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

Cover
Title page
Copyright and Credits
Foreword
Contributors
Table of Contents
Preface
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	Dealing with out-of-date models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
Index
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