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AI and ML for Coders: Applying Core ML algorithms, deep learning models, and MLOps best practices
- AI and ML
- Categories:Experiments, Reference & Workbooks New Technology & Discoveries
- Language:English（Translation Services Available）
- Publication Place:India
- Publication date:May,2025
- Pages:412
- Retail Price:39.95 USD
- Size:190mm×234mm
- Text Color:（Unknown）
- Words:（Unknown）
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Description
AI and ML are reshaping industries and creating unprecedented opportunities for innovation. They play a crucial role in helping businesses grow in a multitude of use cases and create applications, used by millions worldwide. Designed for coders of all levels, this book bridges the gap between theoretical concepts and real-world application, empowering you to build intelligent systems.
In this book, the readers will work with code, tackling fundamental topics like ML, by grasping core principles through practical coding exercises, followed by computer vision, where the code is trained to see the world and learn image processing techniques like feature detection, empowering applications to analyze and interpret visual data. This is followed by natural language processing (NLP), which enables the software to understand and manipulate language by utilizing techniques like tokenization, sentence sequencing, and more. Additionally, this book also talks about sequence modeling, whereby readers master techniques like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, as well as MLOps for deploying and scaling your AI/ML solutions on-premise and in the cloud, with tools like TensorFlow Extended (TFX) and Kubeflow.
By the end of the book, readers will learn to build ML models, deploy AI on diverse platforms, and serve models online and in the cloud, ensuring smooth and scalable AI solutions. They will be equipped with the knowledge of industry-standard tools and best practices.
WHAT YOU WILL LEARN
● Implement ML models with Scikit-learn and TensorFlow across various tasks.
● Build NLP applications with text processing, embeddings, and sequence models.
● Deploy and scale ML models using MLOps, TensorFlow Serving, and mobile tools.
● Learn to bring innovative changes and solutions to use cases across industries.
● Develop scalable solutions using CNNs, object detection, and segmentation.
WHO THIS BOOK IS FOR
This book is for coders and software engineers, from novice to experienced, aiming to integrate AI and ML to enhance their IT systems. While familiarity with core software engineering concepts is beneficial, the book assumes only a basic understanding of programming principles, making it accessible to a broad range of professionals.
In this book, the readers will work with code, tackling fundamental topics like ML, by grasping core principles through practical coding exercises, followed by computer vision, where the code is trained to see the world and learn image processing techniques like feature detection, empowering applications to analyze and interpret visual data. This is followed by natural language processing (NLP), which enables the software to understand and manipulate language by utilizing techniques like tokenization, sentence sequencing, and more. Additionally, this book also talks about sequence modeling, whereby readers master techniques like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, as well as MLOps for deploying and scaling your AI/ML solutions on-premise and in the cloud, with tools like TensorFlow Extended (TFX) and Kubeflow.
By the end of the book, readers will learn to build ML models, deploy AI on diverse platforms, and serve models online and in the cloud, ensuring smooth and scalable AI solutions. They will be equipped with the knowledge of industry-standard tools and best practices.
WHAT YOU WILL LEARN
● Implement ML models with Scikit-learn and TensorFlow across various tasks.
● Build NLP applications with text processing, embeddings, and sequence models.
● Deploy and scale ML models using MLOps, TensorFlow Serving, and mobile tools.
● Learn to bring innovative changes and solutions to use cases across industries.
● Develop scalable solutions using CNNs, object detection, and segmentation.
WHO THIS BOOK IS FOR
This book is for coders and software engineers, from novice to experienced, aiming to integrate AI and ML to enhance their IT systems. While familiarity with core software engineering concepts is beneficial, the book assumes only a basic understanding of programming principles, making it accessible to a broad range of professionals.
Author
Suddhasatwa Bhaumik is a seasoned software engineer, passionate about AI/ML. With 18+ years of experience, he has designed data and machine learning systems, led teams, architected solutions, and delivered impactful projects. He has always believed in continuous learning and thoroughly enjoys sharing his limited yet practical knowledge to empower software engineers to embrace the world of data science, machine learning, and AI.
In his current role at Google Cloud India, based out of Pune, he feels extremely proud to be working as a Strategic Cloud Engineer helping customers worldwide in implementing AI and machine learning solutions on Google Cloud.
In his current role at Google Cloud India, based out of Pune, he feels extremely proud to be working as a Strategic Cloud Engineer helping customers worldwide in implementing AI and machine learning solutions on Google Cloud.
Contents
1. Introducing Artificial Intelligence and Machine Learning
2. Machine Learning Fundamentals
3. TensorFlow Essentials
4. Engineering for Machine Learning
5. Machine Learning Algorithms
6. Implementing First ML Models
7. Computer Vision
8. Natural Language Processing
9. Sequence Modelling and Transformers
10. MLOps and Deployment
11. Model Serving and Scalability
12. Model Deployment for Mobile
13. Summary, Future, and Resources
2. Machine Learning Fundamentals
3. TensorFlow Essentials
4. Engineering for Machine Learning
5. Machine Learning Algorithms
6. Implementing First ML Models
7. Computer Vision
8. Natural Language Processing
9. Sequence Modelling and Transformers
10. MLOps and Deployment
11. Model Serving and Scalability
12. Model Deployment for Mobile
13. Summary, Future, and Resources
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