--- product_id: 533083667 title: "AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence" price: "€ 0.82" currency: EUR in_stock: true reviews_count: 13 url: https://www.desertcart.at/products/533083667-ai-and-machine-learning-for-coders-a-programmers-guide-to store_origin: AT region: Austria --- # AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence **Price:** € 0.82 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence - **How much does it cost?** € 0.82 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.at](https://www.desertcart.at/products/533083667-ai-and-machine-learning-for-coders-a-programmers-guide-to) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: how to build models with TensorFlow using skills that employers desire; the basics of machine learning by working with code samples; how to implement computer vision, including feature detection in images; how to use NLP to tokenize and sequence words and sentences; methods for embedding models in Android and iOS; and how to serve models over the web and in the cloud with TensorFlow Serving. PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Review: Exceeded expectations: I hope to use TensorFlow tomorrow! - I’m a tad suspicious about listening to books that are too deep in the weeds with code. If they’re about programming concepts, audiobooks can be suitable, but if they involve code like this one, I like to have a physical picture of the lines of code. However, I was pleasantly surprised that this book conveyed many ideas despite communicating code aurally, too. Artificial intelligence (AI) and machine learning (ML) are huge topics today. I read this book just to supplement my broader knowledge, but while reading, I found a few applications that relate directly to my work. I’m going to try out TensorFlow, the software demoed here, in my work this week. I can see where it might be a lot more effective for my users than calling remote, server-based services from OpenAI. This book features the Python-based TensorFlow as the main framework. It shows how to use it to build your own large-language model (LLM). When I started this book, I didn’t realize that it’d also demonstrate how to use TensorFlow in web situations, which is my main domain. TensorFlowJS can use an LLM built in Python to perform features in a website. This book also describes how an API can house AI/ML models from TensorFlow. Mobile devices and embedded systems like Raspberry Pi receive their own chapter, too, due to their limited processing capabilities. Honestly, I did not have super-high expectations when I read this book. I just wanted to fill in my AI knowledge from a programmer’s perspective. I did not expect to apply the knowledge directly. Instead, I found my mind engaged with new ways to think about AI – even from a non-programmer’s perspective. I’m now able to better explain how to maximize AI use to my non-IT, scientific team. And I’m really chomping at the bit to see whether TensorFlowJS can meet the needs of my latest project. It’s a total joy when a book surprises with its intellectual depth and applicability. This book did both in spades, and I’m glad I listened to it! Review: Very Practical, Memorable Introduction - I’d summarize this in a sentence as “an introduction to machine learning with TensorFlow,” TensorFlow being Google’s platform for building, training and distributing AI models. I enjoyed this book a lot more than than 100-Page Book on Machine Learning, because the examples and scenarios felt more practice and grounded in the kind of interface that I would have with AI/ML in the workplace. This book was more focused on tools than theory, and went into theoretical concepts when needed to demystify the parameters/API of various TensorFlow toolsets. So far, this has been my favorite introduction to the topic, and I have ready 3-5 different AI/ML books. ## Images ![AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence - Image 1](https://m.media-amazon.com/images/I/81RnKN6BRuL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Exceeded expectations: I hope to use TensorFlow tomorrow! *by S***N on May 13, 2025* I’m a tad suspicious about listening to books that are too deep in the weeds with code. If they’re about programming concepts, audiobooks can be suitable, but if they involve code like this one, I like to have a physical picture of the lines of code. However, I was pleasantly surprised that this book conveyed many ideas despite communicating code aurally, too. Artificial intelligence (AI) and machine learning (ML) are huge topics today. I read this book just to supplement my broader knowledge, but while reading, I found a few applications that relate directly to my work. I’m going to try out TensorFlow, the software demoed here, in my work this week. I can see where it might be a lot more effective for my users than calling remote, server-based services from OpenAI. This book features the Python-based TensorFlow as the main framework. It shows how to use it to build your own large-language model (LLM). When I started this book, I didn’t realize that it’d also demonstrate how to use TensorFlow in web situations, which is my main domain. TensorFlowJS can use an LLM built in Python to perform features in a website. This book also describes how an API can house AI/ML models from TensorFlow. Mobile devices and embedded systems like Raspberry Pi receive their own chapter, too, due to their limited processing capabilities. Honestly, I did not have super-high expectations when I read this book. I just wanted to fill in my AI knowledge from a programmer’s perspective. I did not expect to apply the knowledge directly. Instead, I found my mind engaged with new ways to think about AI – even from a non-programmer’s perspective. I’m now able to better explain how to maximize AI use to my non-IT, scientific team. And I’m really chomping at the bit to see whether TensorFlowJS can meet the needs of my latest project. It’s a total joy when a book surprises with its intellectual depth and applicability. This book did both in spades, and I’m glad I listened to it! ### ⭐⭐⭐⭐⭐ Very Practical, Memorable Introduction *by R***E on July 12, 2024* I’d summarize this in a sentence as “an introduction to machine learning with TensorFlow,” TensorFlow being Google’s platform for building, training and distributing AI models. I enjoyed this book a lot more than than 100-Page Book on Machine Learning, because the examples and scenarios felt more practice and grounded in the kind of interface that I would have with AI/ML in the workplace. This book was more focused on tools than theory, and went into theoretical concepts when needed to demystify the parameters/API of various TensorFlow toolsets. So far, this has been my favorite introduction to the topic, and I have ready 3-5 different AI/ML books. ### ⭐⭐⭐⭐ Don't you hate it when you can't find the table of contents? *by C***E on January 9, 2021* I have a subscription to online O'Reilly, so I can view the book. It is pretty good as far as having practical details. It is not like it is just a collection of code with no explanation. Usually Amazon shows the table of contents, but not for this one. So here it is: Foreword Preface Who Should Read This Book Why I Wrote This Book Navigating This Book Technology You Need to Understand Online Resources Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments I. Building Models 1. Introduction to TensorFlow What Is Machine Learning? Limitations of Traditional Programming From Programming to Learning What Is TensorFlow? Using TensorFlow Installing TensorFlow in Python Using TensorFlow in PyCharm Using TensorFlow in Google Colab Getting Started with Machine Learning Seeing What the Network Learned Summary 2. Introduction to Computer Vision Recognizing Clothing Items The Data: Fashion MNIST Neurons for Vision Designing the Neural Network The Complete Code Training the Neural Network Exploring the Model Output Training for Longer—Discovering Overfitting Stopping Training Summary 3. Going Beyond the Basics: Detecting Features in Images Convolutions Pooling Implementing Convolutional Neural Networks Exploring the Convolutional Network Building a CNN to Distinguish Between Horses and Humans The Horses or Humans Dataset The Keras ImageDataGenerator CNN Architecture for Horses or Humans Adding Validation to the Horses or Humans Dataset Testing Horse or Human Images Image Augmentation Transfer Learning Multiclass Classification Dropout Regularization Summary 4. Using Public Datasets with TensorFlow Datasets Getting Started with TFDS Using TFDS with Keras Models Loading Specific Versions Using Mapping Functions for Augmentation Using TensorFlow Addons Using Custom Splits Understanding TFRecord The ETL Process for Managing Data in TensorFlow Optimizing the Load Phase Parallelizing ETL to Improve Training Performance Summary 5. Introduction to Natural Language Processing Encoding Language into Numbers Getting Started with Tokenization Turning Sentences into Sequences Removing Stopwords and Cleaning Text Working with Real Data Sources Getting Text from TensorFlow Datasets Getting Text from CSV Files Getting Text from JSON Files Summary 6. Making Sentiment Programmable Using Embeddings Establishing Meaning from Words A Simple Example: Positives and Negatives Going a Little Deeper: Vectors Embeddings in TensorFlow Building a Sarcasm Detector Using Embeddings Reducing Overfitting in Language Models Using the Model to Classify a Sentence Visualizing the Embeddings Using Pretrained Embeddings from TensorFlow Hub Summary 7. Recurrent Neural Networks for Natural Language Processing The Basis of Recurrence Extending Recurrence for Language Creating a Text Classifier with RNNs Stacking LSTMs Using Pretrained Embeddings with RNNs Summary 8. Using TensorFlow to Create Text Turning Sequences into Input Sequences Creating the Model Generating Text Predicting the Next Word Compounding Predictions to Generate Text Extending the Dataset Changing the Model Architecture Improving the Data Character-Based Encoding Summary 9. Understanding Sequence and Time Series Data Common Attributes of Time Series Trend Seasonality Autocorrelation Noise Techniques for Predicting Time Series Naive Prediction to Create a Baseline Measuring Prediction Accuracy Less Naive: Using Moving Average for Prediction Improving the Moving Average Analysis Summary 10. Creating ML Models to Predict Sequences Creating a Windowed Dataset Creating a Windowed Version of the Time Series Dataset Creating and Training a DNN to Fit the Sequence Data Evaluating the Results of the DNN Exploring the Overall Prediction Tuning the Learning Rate Exploring Hyperparameter Tuning with Keras Tuner Summary 11. Using Convolutional and Recurrent Methods for Sequence Models Convolutions for Sequence Data Coding Convolutions Experimenting with the Conv1D Hyperparameters Using NASA Weather Data Reading GISS Data in Python Using RNNs for Sequence Modeling Exploring a Larger Dataset Using Other Recurrent Methods Using Dropout Using Bidirectional RNNs Summary II. Using Models 12. An Introduction to TensorFlow Lite What Is TensorFlow Lite? Walkthrough: Creating and Converting a Model to TensorFlow Lite Step 1. Save the Model Step 2. Convert and Save the Model Step 3. Load the TFLite Model and Allocate Tensors Step 4. Perform the Prediction Walkthrough: Transfer Learning an Image Classifier and Converting to TensorFlow Lite Step 1. Build and Save the Model Step 2. Convert the Model to TensorFlow Lite Step 3. Optimize the Model Summary 13. Using TensorFlow Lite in Android Apps What Is Android Studio? Creating Your First TensorFlow Lite Android App Step 1. Create a New Android Project Step 2. Edit Your Layout File Step 3. Add the TensorFlow Lite Dependencies Step 4. Add Your TensorFlow Lite Model Step 5. Write the Activity Code to Use TensorFlow Lite for Inference Moving Beyond “Hello World”—Processing Images TensorFlow Lite Sample Apps Summary 14. Using TensorFlow Lite in iOS Apps Creating Your First TensorFlow Lite App with Xcode Step 1. Create a Basic iOS App Step 2. Add TensorFlow Lite to Your Project Step 3. Create the User Interface Step 4. Add and Initialize the Model Inference Class Step 5. Perform the Inference Step 6. Add the Model to Your App Step 7. Add the UI Logic Moving Beyond “Hello World”—Processing Images TensorFlow Lite Sample Apps Summary 15. An Introduction to TensorFlow.js What Is TensorFlow.js? Installing and Using the Brackets IDE Building Your First TensorFlow.js Model Creating an Iris Classifier Summary 16. Coding Techniques for Computer Vision in TensorFlow.js JavaScript Considerations for TensorFlow Developers Building a CNN in JavaScript Using Callbacks for Visualization Training with the MNIST Dataset Running Inference on Images in TensorFlow.js Summary 17. Reusing and Converting Python Models to JavaScript Converting Python-Based Models to JavaScript Using the Converted Models Using Preconverted JavaScript Models Using the Toxicity Text Classifier Using MobileNet for Image Classification in the Browser Using PoseNet Summary 18. Transfer Learning in JavaScript Transfer Learning from MobileNet Step 1. Download MobileNet and Identify the Layers to Use Step 2. Create Your Own Model Architecture with the Outputs from MobileNet as Its Input Step 3. Gather and Format the Data Step 4. Train the Model Step 5. Run Inference with the Model Transfer Learning from TensorFlow Hub Using Models from TensorFlow.org Summary 19. Deployment with TensorFlow Serving What Is TensorFlow Serving? Installing TensorFlow Serving Installing Using Docker Installing Directly on Linux Building and Serving a Model Exploring Server Configuration Summary 20. AI Ethics, Fairness, and Privacy Fairness in Programming Fairness in Machine Learning Tools for Fairness The What-If Tool Facets Federated Learning Step 1. Identify Available Devices for Training Step 2. Identify Suitable Available Devices for Training Step 3. Deploy a Trainable Model to Your Training Set Step 4. Return the Results of the Training to the Server Step 5. Deploy the New Master Model to the Clients Secure Aggregation with Federated Learning Federated Learning with TensorFlow Federated Google’s AI Principles Summary Index --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.at/products/533083667-ai-and-machine-learning-for-coders-a-programmers-guide-to](https://www.desertcart.at/products/533083667-ai-and-machine-learning-for-coders-a-programmers-guide-to) --- *Product available on Desertcart Austria* *Store origin: AT* *Last updated: 2026-06-02*