Generative AI for Java and Spring Developers Specialization

Colleagues, in the “Generative AI for Java and Spring Developers Specialization” learn how to set up a Java environment optimized for AI libraries, and use Deeplearning4j to build neural network apps, image recognition, and recommendation systems in Java. You’ll master debugging, deploying, and managing Spring AI apps with robust configuration, logging, and error handling. Complete this specialization ready to design and integrate AI-powered service layers, RESTful APIs, and components into Spring apps. Share your portfolio projects with your peers, managers, and prospective employers. You will learn job-ready GenAI skills businesses need for building Java AI-powered apps more quickly and efficiently, build and deploy Java AI apps using libraries, neural networks, Spring integration, and best practices for testing, APIs, and error handling, explain and apply prompt engineering techniques, best practices, and tools for creating effective prompts for generative AI, and use generative AI tools to write and improve code, design apps, solve problems, and address ethical concerns in AI-assisted programming. Key projects include: 1) Popular generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia to generate text, images, and code, 2) Prompt engineering techniques and best practices, 3) CodeCraftHub to build personalized learning for developers, and 4) Building an intelligent customer service platform that leverages both pure Java AI capabilities and Spring AI framework features. This program features 4 training modules: Introduction and Applications, Prompt Engineering Basics, Elevate your Software Development Career, and AI for Java and Spring Development. 

Enroll today (teams & executives are welcome): https://imp.i384100.net/091o9M 

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

Much career success, AI Academy (please subscribe and share with your team)

Microsoft’s Operationalizing Machine Learning and Generative AI Solutions (Exam AI-300)

Colleagues, “Microsoft’s Operationalizing Machine Learning and Generative AI Solutions (Exam AI-300)” course follows Microsoft’s study guide for the AI-300 exam. Each video in the course covers a topic outlined in the exam. A successful candidate for this exam and in the field of Machine Learning operations (MLOps) or Generative AI ops (GenAIOps) needs to understand the MLOps infrastructure, workspace infrastructure, automation, how to manage models, and configure agents. Learn how to: Design and implement an MLOps infrastructure. Implement machine learning model lifecycle and operations. Design and implement a GenAIOps infrastructure. Implement generative AI quality assurance and observability. And Optimize generative AI systems and model performance. Skill-focused training modules include: 1) Design and implement an MLOps infrastructure - Create and manage resources in a Machine Learning workspace, Create and manage assets in a Machine Learning workspace, Implement IaC for Machine Learning, 2) Implement machine learning model lifecycle and operations  - Orchestrate model training, Implement model registration and versioning, Deploy machine learning models for production environments, Monitor and maintain machine learning models in production, 3) Design and implement a GenAIOps infrastructure - Implement Foundry environments and platform configuration, Deploy and manage foundation models for production workloads, Implement prompt versioning and management with source control, 4) Implement generative AI quality assurance and observability - Configure evaluation and validation for generative AI applications and agents, Implement observability for generative AI applications and agents, and 5) Optimize generative AI systems and model performance - Optimize retrieval-augmented generation (RAG) performance and accuracy, and Implement advanced fine-tuning and model customization.

Enroll today (teams & execs welcome): https://tinyurl.com/34pewrmj

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Agentic AI (DeepLearning.AI)

AI Colleagues, taught by Dr. Andrew Ng the “Agentic AI” program represents a new way of building software that leverages LLMs to complete some or all of the steps in complex tasks. Instead of generating single responses to prompts, agentic workflows enable AI to plan multi-step processes, execute them iteratively, and improve outputs through reflection and tool use. This course teaches you to build these sophisticated AI systems from the ground up.

You will master four design patterns that power agentic AI systems:

Reflection: AI critiques its own work and iterates to improve quality—like code review, but automated.

Tool Use: Connect AI to databases, APIs, and external services so it can actually perform actions, not just generate text.

Planning: Break complex tasks into executable steps that AI can follow and adapt when things don’t go as expected.

Multi-Agent: Coordinate multiple specialized AI systems to handle different parts of a complex workflow.

And you will learn to: Build agentic design patterns: reflection, tool use, planning, and multi-agent workflows. Integrate AI with external tools: databases, APIs, web search, and code execution. And Evaluate and optimize AI systems: performance metrics, error analysis, and production deployment. Skill-based training lessons include: 1) Introduction to Agentic Workflows, 2) Reflection Design Pattern, 3) Tool Use, 4) Practical Tips for Building Agentic AI, and 5) Patterns for Highly Autonomous Agents. 

Enroll today (teams & executives are welcome): https://imp.i384100.net/xJPWod

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

Much career success, AI Academy (please subscribe and share with your team)

Agentic AI with LangGraph, CrewAI, AutoGen and BeeAI (IBM)

AI Colleagues, the “Agentic AI with LangGraph, CrewAI, AutoGen and BeeAI” training from IBM provides a structured approach to designing AI-powered systems using agentic design principles, orchestration strategies, and proven workflow patterns. You’ll explore popular frameworks such as LangGraph, CrewAI, BeeAI, and AG2 (formerly AutoGen), and learn how to select the right one for your needs. You will start with LangGraph, applying key design patterns such as sequential flows, routing, and parallelization to structure agent interactions. From there, you’ll move to CrewAI, where you’ll orchestrate agents, tasks, and tools, generate structured outputs using YAML and Pydantic, and extend capabilities with custom functions. Finally, you’ll explore BeeAI for orchestrating agents and workflows, and AG2 for creating multi-agent conversations and role-based collaboration. You will learn to: Design optimized AI systems by selecting and combining appropriate agentic frameworks and architectural patterns. Implement AI workflow patterns using agentic design principles and LangGraph. Build structured multi-agent workflows using CrewAI, including agents, tasks, and custom tools. Develop AI applications with BeeAI and design conversation-driven interactions using AG2 (AutoGen). And gain hands-on skills including AI Integrations, Generative AI Agents, Agentic Systems, Memory Management, Large Language Modeling, and Software Design Patterns while using AI Workflows, Agentic Workflows, and AI Orchestration. Training modules address: 1) Agentic Frameworks and LagGraph Design, 2) CrewAI Fundamentals and Advanced Applications, and 3) Alternative Agentic Frameworks: BeeAI and AG2 (AutoGen).

Register today (teams & executives are welcome): https://imp.i384100.net/OYJEJn

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

4 - The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age  (Audible) (Kindle) 

Much career success, AI Academy (kindly subscribe and share with your peers)

IBM Generative AI Engineering Professional Certificate

Colleagues, in the “IBM Generative AI Engineering Professional Certificate” program you will build highly sought-after gen AI engineering skills and practical experience in just 6 months. Acquire job-ready skills employers are crying out for in gen AI, machine learning, deep learning, NLP apps, and large language models in just 6 months. Build and deploy generative AI applications, agents and chatbots using Python libraries like Flask, SciPy and ScikitLearn, Keras, and PyTorch. Key gen AI architectures and NLP models, and how to apply techniques like prompt engineering, model training, and fine-tuning. Apply transformers like BERT and LLMs like GPT for NLP tasks, with frameworks like RAG and LangChain. Gain highly marketable skills in Machine Learning, Algorithms, Artificial Neural Networks, Deep Learning, Human Learning, Machine Learning Algorithms, Network Model, Applied Machine Learning, Network Architecture, Python Programming, and Software Testing. Take a deep dive into AI, gen AI, and prompt engineering, along with data analysis, machine learning, and deep learning using Python. You'll work with libraries like SciPy and scikit-learn and build apps using frameworks and models such as BERT, GPT, and LLaMA. You'll use Hugging Face Transformers, PyTorch, RAG, and LangChain for developing and deploying LLM NLP-based apps, while exploring tokenization, language models, and transformer techniques. Skill-based training modules include: 1) Introduction to Artificial Intelligence (AI), 2) Generative AI: Introduction and Applications, 2) Prompt Engineering Basics, 3) Python for Data Science, AI & Development, 4) Developing AI Applications with Python and Flask, 4) Building Generative AI-Powered Applications with Python, 5) Data Analysis with Python, 6) Machine Learning with Python, 7) Introduction to Deep Learning & Neural Networks with Keras, 8) Generative AI and LLMs: Architecture and Data Preparation, 9) Gen AI Foundational Models for NLP & Language Understanding, 10) Generative AI Language Modeling with Transformers, 11) Generative AI Engineering and Fine-Tuning Transformers, 12) Generative AI Advance Fine-Tuning for LLMs, 13) Fundamentals of AI Agents Using RAG and LangChain, and 14) Project: Generative AI Applications with RAG and LangChain. 

Enroll today (teams & execs welcome): https://imp.i384100.net/DykgDy  

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Certified Quantum Computing Expert™ (CQCE)

Colleagues, the “Certified Quantum Computing Expert (CQCE)” training is a structured quantum computing program designed to help learners connect the foundations of quantum mechanics with computational methods and real-world technology decisions. The course introduces the mathematics required to describe quantum states before progressing to quantum circuits, hardware, algorithms, programming, and organisational readiness. It covers classical and quantum computation, probability, vectors, matrices, complex amplitudes, phase, and Dirac notation. Learners examine how superposition, measurement, interference, and entanglement shape quantum information, building the conceptual foundation needed to understand, read, and construct simple quantum circuits. You will learn how quantum gates transform quantum states, how measurement produces classical outcomes, and how quantum processors interact with their control and measurement systems. The curriculum compares major quantum hardware approaches, introduces key algorithms including Shor's algorithm, Grover's algorithm, QFT, phase estimation, VQE, and QAOA, and examines blockchain cryptography, quantum threats, and the transition to post-quantum cryptography. The final module connects quantum application assessment with a practical roadmap for organisational readiness and cryptographic transition. Gain high-demand skills: Modern software engineering practice, Cloud platforms and infrastructure, Data engineering and analytics pipelines, Cybersecurity fundamentals, DevOps, automation and CI/CD, System design and architecture, Emerging technology trends, Foundations of Quantum Computing, Mathematics for Quantum Computing, and Qubits and Quantum Principles. Training lessons include: 1) Foundations of Quantum Computing, 2) Mathematics for Quantum Computing, 3) Qubits and Quantum Principles, 4) Quantum Gates, Entanglement, and Circuits, 5) Quantum Hardware and the NISQ Era, 6) Quantum Algorithms and Quantum Advantage, 7) Hands-on Quantum Programming, 8) Quantum Computing and Blockchain, 9) Post-Quantum Cryptography and Quantum-Safe Blockchain, and 10) Quantum Applications, Business Impact, and Readiness.

Enroll today (teams & execs are welcome): https://tinyurl.com/2pj93z7d

Recommended Listening-Reading: “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle) 

Much career success, Online Learning Central (kindly subscribe and share with your team)

Generative AI Learning Path Specialization

Colleagues, in the “Generative AI Learning Path Specialization” you will explore the foundations of large language models (LLMs), their diverse applications, and the ethical considerations crucial for responsible AI development and deployment. The Applied Learning Project focuses on LLMs and responsible AI, features interactive quizzes throughout the modules. These quizzes assess comprehension of key concepts and terminology. Participants receive immediate feedback, reinforcing learning and identifying areas for further exploration. Training lessons cover: 1) Introduction to Generative AI, 2) Introduction to Large Language Models, 3) Introduction to Responsible AI, and 4) Responsible AI: Applying AI Principles with Google Cloud. Gain high-demand skills involving AI Product Strategy, Deep Learning, Statistical Machine Learning, AI Literacy, Responsible AI, Generative Model Architectures, and LLM Applications.

Enroll today (teams & executives are welcome): https://imp.i384100.net/XY06Ja 

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

Much career success, AI Academy (please subscribe and share with your team)

Generative Adversarial Networks (GANs) Specialization

Colleagues, rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, and turning 2D images to 3D. In the “Generative Adversarial Networks (GANs) Specialization” you will: Understand GAN components, build basic GANs using PyTorch and advanced DCGANs using convolutional layers, control your GAN and build conditional GAN. Compare generative models, use FID method to assess GAN fidelity and diversity, learn to detect bias in GAN, and implement StyleGAN techniques. Use GANs for data augmentation and privacy preservation, survey GANs applications. Examine and build Pix2Pix and CycleGAN for image translation. And you will acquire highly marketable skills with Data Synthesis, Machine Learning, Model Evaluation, Image Analysis, Responsible AI, Deep Learning, Convolutional Neural Networks, Image Quality, Data Ethic, Generative Model Architecture, Information Privacy, and Model Training. Tools you will learn cover: PyTorch (Machine Learning Library), Generative AI, and Generative Adversarial Networks (GANs). Skill-based lessons include: 1) Build Basic Generative Adversarial Networks (GANs), 2) Build Better Generative Adversarial Networks (GANs), and 3) Apply Generative Adversarial Networks (GANs).

Enroll today (teams & executives are welcome): https://imp.i384100.net/Jk4G4N

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

3 - The Race for Quantum Computing (Audible) (Kindle) 

Much career success, AI Academy (subscribe & share with your team)

Prompt Design, Grounding, and RAG with Vertex AI Specialization

Colleagues, in the “Prompt Design, Grounding, and RAG with Vertex AI Specialization” begin with generative AI and large language model fundamentals, then explore prompt engineering and Vertex AI Studio. As you progress, you’ll work with text and multimodal embeddings, grounding techniques, controlled generation, and multimodal Gemini use cases. You’ll then apply these concepts to semantic search, hybrid search, multimodal RAG, and LLM-powered application architectures. Build Grounded Generative AI Applications. Design prompts, ground Gemini responses, and build RAG applications with Vertex AI. You will learn to: Design effective prompts for Gemini and Vertex AI using prompt patterns, configurations, and tuning techniques. Build grounded RAG workflows with embeddings, vector search, and your own documents or multimodal data. Create generative AI applications that combine Gemini, multimodal inputs, semantic search, and hybrid retrieval. And you will gain hands-on experience with Metadata Management, Embeddings, Prompt Patterns, Data Stores, Generative AI Agents, Google Cloud Platform, AI Literacy, Large Language Modeling, Retrieval-Augmented Generation, Artificial Intelligence, LLM Application, Image Analysis, Multimodal Prompts, Prompt Engineering Tools, and Generative Model Architectures. And acquire knowledge of high-demand tools including Google Gemini, Prompt Engineering, Generative AI, Vector Databases, and Gemini. Training lessons cover: 1) Generative AI, 2) Large Language Models, 3) Vertex AI Studio, 4) Generative AI with Vertex AI: Prompt Design, 5) Grounding Gemini Models in Vertex AI, 6) Create Generative AI Apps on Google Cloud, 7) Multimodal Use Cases with Gemini, 8) Vertex AI Embeddings: Text and Multimodal, 9) Vector Search and Embeddings, and 10) Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API. 

Enroll today (teams & executives are welcome): https://imp.i384100.net/ZV3W6X

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

4 - The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age  (Audible) (Kindle) 

Much career success, AI Academy (kindly subscribe and share with your peers)