English | 2022 | ISBN: ‎ 1800568789 | 330 pages | True PDF EPUB | 17.27 MB

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 eeering 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.

Table of Contents

Understanding the AI/ML Landscape

Analyzing Open Source Software

Using Anaconda Distribution to Manage Packages

Working with Jupyter Notebooks and NumPy

Cleaning and Visualizing Data

Overcoming Bias in AI/ML

Choosing the Best AI Algorithm

Dealing with Common Data Problems

Building a Regression Model with scikit-learn

Explainable AI - Using LIME and SHAP

Tuning Hyperparameters and Versioning Your Model