A data-first and use-case driven approach to understanding machine learning and deep learning concepts with Low-Code AI is presented in this hands-on guide. It offers three problem-focused ways to learn ML: no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras.

Format: Paperback / softback
Length: 350 pages
Publication date: 29 September 2023
Publisher: O'Reilly Media

Take a Data-First and Use-Case Driven Approach to Understanding Machine Learning and Deep Learning Concepts with Low-Code AI

In this comprehensive hands-on guide, we take a data-first and use-case driven approach to understanding machine learning and deep learning concepts with Low-Code AI. Whether you're a business or data analyst seeking to gain practical insights into these advanced technologies, or a developer looking to expand your skills, this guide is designed to help you achieve your goals.

Three Problem-Focused Ways to Learn ML

We present three problem-focused ways to learn ML:

No Code Using AutoML

Explore the world of no-code ML using AutoML, a powerful tool that automates the process of building machine learning models. With AutoML, you can quickly and easily generate high-quality models without writing a single line of code. This approach is ideal for beginners who want to get started with ML without any prior technical expertise.

Low-Code Using BigQuery ML

Deepen your understanding of ML by leveraging the power of BigQuery ML, a low-code platform that enables you to build and train ML models directly on your data stored in Google Cloud Storage. BigQuery ML provides a user-friendly interface that allows you to explore, analyze, and transform your data using a variety of ML algorithms, without the need for complex programming.

Custom Code Using scikit-learn and Keras

Take your ML skills to the next level by learning custom code using scikit-learn and Keras, two popular Python libraries for machine learning. With these tools, you'll have the flexibility to build and train custom ML models tailored to your specific use cases. This approach is ideal for developers who want to gain a deeper understanding of the underlying algorithms and techniques of ML.

Learning Key ML Concepts

Throughout this guide, we'll use real-world datasets with realistic problems to help you learn key ML concepts. By working with these datasets, you'll gain hands-on experience applying ML techniques to solve real-world business challenges.

Project-Based Introduction to ML/AI

This guide is designed to provide a project-based introduction to ML/AI for business and data analysts. We'll take you through a detailed, data-driven approach, covering key steps such as loading and analyzing data, feeding data into an ML model, building, training, and testing the model, and deploying the model into production.

Building Machine Learning Models for Various Industries

Authors Michael Abel and Gwendolyn Stripling have extensive experience in building machine learning models for retail, healthcare, financial services, energy, and telecommunications industries. They'll share their expertise and insights, helping you develop ML models that can address the unique challenges and opportunities in these industries.

What You'll Learn

By the end of this guide, you'll have a solid understanding of machine learning and deep learning concepts, as well as the practical skills to build and deploy ML models in your own projects. You'll be able to:

Distinguish Structured and Unstructured Data

Understand the different challenges and opportunities presented by structured and unstructured data, and learn how to handle them effectively.

Visualize and Analyze Data

Use powerful visualization tools to explore and analyze your data, gaining insights that can inform your ML model development.

Preprocess Data for Input into a Machine Learning Model

Learn how to preprocess data for input into a machine learning model, ensuring that it is clean, accurate, and suitable for training.

Differentiate Between Regression and Classification Supervised Learning Models

Understand the differences between regression and classification supervised learning models, and learn how to choose the appropriate model for your specific use case.

Compare Different Machine Learning Model Types and Architectures

Explore the various types of machine learning models, from no-code to low-code to custom training, and learn how to select the most suitable model for your project.

Design, Implement, and Tune ML Models

Learn how to design, implement, and tune ML models using scikit-learn and Keras, ensuring that they are optimized for performance and accuracy.

Export Data to a GitHub Repository for Data Management and Governance

Learn how to export data to a GitHub repository for data management and governance, ensuring that your ML models are accessible and reusable by others.

In conclusion, this hands-on guide provides a comprehensive and practical approach to understanding machine learning and deep learning concepts with Low-Code AI. Whether you're a business or data analyst, developer, or simply interested in exploring these advanced technologies, this guide will help you achieve your goals and gain a competitive edge in today's data-driven world. So, let's get started and unlock the power of ML with Low-Code AI!

Dimension: 232 x 178 (mm)
ISBN-13: 9781098146825