Ad copy

𝗢'𝗥𝗲𝗶𝗹𝗹𝘆'𝘀 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗲𝗕𝗼𝗼𝗸 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 – ₱350 (126 eBooks in PDF/EPUB format) ℹ️ O’Reilly’s Artificial Intelligence and Machine Learning Collection brings together the publisher’s entire AI and ML eBook catalog. It covers the field from foundational concepts to advanced systems, including machine learning, deep learning, NLP, computer vision, reinforcement learning, MLOps, and generative AI. The collection continuously expands as O’Reilly releases new AI and ML titles and updated editions. 📋 𝗕𝗼𝗼𝗸 𝗟𝗶𝘀𝘁: 1. AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence 2. AI and Machine Learning for On-Device Development: A Programmer's Guide 3. AI and ML for Coders in PyTorch: A Coder's Guide to Generative AI and Machine Learning 4. AI at the Edge: Solving Real-World Problems with Embedded Machine Learning 5. AI Engineering: Building Applications with Foundation Models 6. AI for People and Business: A Framework for Better Human Experiences and Business Success 7. AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch 8. AI Value Creators: Beyond the Generative AI User Mindset 9. AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment 10. AI-First Healthcare: AI Applications in the Business and Clinical Management of Health 11. AI-Native Software Delivery: Proven Practices to Produce High-Quality Software Faster 12. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning 13. Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise 14. Applied AI for Enterprise Java Development: Leveraging Generative AI, LLMs, and Machine Learning in the Java Enterprise 15. Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically 16. Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand 17. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning 18. Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud 19. Artificial Intelligence in Finance: A Python-Based Guide 20. Artificial Intelligence with Microsoft Power BI: Simpler AI for the Enterprise 21. Automating Data Quality Monitoring: Scaling Beyond Rules with Machine Learning 22. Azure AI Services at Scale for Cloud, Mobile, and Edge: Building Intelligent Apps with Azure Cognitive Services and Machine Learning 23. Azure OpenAI Service for Cloud Native Applications: Designing, Planning, and Implementing Generative AI Solutions 24. Beyond Vibe Coding: From Coder to AI-Era Developer 25. Blockchain Tethered AI: Trackable, Traceable Artificial Intelligence and Machine Learning 26. Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications 27. Building AI-Powered Products: The Essential Guide to AI and GenAI Product Management 28. Building Applications with AI Agents: Designing and Implementing Multiagent Systems 29. Building Generative AI Services with FastAPI: A Practical Approach to Developing Context-Rich Generative AI Applications 30. Building Machine Learning Pipelines - Automating Model Life Cycles with TensorFlow 31. Building Machine Learning Powered Applications: Going from Idea to Product 32. Building Recommendation Systems in Python and JAX 33. Causal Inference in Python: Applying Causal Inference in the Tech Industry 34. Deep Learning: A Practitioner's Approach 35. Deep Learning at Scale: At the Intersection of Hardware, Software, and Data 36. Deep Learning Cookbook: Practical Recipes to Get Started Quickly 37. Deep Learning for Biology: Harness AI to Solve Real-World Biology Problems 38. Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD 39. Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python 40. Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More 41. Deep Learning from Scratch: Building with Python from First Principles 42. Designing Autonomous AI: A Guide for Machine Teaching 43. Designing Large Language Model Applications: A Holistic Approach to LLMs 44. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 45. Developing Apps with GPT-4 and ChatGPT: Build Intelligent Chatbots, Content Generators, and More, 2nd Edition 46. Effective Machine Learning Teams: Best Practices for ML Practitioners 47. Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems 48. Explainable AI for Practitioners 49. Feature Engineering for Machine Learning 50. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, 2nd Edition 51. GenAI on Google Cloud: Enterprise Generative AI Systems and Agents 52. Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications 53. Generative AI for Software Development: Building Software Faster and More Effectively 54. Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications 55. Generative Deep Learning 56. Graph-Powered Analytics and Machine Learning with TigerGraph 57. Hands-On Generative AI with Transformers and Diffusion Models 58. Hands-On Large Language Models: Language Understanding and Generation 59. Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems 60. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3rd Edition 61. Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data 62. Implementing MLOps in the Enterprise: A Production-First Approach 63. Introducing MLOps: How to Scale Machine Learning in the Enterprise 64. Introduction to Machine Learning with Python - A Guide for Data Scientists 65. Introduction to Machine Learning with R: Rigorous Mathematical Analysis 66. Kubeflow for Machine Learning: From Lab to Production 67. LangChain for Life Sciences and Healthcare: Innovation Through LLMs and Generative AI Agents 68. Lean AI: How Innovative Startups Use Artificial Intelligence to Grow 69. Learning AI Tools in Tableau: Level Up Your Data Analytics and Visualization Capabilities with Tableau Pulse and Tableau Agent 70. Learning GitHub Copilot: Multiplying Your Coding Productivity Using AI 71. Learning LangChain: Building AI and LLM Applications with LangChain and LangGraph 72. Learning Ray: Flexible Distributed Python for Machine Learning 73. Learning TensorFlow: A Guide to Building Deep Learning Systems 74. Learning TensorFlow.js: Powerful Machine Learning in JavaScript 75. LLMOps: Managing Large Language Models in Production 76. LLMs and Generative AI for Healthcare: The Next Frontier 77. Low-Code AI: A Practical Project-Driven Introduction to Machine Learning 78. Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python 79. Machine Learning and Security - Protecting Systems with Data and Algorithms 80. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 81. Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk 82. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 83. Machine Learning for High-Risk Applications: Approaches to Responsible AI 84. Machine Learning Interviews: Kickstart Your Machine Learning and Data Career 85. Machine Learning Pocket Reference: Working with Structured Data in Python 86. Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines 87. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning, 2nd Edition 88. Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications 89. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit 90. Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning 91. Natural Language Processing with Spark NLP: Learning to Understand Text at Scale 92. Natural Language Processing with Transformers, Revised Edition 93. Practical AI on the Google Cloud Platform: Utilizing Google's State-of-the-Art AI Cloud Services 94. Practical Artificial Intelligence with Swift: From Fundamental Theory to Development of AI-Driven Apps 95. Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions 96. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 97. Practical Fairness: Achieving Fair and Secure Data Models 98. Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images 99. Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI 100. Practical MLOps: Operationalizing Machine Learning Models 101. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems 102. Practical Simulations for Machine Learning: Using Synthetic Data for AI 103. Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines 104. Predictive Analytics for the Modern Enterprise: A Practitioner's Guide to Designing and Implementing Solutions 105. Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI 106. Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python 107. Programming Collective Intelligence - Building Smart Web 2.0 Applications 108. Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications 109. Prompt Engineering for Generative AI 110. Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications 111. PyTorch Pocket Reference: Building and Deploying Deep Learning Models 112. Reinforcement Learning: Industrial Applications of Intelligent Agents 113. Reinforcement Learning for Finance: A Python-Based Introduction 114. Reliable Machine Learning: Applying SRE Principles to ML in Production 115. Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch 116. Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery 117. TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models 118. TensorFlow for Deep Learning 119. The AI Ladder: Accelerate Your Journey to AI 120. The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI 121. The Developer's Playbook for Large Language Model Security: Building Secure AI Applications 122. Thoughtful Machine Learning with Python P A Test-Driven Approach 123. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers 124. Training Data for Machine Learning: Human Supervision from Annotation to Data Science 125. Using Generative AI for SEO: AI-First Strategies to Improve Quality, Efficiency, and Costs 126. Visualizing Generative AI: How AI Paints, Writes, and Assists 💬 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝘂𝘀 𝘁𝗼 𝗮𝘃𝗮𝗶𝗹.