handle dependencies with conda
        Creating separate work areas with Anaconda environments
    Summary
Chapter 2: Analyzing Open Source Software
    Technical requirements
    Understanding open source
        Forking an OSS repository with Git and GitHub
        Defining open source software
        Advantages of OSS
    Understanding the top four OSS licenses
        Copyleft versus permissive licenses
        How to find out what license a library uses
    Evaluating a new tool or library
        GitHub stars
        Age
        How long since it's been updated
        Number of maintainers
        Age of open issues/PRs
        Number of external dependencies
    Importing packages with Anaconda and conda-forge
        Updating to the latest conda version
        Creating a conda virtual environment
        The differences between modules, packages, and libraries
    Evaluating and using scikit-learn
        Evaluation metrics
        Getting up and running with scikit-learn
    Summary
Chapter 3: Using the Anaconda Distribution to Manage Packages
    Technical requirements
    Learning how dependency resolution works
        How pip and conda are different
    Discovering what conda environments are and how to use them 
        Creating environments in conda
        Creating environments in Navigator
        Installing packages via Navigator
        Installing packages via conda
        Exporting environments to Anconda.org
    Managing channels with Anaconda Navigator and conda
        Understanding what a channel is
        Setting channel priority
    Using advanced conda info and settings
        Using conda info to see configuration information
        Setting up your conda settings file
    Conda cheat sheet
        Conda general commands
        Conda environment commands
    Summary
Chapter 4: Working with Jupyter Notebooks and NumPy
    Technical requirements
    Working with Jupyter notebooks
        Creating a new Jupyter notebook
        Working with Jupyter notebook cells
        Line and cell magic in Jupyter cells
        Accessing the system command line
    Using NumPy to perform calculations quickly
        Creating and manipulating NumPy arrays
        Understanding why NumPy's ndarrays are fast
    Summary
Part 2: Data Is the New Oil, Models Are the New Refineries
Chapter 5: Cleaning and Visualizing Data
    Technical requirements
    Cleaning data with pandas
        Installing pandas in your conda environment
        Working with CSVs
        Analyzing and cleaning data
        Dealing with missing data
        Creating a deep copy of a Data Frame
    Visualization with Matplotlib
        Preparing data for plotting
        Plotting data
        Customizing the plot
        Showing the plot
        Plotting a scatter plot and polynomial regression line 
    Summary
Chapter 6: Overcoming Bias in AI/ML
    Technical requirements
    Defining bias versus discrimination
        Bias in AI/ML
        Discrimination in AI/ML
    Overcoming proxy bias
        Examples of proxy bias
        How to prevent proxy bias
    Overcoming sample bias
        Examples of sample bias
        Racial/gender bias
        How to prevent sample bias
    Overcoming exclusion bias
        Examples of exclusion bias
        How to prevent exclusion bias
    Overcoming measurement bias
        Examples of measurement bias
        How to prevent measurement bias
    Overcoming societal AI bias
        Examples of societal bias
    Finding bias in an example
    Summary
Chapter 7: Choosing the Best AI Algorithm
    Technical requirements
    Defining your problem
        Model problem types
        Algorithms by problem type
    Understanding regression problems with examples
        Linear regression
        Random forest
        Support vector machines
        Artificial neural networks
    Classification
        Classification algorithms
        Classification example
        Logistic regression
        Decision trees/random forest
        K-nearest neighbors
    Anomaly detection
        One-class SVM
        Isolation forests
    Clustering problems
        DBScan
        K-means clustering
    Summary
Chapter 8: Dealing with Common Data Problems
    Technical requirements
    Dealing with too much data
        Checking feature correlation
        Detecting NaN values
        Dealing with valid NaN values
        Dealing with invalid NaN values
    Finding and correcting data entries
        Retrieving specific pandas items by condition
    Working with categorical values with one-hot encoding
        One-hot encoding with pandas
        Ordinal encoding
    Feature scaling
        Creating a histogram with pandas
        Using the R2 score to evaluate a model
        Using the MSE score to evaluate a model
        Using the MAE score to evaluate a model
        Overcoming the limits of capped values
        Recovering the raw dataset
    Working with date formats
    Summary
Part 3: Practical Examples and Applications
Chapter 9: Building a Regression Model with scikit-learn
    Technical requirements
    Walking through the data science workflow
    Setting up and understanding the problem space
        Setting up your workspace
        Combining two CSV files
    Exploring and cleaning the data
        Checking for missing values
        Checking for redundant features
        Focusing on the key features
    Creating and evaluating regression algorithms
        Comparing regression and classification
        Preparing the data for training
    Evaluating potential models using MSE and R2 scores
        Training your models
        Analyzing model results with MSE and R2 score
        R2 score
        Training a KNN model
        Linear regression
        Making use of our results
    Summary
Chapter 10: Explainable AI - Using LIME and SHAP
    Technical requirements
    Understanding the value of interpretation
        Knowing the difference between interpreting and explaining
        Looking at legal reasons for interpretability
        Looking at moral reasons for interpretability
        Looking at business reasons for interpretability
        Looking at model improvement reasons for interpretability
    Understanding models that are interpretable by design
        Interpreting decision trees
        Graphing a decision tree
    Explaining a model's outcome with LIME
        Creating a LIME example
        Weighing the drawbacks of LIME
    Explaining a model's outcome with SHAP 
        Avoid confusion with Shapley values
        Creating a SHAP example
        Looking at the SHAP result
        Weighing the drawbacks of SHAP
        Thinking through shortcomings of interpretation and XAI
    Summary
Chapter 11: Tuning Hyperparameters and Versioning Your Model
    Technical requirements 
    Creating a scikit-learn pipeline
        scikit-learn estimators and transformers
        Creating a scikit-learn pipeline
        Testing out various algorithm methods
        Feeding live production data into pipelines
    Finding optimal hyperparameters with GridSearchCV
        Defining the difference between hyperparameters and parameters
        Using a grid search on a random forest pipeline
    Versioning and storing your model
        Pickling a model
        Loading your pickled model
        Storing your model with joblib
    Summary
    Close
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