AI Solution Architecture

This course is for solution architects, data professionals, and tech leads looking to integrate AI into their frameworks. It focuses on real-world applications, a 'Data First' approach, and mastering data science techniques for strategic AI success.

Deep Learning is the application of artificial neural networks to solve complex problems and commercial problems. There are several practical applications that have already been built using these techniques, such as: self-driving cars, development of new medicines, diagnosis of diseases, automatic generation of news, facial recognition, product recommendation, forecast of stock prices, and many others! The technique used to solve these problems is artificial neural networks, which aims to simulate how the human brain works. They are considered to be the most advanced techniques in the Machine Learning area.One of the most used libraries to implement this type of application is Google TensorFlow, which supports advanced architectures of artificial neural networks. There is also a repository called TensorFlow Hub which contains pre-trained neural networks for solving many kinds of problems, mainly in the area of Computer Vision and Natural Language Processing. The advantage is that you do not need to train a neural network from scratch! Google itself provides hundreds of ready-to-use models, so you just need to load and use them in your own projects. Another advantage is that few lines of code are needed to get the results!In this course you will have a practical overview of some of the main TensorFlow Hub models that can be applied to the development of Deep Learning projects! At the end, you will have all the necessary tools to use TensorFlow Hub to build complex solutions that can be applied to business problems. See below the projects that you are going to implement:Classification of five species of flowers Detection of over 80 different objects Creating new images using style transfer Use of GANs (generative adversarial network) to complete missing parts of images Recognition of actions in videos Text polarity classification (positive and negative)Use o

Welcome to "Machine Learning and Data Science with LangChain and LL Ms"! This comprehensive course is designed to equip you with the skills and knowledge needed to harness the power of LangChain and Large Language Models (LL Ms) for advanced data science and machine learning tasks.In today’s data-driven world, the ability to process, analyze, and extract insights from large volumes of data is crucial. Language models like GPT have transformed how we interact with and utilize data, allowing for more sophisticated natural language processing (NLP) and machine learning applications. LangChain is an innovative framework that enables you to build applications around these powerful LL Ms. This course dives deep into the integration of LL Ms within the data science workflow, offering hands-on experience with real-world projects.What You Will Learn?Throughout this course, you will gain a thorough understanding of how LangChain can be utilized in various data science applications, along with the practical knowledge of how to apply LL Ms in different scenarios. Starting with the basics of machine learning and data science, we gradually explore the core concepts of LL Ms and how LangChain can enhance data-driven solutions.Key Learning Areas:1. Introduction to Machine Learning and Data Science: Begin your journey by understanding the core principles of machine learning and data science, including the types of data, preprocessing techniques, and model-building strategies.2. Exploring Large Language Models (LL Ms): Learn what LL Ms are, how they function, and their applications in various domains. This section covers the latest advancements in language models, including their architecture and capabilities in text generation, classification, and more.3. LangChain Fundamentals: Discover the potential of LangChain as a tool for

Unlock the potential of data-driven insights with our comprehensive course, "Deep Dive into Mastering Data Science and Machine Learning." In today's data-driven world, the ability to extract knowledge, predict trends, and make informed decisions is a crucial skill. This course is designed to empower you with the expertise required to navigate the intricate landscape of data science and machine learning.Course Highlights:Dive into Data: Learn to wrangle, clean, and preprocess data from various sources, preparing it for in-depth analysis. Discover techniques to identify and handle missing values, outliers, and anomalies that could affect your analysis.Algorithm Mastery: Delve into the world of machine learning algorithms, from foundational concepts to cutting-edge techniques. Understand the nuances of classification, regression, clustering, and recommendation systems, and explore ensemble methods and deep learning architectures for enhanced performance.Visualize Insights: Develop the art of data visualization to effectively communicate your findings. Learn to create compelling graphs, plots, and interactive dashboards that bring data to life and aid decision-making.Real-world Projects: Put theory into practice with hands-on projects that simulate real-world scenarios. Tackle challenges ranging from predicting customer behavior to image recognition, gaining experience that mirrors the complexities of the field.Ethical and Transparent AI: Understand the ethical considerations in data science and machine learning. Explore methods to interpret and explain model predictions, ensuring transparency and accountability in your applications.Model Deployment: Take your models from the development stage to real-world deployment. Learn about containerization, cloud services, and deployment pipelines, ensuring your solutions are accessible and scalable.Peer Learning: Engage with a

MASTER ENTERPRISE AI AGENTS & FUTURE-PROOF YOUR CAREER2025 is the year AI agents enter the workforce. While 47% of companies believe organizations not using AI will fail, only 15% have skilled AI engineers. Don't get left behind.Transform from curious learner to Professional AI Agent Engineer using LangChain, Lang Graph, CrewAI, Auto Gen, and RAG systems with the same enterprise patterns deployed by Netflix, Google, and Fortune 500 companies. Master the complete journey from cost-free local development with Ollama to production enterprise deployment.WHY THIS AI AGENTS BOOTCAMP IS DIFFERENTELIMINATE COST BARRIERS: Start with 100% FREE local models using Ollama, Deep Seek-R1, and Llama 3.2, then scale intelligently to enterprise cloud when needed. No more $200/month GPT-4o bills blocking your learning with ANY LLM provider flexibility.ENTERPRISE-GRADE AI AGENT ARCHITECTURE: Learn the same multi-agent orchestration patterns using LangChain, Lang Graph, CrewAI, and Auto Gen that tech giants use to save millions in operational costs and scale AI agents to millions of users.2025 CUTTING-EDGE AI AGENT STACK: Master Deep Seek-R1 (competitive with OpenAI o1 at 96% lower cost), Lang Graph workflows, CrewAI multi-agent systems, and Auto Gen coordination patterns before your competition.COMPLETE PROFESSIONAL AI AGENT TECHNOLOGY STACKCORE ENTERPRISE AI FRAMEWORKS: LangChain & Lang Graph: AI agent orchestration with ANY LLM provider RAG Systems: Vector search with FAISS, ChromaDB, Pinecone for intelligent document retrieval Multi-Agent Systems: Auto Gen team coordination, CrewAI role-based agents, advanced orchestration patterns Visual Development: Langflow no-code AI agent pipelines for rapi

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CP Us or GP Us in a desktop, server, or mobile device with a single API. So if you’re looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support Embedded with solid projects and examples to teach you how to implement TensorFlow in production Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with Tensor Board – all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more,

Artificial intelligence is one of the most rapidly evolving fields in technology, and ChatGPT is at the forefront of this revolution. By understanding ChatGPT and its capabilities, you will have the knowledge to create innovative solutions in a wide range of industries, from finance and healthcare to education and entertainment.This course is designed to provide a comprehensive overview of ChatGPT for beginners who are interested in exploring the possibilities of artificial intelligence. ChatGPT is a powerful language model trained by OpenAI, based on the GPT-3.5 architecture, and is capable of generating human-like responses to a wide range of prompts.In this masterclass, you will learn how to use ChatGPT to generate text, including content creation and creating ebooks.In addition to its use for content creation and ebooks, ChatGPT has also become an essential tool for aspiring AI consultants. The course includes a section on how to maximize the potential of ChatGPT as an AI consultant, including best practices for working with clients and integrating the technology into existing workflows.Whether you are a beginner exploring the possibilities of AI or an experienced consultant looking to expand your skillset, this masterclass will provide you with a comprehensive understanding of ChatGPT and its potential applications.

This complete Generative AI course takes you from beginner to advanced with hands-on projects, real-world applications, and career-ready skills. You’ll learn the foundations of Generative AI, explore Large Language Models (LL Ms), master frameworks like LangChain, Llama Index, CrewAI, and PydanticAI, and deploy your own AI solutions on the cloud. The course is tailored to equip you with both the knowledge and practical experience required to step into a Generative AI Engineer role.Each section includes quizzes & coding exercises to help you test your knowledge and reinforce your skills.What you’ll learn in each section1. Introduction – Get started with the course, understand what you will learn & set up Python environments (Colab, Jupyter, Py Charm).2. Generative AI – Foundation – Understand AI vs ML vs DL vs GenAI, dive into Large Language Models, and learn the Transformer architecture.3. Accessing LL Ms in Python – Use OpenAI, Gemini, Groq, and Ollama LL Ms, and connect them through LangChain and Llama Index.4. Prompt Engineering – Explore prompt templates, zero-shot, and few-shot prompting to effectively interact with LL Ms.5. Building GenAI Chatbots – Build and deploy chatbots step by step using LangChain, Llama Index, Streamlit UI, and Streamlit Cloud.6. Retrieval-Augmented Generation (RAG) – Understand RAG, build RAG pipelines with LangChain and Llama Index, and create a PDF Q&A bot.7. AI Agents – Learn what AI agents are and build agents with PydanticAI, Auto Gen, and CrewAI for multi-agent workflows.8. LLM Deployment – Deploy open-source LL Ms with Ollama, Docker, and vLLM, and set them up on AWS EC2 for real-world usage.

Step into the world of Generative AI and Large Language Models (LL Ms) with this Complete Generative AI Mastery Course, an immersive, project-driven program designed to take you from fundamentals to professional-level mastery. In this Generative AI course, you will build production-grade AI applications using industry-standard frameworks such as LangChain, LLaMA 3, FAISS, and Milvus — the same technologies powering real-world enterprise and research-grade AI systems.The course begins with a deep dive into the core concepts of Transformers, GANs, embeddings, and foundation models, helping you understand how modern generative models process and generate human-like content. You will then explore Retrieval-Augmented Generation (RAG), vector databases, and multimodal AI to create powerful, context-aware, and intelligent solutions for text, image, and video understanding.Through 12+ guided, hands-on projects, you will build:AI chatbots powered by LL Ms Intelligent document retrieval &amp; RAG systems Image generation &amp; Vision AI applications Semantic similarity search enginesAI-powered video retrieval systems You will work with cutting-edge models and architectures, including T5 and multimodal models, while applying best practices for real-world system design.By the end of this course, you will master the complete Generative AI pipeline — from data ingestion, embeddings, model chaining, fine-tuning, and optimization to scalable deployment across edge, cloud, and hybrid environments.Whether you are a Python developer, AI enthusiast, data scientist, researcher, or tech innovator, this course equips you with the <st

Course Description This tutorial course is a practical, project driven introduction to Machine Learning and Deep Learning using PyTorch. Each concept is taught through real world examples, allowing professionals to quickly understand, how models work and how they are used in real applications. You will build complete end to end projects such as LSTMs based sentiment analysis, RNNs based spam detection, CNNs models for image classification, MLPs networks for video quality prediction, and regression models using real datasets from sales, finance, and home loan scenarios. This tutorial course also covers how to convert Jupyter Notebook experiments into a clean, modular Python project structure suitable for production use.By combining NLP, computer vision, and predictive analytics use cases, this tutorial course helps you gain solid practical experience in PyTorch while learning how to preprocess data, design model architectures, train models, evaluate results, and prepare solutions for real-world implementation.This Tutorial Course Primarily Focuses On:Building ML &amp; DL models end to end in PyTorch Performing data preprocessing and feature engineering Training, evaluating, and deploying models with real datasets Understanding architectures like LSTMs, CNNs, DNNs, Decision Trees, Random Forest &amp; MLPs Converting research notebooks into production ready Python modules By the end of this course, You will be able to Build machine learning regression &amp; classification models Develop CNNs, RNNs, MLPs, and LSTMs architectures in PyTorch Perform NLP tasks like sentiment analysis &amp; spam detection Implement image classification models for handwritten alphabets &amp; traffic signs Convert notebooks into modular Python project structures Work with real time data for prediction and quality assessment You will learn in this tutorial course Dec

Embark on a transformative journey into the world of Generative AI &amp; Prompt Engineering. This comprehensive course will equip you with the skills and knowledge to harness the full potential of Large Language Models (LL Ms) and prompt engineering, propelling your career to new heights in the AI-driven era.What you'll learn:Deep Dive into LL Ms: Gain an in-depth understanding of how LL Ms work, their capabilities, and their limitations. Explore various LLM architectures like transformers and their applications across diverse domains. Develop deep understanding of highly technical concepts like self-attention but in extremely easy to understand language. Master Prompt Engineering: Learn the art and science of crafting effective prompts to elicit desired responses from LL Ms. Discover advanced techniques for fine-tuning outputs, controlling style, and ensuring accuracy.Evaluate AI and LLM Performance: Delve into essential evaluation metrics to assess the effectiveness of AI models and LL Ms. Learn how to interpret results, identify areas for improvement, and make informed decisions.Become a Gen AI Expert: Through hands-on projects and real-world examples, gain practical experience with LL Ms and prompt engineering. Develop the expertise to leverage Gen AI tools for innovative solutions and creative problem-solving.Unlock Career Success: Position yourself for success in the AI-driven job market. Learn how to apply prompt engineering to enhance productivity, streamline workflows, and drive innovation in your field.BERT Model with Hugging Face- implement BERT model for sentiment analysis using Hugging Face transformers.By the end of this course, you'll be able to:Confidently navigate the world of Generative AI and LL Ms.Craft prec

Generative AI: From Fundamentals to Advanced Applications This comprehensive course is designed to equip learners with a deep understanding of Generative AI, particularly focusing on Large Language Models (LL Ms) and their applications. You will delve into the core concepts, practical implementation techniques, and ethical considerations surrounding this transformative technology.What You Will Learn:Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.LLM Architecture and Training: Gain insights into the architecture of LL Ms, their training processes, and the factors influencing their performance.Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.Fine-Tuning and Optimization: Learn how to tailor LL Ms to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.RAG and Real-World Applications: Discover how to integrate LL Ms with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.Ethical Considerations: Understand the ethical implications of Generative AI and responsible AI practices.By the end of this course, you will be equipped to build and deploy robust Generative AI solutions, addressing real-world challenges while adhering to ethical guidelines. Whether you are a data scientist, developer, or business professional, this course will provide you with the necessary skills to thrive in the era of Generative AI.Course Structure:The course is structured into 12 sections, covering a wide range of topics from foundational concepts to advanced techniques. Each section includes multiple lectures, providing a comp

Deep Learning with TensorFlow focuses on building and deploying advanced neural network models that mimic the human brain’s learning capabilities to solve complex problems. This topic explains the architecture of deep neural networks, including layers, neurons, activation functions, loss functions, backpropagation, and optimization techniques. Learners explore how TensorFlow, a leading open-source framework, enables the design, training, and deployment of deep learning models efficiently, handling large datasets and computational requirements. Practical applications such as image classification, object detection, natural language processing, speech recognition, and recommendation systems are highlighted to show real-world relevance. The topic also covers hyperparameter tuning, model evaluation, performance optimization, and techniques to prevent overfitting or underfitting. Learners gain a comprehensive understanding of how to preprocess data, structure neural networks, and apply advanced algorithms to achieve accurate and reliable results. This topic is ideal for students, AI enthusiasts, developers, and data scientists seeking practical deep learning expertise. By mastering Deep Learning with TensorFlow, learners develop the skills necessary to build intelligent systems that solve complex problems, contribute to innovation in AI-driven industries, and prepare for advanced roles in artificial intelligence, data science, and machine learning engineering. The knowledge gained empowers learners to create scalable, high-performing AI solutions that can be applied across multiple sectors, from technology to business intelligence.