Note
About
This section briefly introduces the author, the coverage of this book, the technical skills you'll need to get started, and the hardware and software requirements required to complete all of the included activities and exercises.
In this book, you will learn about the various artificial intelligence and machine learning services available on AWS. Through practical hands-on exercises, you will learn how to use these services to generate impressive results. By the end of this book, you will have a basic understanding of how to use a wide range of AWS services in your own projects.
Jeffrey Jackovich, is the author of this book, and a curious data scientist with a background in health-tech and mergers and acquisitions (M&A). He has extensive business-oriented healthcare knowledge, but enjoys analyzing all types of data with R and Python. He loves the challenges involved in the data science process, and his ingenious demeanor was tempered while serving as a Peace Corps volunteer in Morocco. He is completing a Masters of Science in Computer Information Systems, with a Data Analytics concentration, from Boston University.
Ruze Richards, is the author of this book, and a data scientist and cloud architect who has spent most of his career building high-performance analytics systems for enterprises and startups. He is especially passionate about AI and machine learning, having started life as a physicist who got excited about neural nets, then going on to work at AT&T Bell Labs in order to further pursue this area of interest. With the new wave of excitement along with the actual computing power being available on the cloud for anybody to actually get amazing results with machine learning, he is thrilled to be able to spread the knowledge and help people achieve their goals.
Get up and running with machine learning on the AWS platform
Analyze unstructured text using AI and Amazon Comprehend
Create a chatbot and interact with it using speech and text input
Retrieve external data via your chatbot
Develop a natural language interface
Apply AI to images and videos with Amazon Rekognition
This book is ideal for data scientists, programmers, and machine-learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of the Amazon Web Services.
This book takes a hands-on approach to teach you machine learning with AWS. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.
For an optimal student experience, we recommend the following hardware configuration:
Processor: Intel Core i5 or equivalent
Memory: 4GB RAM
Storage: 35GB available space
You'll also need the following software installed in advance:
OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
Browser: Google Chrome, Latest Version
An AWS free tier account
aws comprehend detect-dominant-language ^ --region us-east-1 ^ --text "Machine Learning is fascinating."
Conventions
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: " The command form is, "s3://myBucketName/myKey."
A block of code is set as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Data stored in S3 is managed as objects using an Application Programming Interface (API) accessible via the internet (HTTPS)."
Before you start this book, you will need an AWS account. You will also need to set up the AWS command-line interface (AWXSCLI), the steps for which can be found below. You will also need Python 3.6, pip and an AWS Rekognition Account throughout the book.
AWS account
For an AWS free tier account, you will need a personal email address, and credit or debit card, and a cell phone that can receive text message so you can verify your account. To create a new account, follow this link https://aws.amazon.com/free/.
AWSCLI Setup
Install AWS CLI setup from the link https://s3.amazonaws.com/aws-cli/AWSCLISetup.exe. To download the AWS CLI setup file (*includes 32-bit and 64-bit MSI installers and will automatically install the correct version). To verify install was successful open a command prompt and type aws --version.
Installing Python
Install Python 3.6 following the instructions at: https://realpython.com/installing-python/.
Installing pip
To install pip, go to command prompt and type pip install awscli --upgrade --user. Verify the successful install with command "aws - -version"
After installing pip, add the AWS executable to your OS's PATH environment variable. With an MSI installation, this should occur automatically, but you may need to set it manually if the "aws - -version" command is not working.
To modify your PATH variable (Windows), type environment variables, and select Edit the system environment variables for your account, select the path, and add the path to the variable value field, separated by semicolons.
Installing Virtual Environment
Install the Anaconda version depending on your operating system from the following link https://www.anaconda.com/download/. Anaconda helps install what you need without conflicting packages.
To check the Anaconda Distribution is up to date, type conda update conda.
To create a virtual environment, type conda create -n yourenvname python=3.6 anaconda and press y to continue, this will install the Python version and all associated anaconda packaged libraries at path_to_you_anaconda_location/anaconda/envs/yourenvname.
To activate the account on macOS and Linux, type source activate yourenvname and for Windows type activate yourenvname.
To install the additional Python packages to a virtual environment, type conda install –n yourenvname [package].
To deactivate the virtual environment type deactivate.
Configuration and Credential files
To locate the config file, see the operating specific commands below. For more information see: https://docs.aws.amazon.com/cli/latest/userguide/cli-config-files.html.
Amazon Rekognition Account
You will need to create a new Amazon Rekognition free tier account where customers can analyze up to 5,000 images free each month for the first 12 months. To create the free account, follow the link https://aws.amazon.com/rekognition/
Installing the Code Bundle
Additional Resources
The code bundle for this book is also hosted on GitHub at: https://github.com/TrainingByPackt/Machine-Learning-with-AWS.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!