Book Image

AI-Powered Commerce

By : Andy Pandharikar, Frederik Bussler
Book Image

AI-Powered Commerce

By: Andy Pandharikar, Frederik Bussler

Overview of this book

Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must. Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends. By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.
Table of Contents (17 chapters)
1
Section 1:Benefits of AI-Powered Commerce
5
Section 2:How Top Brands Use Artificial Intelligence
11
Section 3:How to Use Commerce.AI for Product Ideation, Trend Analysis, and Predictions

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Take a look at more positive reviews, with a positive polarity and fairly low subjectivity."

A block of code is set as follows:

s = df['Reviews']
df['Reviews'] = df['Reviews'].astype(str)
df = df[df['Reviews'] == s]
df[['polarity', 'subjectivity']] = df['Reviews'].apply(lambda   Text: pd.Series(TextBlob(Text).sentiment))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

s = df['Reviews']
df['Reviews'] = df['Reviews'].astype(str)
df = df[df['Reviews'] == s]
df[['polarity', 'subjectivity']] = df['Reviews'].apply(lambda   Text: pd.Series(TextBlob(Text).sentiment))

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Even positive reviews complain about the range, such as one review that says simply Good short range."

Tips or important notes

Appear like this.