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𝗣𝗮𝗰𝗸𝘁’𝘀 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗲𝗕𝗼𝗼𝗸 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 – ₱395 (197 eBooks in PDF/EPUB format) ℹ️ Packt’s Artificial Intelligence and Machine Learning Collection brings together the publisher’s complete AI and ML catalog. It covers the field from foundational mathematics and classical machine learning to deep learning, generative AI, large language models, multi-agent systems, reinforcement learning, computer vision, NLP, MLOps, and enterprise AI deployment across AWS, Azure, and Google Cloud. The collection continuously expands as new Packt AI and Machine Learning titles and updated editions are released. 📋 𝗕𝗼𝗼𝗸 𝗟𝗶𝘀𝘁: 1. A Handbook of Mathematical Models with Python: Elevate your machine learning projects with NetworkX, PuLP, and linalg 2. Accelerate Deep Learning Workloads with Amazon SageMaker: Train, deploy, and scale deep learning models effectively using Amazon SageMaker 3. Accelerate Model Training with PyTorch 2.X: Build more accurate models by boosting the model training process 4. Active Machine Learning with Python: Refine and elevate data quality over quantity with active learning 5. Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition 6. Agentic Architectural Patterns for Building Multi-Agent Systems 7. Agile Machine Learning with DataRobot: Automate each step of the machine learning life cycle, from understanding problems to delivering value 8. AI for Creative Production: A handbook for the ethical use of AI in creating and processing text, images, video, and audio 9. AI Product Manager’s Handbook: Build, integrate, scale, and optimize products to grow as an AI product manager, 2nd Edition 10. AI-Assisted Programming for Web and Machine Learning: Improve your development workflow with ChatGPT and GitHub Copilot 11. AI-Native LLM Security: Threats, defenses, and best practices for building safe and trustworthy AI 12. AI-Powered Commerce: Building the products and services of the future with Commerce .AI 13. Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker 14. Apache Spark for Machine Learning: Build and deploy high-performance big data AI solutions for large-scale clusters 15. Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures 16. Applied Machine Learning and High Performance Computing on AWS: Accelerate development of machine learning applications following architectural best practices 17. Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more 18. Applied Machine Learning for Healthcare and Life Sciences Using AWS: Transformational AI implementations for biotech, clinical, and healthcare organizations 19. Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition 20. Artificial Intelligence for IoT Cookbook: Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities 21. Artificial Intelligence for Robotics: Build intelligent robots using ROS 2, Python, OpenCV, and AI & ML techniques for real-world tasks, 2nd Edition 22. Artificial Intelligence with Power BI: Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI 23. Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 24. Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x and TensorFlow 2, 2nd Edition 25. Automated Machine Learning on AWS: Fast-track the development of your production-ready machine learning applications the AWS way 26. Automated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions with Azure AutoML 27. Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms 28. AWS Certified Machine Learning – Specialty (MLS-C01) Certification Guide: The ultimate guide to passing the MLS-C01 exam on your first attempt, 2nd Edition 29. Azure Data and AI Architect Handbook: A structured approach to designing data and AI solutions at scale on Microsoft Azure 30. Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure 31. Bayesian Analysis with Python: A practical guide to probabilistic modeling, 3rd Edition 32. Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt 33. Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents 34. Building AI Applications with OpenAI APIs: Leverage ChatGPT, Whisper, and DALL-E APIs to build 10 innovative AI projects, 2nd Edition 35. Building AI Intensive Python Applications: Create intelligent apps with LLMs and vector databases 36. Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications 37. Building LLM Powered Applications: Create intelligent apps and agents with large language models 38. Building Natural Language and LLM Pipelines: Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph 39. Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more 40. ChatGPT and AI for Accountants: A practitioner’s guide to harnessing the power of GenAI to revolutionize your accounting practice 41. ChatGPT for Conversational AI and Chatbots: Learn how to automate conversations with the latest large language model technologies 42. Coding with ChatGPT and Other LLMs: Navigate LLMs for effective coding, debugging, and AI-driven development 43. Computer Vision on AWS: Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker 44. Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning 45. Creators of Intelligence: Industry secrets from AI Leaders that can be easily applied to build and ace your data science career 46. Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly 47. Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models 48. Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data 49. Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment 50. Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models 51. Decoding Large Language Models: An exhaustive guide to understanding, implementing, and optimizing LLMs for NLP applications 52. Deep Learning and XAI Techniques for Anomaly Detection: Integrate the theory and practice of deep anomaly explainability 53. Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology 54. Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection 55. Deep Learning with MXNet Cookbook: Discover an extensive collection of recipes for creating and implementing AI models on MXNet 56. Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition 57. Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition 58. Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF , Third Edition 59. DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM 60. Democratizing Artificial Intelligence with UiPath: Expand automation in your organization to achieve operational efficiency and high performance 61. Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems 62. Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale 63. Enhancing Deep Learning with Bayesian Inference: Create More Powerful, Robust Deep learning Systems with Bayesian Deep learning 64. Essential Guide to LLMOps: Implementing effective LLMOps strategies and tools from data to deployment 65. Exploring Deepfakes: Deploy powerful AI techniques for face replacement and more with this comprehensive guide 66. Feature Store for Machine Learning: Curate, discover, share and serve ML features at scale 67. Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks 68. Generating Creative Images With DALL-E 3: Create accurate images with effective prompting for real-world applications 69. Generative AI Application Integration Patterns: Integrate large language models into your applications 70. Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments 71. Generative AI Foundations in Python: Discover key techniques and navigate modern challenges in LLMs 72. Generative AI on Google Cloud with LangChain: Design scalable generative AI solutions with Python, LangChain, and Vertex AI on Google Cloud 73. Generative AI with Amazon Bedrock: Build, scale, and secure generative AI applications using Amazon Bedrock 74. Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs 75. Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python, 2nd Edition 76. Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications, 2nd Edition 77. Generative AI with Python and TensorFlow 2: Harness the power of generative models to create images, text, and music 78. Generative AI-Powered Assistant for Developers: Accelerate software development with Amazon Q Developer 79. Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE 80. Google Machine Learning and Generative AI for Solutions Architects: Build efficient and scalable AI ML solutions on Google Cloud 81. Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models, 2nd Edition 82. Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms 83. Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications using machine learning techniques 84. Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques, 2nd Edition 85. Hands-On Computer Vision with Detectron2: Develop object detection and segmentation models with a code and visualization approach 86. Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps 87. Hands-On Genetic Algorithms with Python: Apply genetic algorithms to solve real-world AI and machine learning problems, 2nd Edition 88. Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch 89. Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines , Second Edition 90. Hands-On Machine Learning with ML .NET: Getting started with Microsoft ML .NET to implement popular machine learning algorithms in C# 91. Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python 92. Hands-On Music Generation with Magenta: Explore the role of deep learning in music generation and assisted music composition 93. Hands-On One-shot Learning with Python: A practical guide to implementing fast and accurate deep learning models with fewer training samples 94. Hyperparameter Tuning with Python: Boost your machine learning model’s performance via hyperparameter tuning 95. IBM Cloud Pak for Data: An enterprise platform to operationalize data, analytics, and AI 96. Intelligent Document Processing with AWS AI ML: A comprehensive guide to building IDP pipelines with applications across industries 97. Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples, 2nd Edition 98. Journey to Become a Google Cloud Machine Learning Engineer: Build the mind and hand of a Google Certified ML professional 99. Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists, 2nd Edition 100. Learn Model Context Protocol with TypeScript: Build agentic systems in TypeScript with the new standard for AI capabilities 101. Learn OpenAI Whisper: Transform your understanding of GenAI through robust and accurate speech processing solutions 102. Learn TensorFlow Enterprise: Build, manage, and scale machine learning workloads seamlessly using Google’s TensorFlow Enterprise 103. Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition 104. LLM Engineer's Handbook: Master the art of engineering large language models from concept to production 105. Machine Learning and Generative AI for Marketing: Take your data-driven marketing strategies to the next level using Python 106. Machine Learning at Scale with H2O: A practical guide to building and deploying machine learning models on enterprise systems 107. Machine Learning Engineering on AWS: Build, scale, and secure machine learning systems and MLOps pipelines in production 108. Machine Learning Engineering with MLflow: Manage the end-to-end machine learning life cycle with MLflow 109. Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples, 2nd Edition 110. Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition 111. Machine Learning for Emotion Analysis in Python: Build AI-powered tools for analyzing emotion using natural language processing and machine learning 112. Machine Learning for Finance: Principles and practice for financial insiders 113. Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques 114. Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks 115. Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods 116. Machine Learning in Microservices: Productionizing microservices architecture for machine learning solutions 117. Machine Learning Infrastructure and Best Practices for Software Engineers: Take your machine learning software from a prototype to a fully fledged software system 118. Machine Learning Model Serving Patterns and Best Practices: A definitive guide to deploying, monitoring, and providing accessibility to ML models in production 119. Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes 120. Machine Learning Security with Azure: Best practices for assessing, securing, and monitoring Azure Machine Learning workloads 121. Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation 122. Machine Learning Using TensorFlow Cookbook: Create powerful machine learning algorithms with TensorFlow 123. Machine Learning with Amazon SageMaker Cookbook: 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments 124. Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries 125. Machine Learning with LightGBM and Python: A practitioner’s guide to developing production-ready machine learning systems 126. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python 127. Machine Learning with Qlik Sense: Utilize different machine learning models in practical use cases by leveraging Qlik Sense 128. Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, 4th Edition 129. Machine Learning with the Elastic Stack: Gain valuable insights from your data with Elastic Stack’s machine learning features, 2nd Edition 130. Mastering Azure Machine Learning: Execute large-scale end-to-end machine learning with Azure, 2nd Edition 131. Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition 132. Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python 133. Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond, 2nd Edition 134. Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices 135. Mastering spaCy: Build structured NLP solutions with custom components and models powered by spacy-llm, 2nd Edition 136. Mastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion, 2nd Edition 137. Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning 138. MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results, 2nd Edition 139. Microsoft Azure AI Fundamentals AI-900 Exam Guide: Gain proficiency in Azure AI and machine learning concepts and services to excel in the AI-900 exam 140. MLOps with Red Hat OpenShift: A cloud-native approach to machine learning operations 141. Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI, 2nd Edition 142. Modern Generative AI with ChatGPT and OpenAI Models: Leverage the capabilities of OpenAI’s LLM for productivity and innovation with GPT3 and GPT4 143. Natural Language Processing with TensorFlow: The definitive NLP book to implement the most sought-after machine learning models and tasks, 2nd Edition 144. Natural Language Understanding with Python: Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systems 145. Neural Search – From Prototype to Production with Jina: Build deep learning–powered search systems that you can deploy and manage with ease 146. Neuro-Symbolic AI: Design transparent and trustworthy systems that understand the world as you do 147. OpenAI API Cookbook: Build intelligent applications including chatbots, virtual assistants, and content generators 148. Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models 149. Practical Automated Machine Learning Using H2O .ai: Discover the power of automated machine learning, from experimentation through to deployment to production 150. Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production 151. Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications 152. Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks 153. Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS 154. Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats 155. Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep-learning frameworks 156. Python Deep Learning: Understand how deep neural networks work and apply them to real-world tasks, 3rd Edition 157. Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models, 3rd Edition 158. Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases, 4th Edition 159. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 160. Python Natural Language Processing Cookbook: Over 60 recipes for building powerful NLP solutions using Python and LLM libraries, 2nd Edition 161. Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage 162. RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone 163. Responsible AI in the Enterprise: Practical AI Risk Management for Explainable, Auditable, and Safe Models with Hyperscalers and Azure OpenAI 164. scikit-learn Cookbook: Over 80 recipes for machine learning in Python with scikit-learn, 3rd Edition 165. Serverless Machine Learning with Amazon Redshift: Create, train, and deploy machine learning models using familiar SQL commands 166. Synthetic Data for Machine Learning: A revolutionary approach for the future of ML with issues, solutions, case studies, and insights 167. TensorFlow Developer Certificate Guide: Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam 168. The AI Optimization Playbook: Drive business success with proven AI strategies, best practices, and responsible innovation 169. The AI Product Manager’s Handbook: Develop a product that takes advantage of machine learning to solve AI problems 170. The AI Value Playbook: How to make AI work in the real world 171. The Applied AI and Natural Language Processing Workshop: Learn how to use powerful natural language processing techniques within your own artificial intelligence applications, 2nd Edition 172. The Applied Artificial Intelligence Workshop: Start working with AI today, to build games, design decision trees, and train your own machine learning models 173. The Artificial Intelligence Infrastructure Workshop: Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications 174. The Chief AI Officer’s Handbook: Master AI leadership with strategies to innovate, overcome challenges, and drive business growth 175. The Deep Learning Architect’s Handbook: Build and deploy production-ready DL solutions leveraging the latest Python techniques 176. The Definitive Guide to Google Vertex AI: Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices 177. The Future of Finance with ChatGPT and Power BI: Transform your trading, investing, and financial reporting with ChatGPT and Power BI 178. The Kaggle Book: Master data science competitions with machine learning, GenAI, and LLMs, 2nd Edition 179. The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI, 2nd Edition 180. The Machine Learning Workshop: Get ready to develop your own high-performance machine learning algorithms with scikit-learn, 2nd Edition 181. The Midjourney Expedition: Generate creative images from text prompts and seamlessly integrate them into your workflow 182. The Profitable AI Advantage : A business leader's guide to designing and delivering AI roadmaps for measurable results 183. The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models 184. The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems 185. The Statistics and Machine Learning with R Workshop: Unlock the power of efficient data science modeling with this hands-on guide 186. The Supervised Learning Workshop: Predict outcomes from data by building your own powerful predictive models with machine learning in Python , Second Edition 187. The Unsupervised Learning Workshop: Get started with unsupervised learning and simplify unorganized data to make predictions, 2nd Edition 188. Time Series Analysis on AWS: Learn how to build forecasting models and detect anomalies in your time series data 189. TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems, 2nd Edition 190. Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3, 3rd Edition 191. Unity Artificial Intelligence Programming: Add powerful, believable, and fun AI entities in your game with the power of Unity, 5th Edition 192. Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall , 2nd Edition 193. Unlocking the Power of Auto-GPT and Its Plugins: Implement, customize, and optimize Auto-GPT for building robust AI applications 194. Unlocking the Secrets of Prompt Engineering: Master the art of creative language generation to accelerate your journey from novice to pro 195. Using Stable Diffusion with Python: Leverage Python to control and automate high-quality AI image generation using Stable Diffusion 196. Vector Search for Practitioners with Elastic: A toolkit for building NLP solutions for search, observability, and security using vector search 197. XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build, evaluate, and deploy predictive models with expert guidance 💬 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝘂𝘀 𝘁𝗼 𝗮𝘃𝗮𝗶𝗹.