Building machine learning pipelines is very common in data science. It helps simplify complex operations and adds a level of reusability to the software code. In this recipe, we used sklearn to perform complex operations on a simple pipeline. In our pipeline, we created a set of transformers. For the numeric numbers, we used a scaler, while for the categorical numbers, we used one-hot encoding. Next, we created a processor pipeline. In our case, we used a random forest classifier. Note that this pipeline step is an array, so we could pass more classifiers into our array. However, for the sake of simplicity, we will save that for the There's more... section. Finally, we trained and got the predictions from our model.
Artificial Intelligence for IoT Cookbook
By :
Artificial Intelligence for IoT Cookbook
By:
Overview of this book
Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.
Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease.
By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Related Content you might be interested in
Current Title:
Artificial Intelligence for IoT Cookbook
Table of Contents (11 chapters)
Preface
Setting Up the IoT and AI Environment
Free Chapter
Handling Data
Machine Learning for IoT
Deep Learning for Predictive Maintenance
Anomaly Detection
Computer Vision
NLP and Bots for Self-Ordering Kiosks
Optimizing with Microcontrollers and Pipelines
Deploying to the Edge
Customer Reviews