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Free Download Llm Engineering - Master Ai & Large Language Models (Llms) Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.65 GB | Duration: 7h 26m Master Generative AI and Large Language Models (LLMs). Explore and deploy LLM applications, learn fundamental theory. What you'll learn Design and develop a full solution to a given business problem by selecting, training and applying LLMs Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task Solve problems by applying leading open-source platforms, frameworks and tools, including Hugging Face, Gradio and Weights & Biases State the common AI paradigms, and identify the types of business problems most suitable for each Define fundamental data science concepts around deep learning, including training vs inference, generalizing vs overfitting, and the key ideas behind the NN Describe core concepts such as Generative AI, LLMs and the Transformer Architecture, and discuss what can be achieved with state-of-the-art performance Explain how LLMs work in sufficient detail to be able to train and test them, apply them to new scenarios, and diagnose & fix common issues Implement LLM solutions in Python using frontier and open-source models with both APIs and direct inference Execute code to write documents, answer questions and generate images. Requirements Familiarity with Python. This course will not cover Python basics and is completed in Python. Description Mastering Generative AI and LLMs: An 8-Week Hands-On JourneyAccelerate your career in AI with practical, real-world projects led by industry veteran Ed Donner. Build advanced Generative AI products, experiment with over 20 groundbreaking models, and master state-of-the-art techniques like RAG, QLoRA, and Agents.What you'll learn• Build advanced Generative AI products using cutting-edge models and frameworks.• Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models.• Develop proficiency with platforms like HuggingFace, LangChain, and Gradio.• Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents.• Create real-world AI applications, including:• A multi-modal customer support assistant that interacts with text, sound, and images.• An AI knowledge worker that can answer any question about a company based on its shared drive.• An AI programmer that optimizes software, achieving performance improvements of over 60,000 times.• An ecommerce application that accurately predicts prices of unseen products.• Transition from inference to training, fine-tuning both Frontier and Open-Source models.• Deploy AI products to production with polished user interfaces and advanced capabilities.• Level up your AI and LLM engineering skills to be at the forefront of the industry.About the InstructorI'm Ed Donner, an entrepreneur and leader in AI and technology with over 20 years of experience. I've co-founded and sold my own AI startup, started a second one, and led teams in top-tier financial institutions and startups around the world. I'm passionate about bringing others into this exciting field and helping them become experts at the forefront of the industry.Why This Course?• Hands-On Learning: The best way to learn is by doing. You'll engage in practical exercises, building real-world AI applications that deliver stunning results.• Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents.• Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided.• No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.Course StructureWeek 1: Foundations and First Projects• Dive into the fundamentals of Transformers.• Experiment with six leading Frontier Models.• Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.Week 2: Frontier APIs and Customer Service Chatbots• Explore Frontier APIs and interact with three leading models.• Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.Week 3: Embracing Open-Source Models• Discover the world of Open-Source models using HuggingFace.• Tackle 10 common Gen AI use cases, from translation to image generation.• Build a product to generate meeting minutes and action items from recordings.Week 4: LLM Selection and Code Generation• Understand the differences between LLMs and how to select the best one for your business tasks.• Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.Week 5: Retrieval-Augmented Generation (RAG)• Master RAG to improve the accuracy of your solutions.• Become proficient with vector embeddings and explore vectors in popular open-source vector datastores.• Build a full business solution similar to real products on the market today.Week 6: Transitioning to Training• Move from inference to training.• Fine-tune a Frontier model to solve a real business problem.• Build your own specialized model, marking a significant milestone in your AI journey.Week 7: Advanced Training Techniques• Dive into advanced training techniques like QLoRA fine-tuning.• Train an open-source model to outperform Frontier models for specific tasks.• Tackle challenging projects that push your skills to the next level.Week 8: Deployment and Finalization• Deploy your commercial product to production with a polished UI.• Enhance capabilities using Agents.• Deliver your first productionized, agentized, fine-tuned LLM model.• Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career. Overview Section 1: Week 1 - Build Your First LLM Product: Exploring Frontier Models & Transformers Lecture 1 Day 1 - Mastering LLM Engineering: From Basics to Outperforming GPT-4 in 8 Weeks Lecture 2 Day 1 - Getting Started with Generative AI: First Steps in LLM Project Setup Lecture 3 Day 1 - Building a Web Page Summarizer with OpenAI GPT-4: Instant Gratification Lecture 4 Day 1 - Mastering OpenAI API: Write Code for Frontier Models in Generative AI Lecture 5 Day 2 - Generative AI Course Structure: 8 Weeks to LLM Mastery Lecture 6 Day 2 - Exploring Frontier LLMs: ChatGPT, Claude, Gemini and more Lecture 7 Day 3 - Frontier LLMs: Exploring Strengths and Weaknesses of Top Gen AI Models Lecture 8 Day 3 - ChatGPT vs Other LLMs: Strengths, Weaknesses, and Complementary Models Lecture 9 Day 3 - Claude AI: Exploring Capabilities and Limitations of the Frontier Model Lecture 10 Day 3 - Comparing Gemini AI to Other Frontier Models: Strengths and Limitations Lecture 11 Day 3 - Comparing Frontier LLMs: Command-R Plus, Meta AI, & Perplexity AI Models Lecture 12 Day 3 - Comparing Top AI Models: GPT-4, Claude, and Gemini in Leadership Battle Lecture 13 Day 4 - AI Leadership Battle: Analyzing GPT-4, Claude-3, and Gemini-1.5 Pitches Lecture 14 Day 4 - Gen AI Breakthroughs: Transformer Models & Emergent Intelligence Lecture 15 Day 4 - Tokenization in LLMs: How GPT Processes Text for Natural Language Tasks Lecture 16 Day 4 - Understanding Context Windows: Maximizing LLM Performance and Memory Lecture 17 Day 5 - Implementing One-Shot Prompting with OpenAI for Business Applications Lecture 18 Day 5 - How to Use GPT-4 for JSON Generation in Python: AI-Powered Web Scraping Lecture 19 Day 5 - Building a Full Business Solution with Generative AI and OpenAI's API Lecture 20 Day 5 - Extending Gen AI: Multi-Shot Prompting & Translation Techniques Section 2: Week 2 - Build a Multi-Modal Chatbot: LLMs, Gradio UI, and Agents in Action Lecture 21 Day 1 - Mastering Multiple AI APIs: OpenAI, Claude, and Gemini for LLM Engineers Lecture 22 Day 1 - Streaming AI Responses: Implementing Real-Time LLM Output in Python Lecture 23 Day 1 - How to Create Adversarial AI Conversations Using OpenAI and Claude APIs Lecture 24 Day 1 - AI Tools: Exploring Transformers & Frontier LLMs for Developers Lecture 25 Day 2 - Building AI UIs with Gradio: Quick Prototyping for LLM Engineers Lecture 26 Day 2 - Gradio Tutorial: Create Interactive AI Interfaces for OpenAI GPT Models Lecture 27 Day 2 - Implementing Streaming Responses with GPT and Claude in Gradio UI Lecture 28 Day 2 - Building a Multi-Model AI Chat Interface with Gradio: GPT vs Claude Lecture 29 Day 2 - Building Advanced AI UIs: From OpenAI API to Chat Interfaces with Gradio Lecture 30 Day 3 - Building AI Chatbots: Mastering Gradio for Customer Support Assistants Lecture 31 Day 3 - Build a Conversational AI Chatbot with OpenAI & Gradio: Step-by-Step Lecture 32 Day 3 - Enhancing Chatbots with Multi-Shot Prompting and Context Enrichment Lecture 33 Day 3 - Mastering AI Tools: Empowering LLMs to Run Code on Your Machine Lecture 34 Day 4 - Using AI Tools with LLMs: Enhancing Large Language Model Capabilities Lecture 35 Day 4 - Building an AI Airline Assistant: Implementing Tools with OpenAI GPT-4 Lecture 36 Day 4 - How to Equip LLMs with Custom Tools: OpenAI Function Calling Tutorial Lecture 37 Day 4 - Mastering AI Tools: Building Advanced LLM-Powered Assistants with APIs Lecture 38 Day 5 - Multimodal AI Assistants: Integrating Image and Sound Generation Lecture 39 Day 5 - Multimodal AI: Integrating DALL-E 3 Image Generation in JupyterLab Lecture 40 Day 5 - Build a Multimodal AI Agent: Integrating Audio & Image Tools Lecture 41 Day 5 - How to Build a Multimodal AI Assistant: Integrating Tools and Agents Section 3: Week 3 - Open-Source Gen AI: Building Automated Solutions with HuggingFace Lecture 42 Day 1 - Hugging Face Tutorial: Exploring Open-Source AI Models and Datasets Lecture 43 Day 1 - Exploring HuggingFace Hub: Models, Datasets & Spaces for AI Developers Lecture 44 Day 1 - Intro to Google Colab: Cloud Jupyter Notebooks for Machine Learning Lecture 45 Day 1 - Hugging Face Integration with Google Colab: Secrets and API Keys Setup Lecture 46 Day 1 - Mastering Google Colab: Run Open-Source AI Models with Hugging Face Lecture 47 Day 2 - Hugging Face Transformers: Using Pipelines for AI Tasks in Python Lecture 48 Day 2 - Hugging Face Pipelines: Simplifying AI Tasks with Transformers Library Lecture 49 Day 2 - Mastering HuggingFace Pipelines: Efficient AI Inference for ML Tasks Lecture 50 Day 3 - Exploring Tokenizers in Open-Source AI: Llama, Phi-2, Qwen, & Starcoder Lecture 51 Day 3 - Tokenization Techniques in AI: Using AutoTokenizer with LLAMA 3.1 Model Lecture 52 Day 3 - Comparing Tokenizers: Llama, PHI-3, and QWEN2 for Open-Source AI Models Lecture 53 Day 3 - Hugging Face Tokenizers: Preparing for Advanced AI Text Generation Lecture 54 Day 4 - Hugging Face Model Class: Running Inference on Open-Source AI Models Lecture 55 Day 4 - Hugging Face Transformers: Loading & Quantizing LLMs with Bits & Bytes Lecture 56 Day 4 - Hugging Face Transformers: Generating Jokes with Open-Source AI Models Lecture 57 Day 4 - Mastering Hugging Face Transformers: Models, Pipelines, and Tokenizers Lecture 58 Day 5 - Combining Frontier & Open-Source Models for Audio-to-Text Summarization Lecture 59 Day 5 - Using Hugging Face & OpenAI for AI-Powered Meeting Minutes Generation Lecture 60 Day 5 - Build a Synthetic Test Data Generator: Open-Source AI Model for Business Aspiring AI engineers and data scientists eager to break into the field of Generative AI and LLMs.,Professionals looking to upskill and stay competitive in the rapidly evolving AI landscape.,Developers interested in building advanced AI applications with practical, hands-on experience. 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Free Download MLOps Engineering at Scale Author: Carl Osipov Narrator: n/a English | 2024 | ISBN: 9781617297762 | MP3@64 kbps | Duration: 7h 7m | 583 MB MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You'll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities. Rapidgator https://rg.to/file/de8b8ed859cc7b00a27484228d2bc712/542uw.rar.html Fikper Free Download https://fikper.com/ELGAGg1VY9/542uw.rar.html Links are Interchangeable - No Password - Single Extraction

epub | 7.24 MB | English| Isbn:9781632405272 | Author: Seth Royal (Editor) | Year: 2016 Description: https://ddownload.com/y3bps9ezugtk https://rapidgator.net/file/eeceae36ad3e40f16a4355afc2b2d06f/

Free Download Cracking the Data Engineering Interview Published 9/2024 Created by Milo Kite MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 102 Lectures ( 3h 55m ) | Size: 3 GB Use SQL, Python, Data Modelling and Project Interview preparation to land your first job in Data Engineering! What you'll learn: To query Data in a Database To write your own scripts in Python The most powerful SQL transformations Python's control flow and conditional logic To sell your project experience effectively To navigate AI's role in Data Strategies for modelling Data in a Database How problems are posed in industry Navigate live-coding interviews calmly Write scalable, professional code Know what role a Data Engineer performs Gain confidence in yourself and experience Set Theory's relation to Data What hiring managers really look for Requirements: No programming experience required, all you need is an email address and an internet connection. All of the tools used can be accessed from your browser. Description: Ready to Launch Your Career in Data Engineering?Whether you're curious about the world of data, a student, looking to change up your career, in a related field, or just looking to sharpen up your skills this course is designed just for you! Cracking the Data Engineering Interview is your gateway to landing a job in one of the fastest-growing, high-demand fields in tech today.Course OverviewCracking the Data Engineering Interview is a beginner-friendly course that takes you step-by-step through everything you need to succeed in your data engineering job hunt. We start with the basics and gradually build your skills, ensuring you're fully equipped to crack any interview. Throughout the course, you'll find loads of bite-sized coding example questions designed to test your skills and remove the scare factor. These exercises are not just practice-they're a built-in study plan, helping you focus on key concepts without the extra effort of planning your own study sessions. What this course DOESN'T include:Downloading any softwareConfusing terminologyWriting code in your terminalLengthy hard to digest lectures Course ContentIntroduction | We'll kick off with an overview of the data engineering landscape, what the role entails, and why it's such a critical function in the age of big data and AI.Data Modelling | Learn the fundamentals of data modelling, including how to structure and organise data in ways that make it accessible, reliable, and easy to analyse.SQL | SQL is the backbone of data engineering. In this module, you'll master the art of writing efficient queries, understanding joins, and optimising databases. You'll also get hands-on practice with exercises that mimic real-world scenarios you'll face in a data engineering role.Projects | This section also teaches you how to effectively present and sell your project experience to hiring managers. You'll learn how to highlight your contributions, explain the impact of your work, and showcase your skills in a way that makes you stand out in interviews.Python | Python is a key tool in the data engineer's toolkit. We'll start from the basics and move into more advanced concepts like data manipulation, scripting, and automation.Start Learning Today!This course is structured to make learning easy and accessible, with no need for complex installations or deep technical know-how to get started. We believe that the best way to learn is by doing, and we're here to support you every step of the way.Get ready to ace your data engineering interview and step into a rewarding career with confidence. Enrol now and take the first step towards your new future!If you have any questions about the content of the course or need any support during the course feel free to reach out to me by email. Who this course is for: This course is designed for programming beginners looking to get their first job in Data Engineering! The most common types are people looking for a career change, graduate students, professionals in related fields, or even Data Engineers looking to round out their experience. 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epub | 26.88 MB | English| Isbn:9781000648300 | Author: David E. Clough, Steven C. Chapra | Year: 2023 Description: https://rapidgator.net/file/5dc80105f8222491676e2dba0c98bd19/ https://ddownload.com/lj8jhr3hmcsx https://nitroflare.com/view/17D2AFBA681EBA0/

Free Download Product Management for Engineering Managers Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz Language: English | Size: 215.92 MB | Duration: 33m 38s Engineering managers have a tough job, guiding engineers to balance business and tech priorities. This course will teach you how to collaborate effectively with product managers to foster a healthy engineering team that builds great products. As an engineering manager, it's tough to balance business and technical priorities. This often shows up when giving your direct reports guidance on how to right-size the fidelity of solutions and code they're writing. In this course, Product Management for Engineering Managers, you'll learn how to speak the language of product to collaborate well with your cross-functional peers. First, you'll explore an overview of product management. Next, you'll discover what a product vision is and why it matters. Finally, you'll learn how to do great stakeholder management so you can increase your influence with your business leaders. When you're finished with this course, you'll have the skills and knowledge of product management needed to collaborate effectively with your product and business stakeholders to foster a healthy engineering team that builds great products. Homepage https://www.pluralsight.com/courses/product-management-engineering-managers TakeFile https://takefile.link/i8sdduo0sh71/qmtnn.Product.Management.for.Engineering.Managers.rar.html Rapidgator https://rg.to/file/a3af5bad2ba106613d5905b8469de586/qmtnn.Product.Management.for.Engineering.Managers.rar.html Fikper Free Download https://fikper.com/TBltWfX1Ov/qmtnn.Product.Management.for.Engineering.Managers.rar.html No Password - Links are Interchangeable

Free Download Overview - Cybersecurity Engineering (ISO/SAE 21434:2021) Published 9/2024 Created by Alexander Walter MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 43 Lectures ( 2h 33m ) | Size: 871 MB Plain and simple clause by clause explanation of ISO/SAE 21434:2021 - Cybersecurity Engineering What you'll learn: Organizational cybersecurity Project dependent cybersecurity management Distributed cybersecurity activities Continual cybersecurity activities Concept Product development Cybersecurity validation Production Operations and maintenance End of cybersecurity support and decommissioning Threat analysis and risk assessment methods Requirements: None Description: The course is divided into two main chapters:- Cybersecurity- ISO 21434Chapter 1: CybersecurityThe chapter provides information on history and basic terms and definitions related to cybersecurity. Also legal aspects are discussed in the chapter.Chapter 2: ISO 21434The chapter includes all chapters of the cybersecurity standard ISO 21434 and explains the most important aspects of the chapters step by step.The following chapters are covered by the course:- Clause 5: Organizational cybersecurity managementThe chapter provides an overview of the most important aspects to be considered at the organizational level. For example the consideration of a cybersecurity policy.- Clause 6: Project dependent cybersecurity managementThe project dependent management includes the definition of responsibilities, a cybersecurity plan and cybersecurity case and more.- Clause 7: Distributed cybersecurity activitiesThis chapter focuses on the supplier interfaces agreement. - Clause 8: Continual cybersecurity activitiesIn this chapter requirements for monitoring of cybersecurity and management of vulnerabilities is described.- Clause 9: ConceptThe concept phase is part of the ISO 21434 V-model and includes the item definition and performance of a threat analysis and risk assessment.- Clause 10: Product developmentThe chapters focuses on the cybersecurity specifications and definition of verification activities.- Clause 11: Cybersecurity validationThe chapter focuses on cybersecurity validation activities.- Clause 12: Production- Clause 13: Operations and maintenance- Clause 14: End of cybersecurity support and decommissioningAt the end you get an overview of the ISO 26262 structure, since the safety standard ISO 26262 is one of the most important references in the ISO 21434. Who this course is for: Cybersecurity responsibles Homepage https://www.udemy.com/course/overview-cybersecurity-engineering-isosae-214342021/ Rapidgator https://rg.to/file/3cc9bb2fa68ce55262aaf8235bc432c4/owwka.Overview..Cybersecurity.Engineering.ISOSAE.214342021.rar.html Fikper Free Download https://fikper.com/zHLYbtLDkF/owwka.Overview..Cybersecurity.Engineering.ISOSAE.214342021.rar.html No Password - Links are Interchangeable

Free Download EssentialSQL - Microsoft Fabric Data Engineering Mastery Published 9/2024 Duration: 7h55m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 3.83 GB Genre: eLearning | Language: English Data Engineering | Microsoft Fabric | Azure Data Factory | Spark | Medallion | Lakehouse | Data Warehouse | SQL | Python What you'll learn Optimize Microsoft Fabric: Learn best practices for efficient data engineering organization. Understand Azure Data Factory: Grasp workflow orchestration for foundational knowledge in data engineering. Apply Data Integration: Utilize principles for end-to-end solutions in comprehensive data engineering. Evaluate Data Transformations: Compare techniques within Microsoft Fabric for informed data processing. Implement Solutions with Azure Data Factory: Leverage it with SQL Data Warehouse for comprehensive data engineering. Develop Solutions with Spark: Create end-to-end solutions for large-scale data processing and analytics. Construct Fabric Data Flows: Tailor flows in Microsoft Fabric for transforming and managing data in Lakehouse. Build Fabric Spark Jobs: Create advanced Spark jobs within Microsoft Fabric for complex data transformations. Establish Data Warehouse: Ingest from Fabric Lakehouse for a scalable and reliable data warehouse. Design ADF Pipelines: Develop Azure Data Factory pipelines for efficient end-to-end data transformations. Create Data Solutions with Dataflows: Construct end-to-end solutions, integrating components for seamless data processing. Apply DevOps for Version Control: Utilize Microsoft DevOps for systematic source code management. Implement Medallion Architecture: Apply advanced principles in Microsoft Fabric for efficient data handling. Requirements Familiarity with SQL and basic programming skills can be beneficial. We'll focus on SQL and Python. I provide to you all the sample you need to complete the case study. Proficiency in programming will be valuable when working with scripting and data transformation tasks Experience in troubleshooting and debugging technical issues will aid in building and troubleshooting Azure Data Factory pipelines. Description Unlock the full potential of data engineering with our comprehensive course on Microsoft Fabric. This hands-on program is designed for individuals seeking to master the latest in data transformation and integration technologies using Microsoft Fabric. Whether you're a data engineer, analyst, or IT professional, this course will equip you with the skills to build robust, scalable data solutions. What You Will Learn Introduction to Microsoft Fabric : Get started with Microsoft Fabric, exploring its core features and capabilities for data engineering. Learn how to set up your workspace and prepare for data ingestion. Medallion Architecture : Understand and implement the Medallion Architecture in Microsoft Fabric. Gain insights into organizing data into Raw, Bronze, Silver, and Gold layers for efficient processing and analysis. Dataflows Gen 2 : Discover the power of Dataflows Gen 2 for transforming and managing data. Learn how to ingest data through various stages, from Raw to Gold, and address common challenges. SQL and Python Integration : Dive into SQL and Python for advanced data transformation and processing. Create pipelines, automate workflows, and leverage SQL queries and PySpark to enhance data management. Visualization and Reporting : Use PowerBI to visualize data and generate insightful reports. Understand how to prepare data for visualization and create meaningful dashboards. End-to-End Solutions : Construct comprehensive end-to-end data engineering solutions. Learn how to integrate dataflows, pipelines, and transformation techniques to build a scalable data architecture. Practical Application : Engage in hands-on exercises with real-world examples, including a Car Sales case study. Work alongside the instructor to implement data engineering solutions and solve practical problems. Course Highlights Hands-On Learning : Experience a practical, "work with me" approach where you'll actively engage with the tools and concepts alongside the instructor. Flexible Learning Path : Start with foundational concepts and advance to complex topics at your own pace. Utilize SQL and Python methods to master different aspects of data transformation. Interactive Exercises : Build and optimize dataflows, create pipelines, and develop comprehensive data models. Address real-world scenarios and troubleshoot common issues. Expert Guidance : Benefit from direct access to course instructors for questions and support. Engage with a community of learners and share insights and solutions. Career Advancement : Enhance your data engineering skills and prepare for roles that require expertise in Microsoft Fabric, SQL, and Python. Gain practical experience that will set you apart in the data engineering field. Who Should Enroll Data Engineers : Professionals looking to enhance their skills in data architecture and processing using Microsoft Fabric. Data Analysts : Individuals seeking to expand their capabilities in data integration and transformation. Database Administrators : Those interested in optimizing data management with Microsoft Fabric. Business Intelligence Professionals : Experts aiming to develop end-to-end solutions for business analytics. Data Scientists : Professionals wanting to strengthen their data engineering skills, especially with large-scale data processing. IT Professionals and Technology Enthusiasts : Individuals interested in the latest Microsoft technologies for data engineering and analytics. Join us to gain hands-on experience with Microsoft Fabric and elevate your data engineering skills to the next level! Note: This course is a good introduction to Fabric and covers many of the topics required for the Azure Data Engineer Associate certification. Check out the preview lesson "Introduction to Microsoft Fabric Data Engineering and Certification Alignment" to see what's covered and what is not. Who this course is for Data Engineers: Professionals responsible for designing, constructing, and maintaining data architecture, databases, and processing systems. Data Analysts: Individuals seeking to expand their data processing and integration skills for comprehensive data analysis. Database Administrators: Those looking to enhance their knowledge of Microsoft Fabric and related tools for optimizing data management. Business Intelligence Professionals: Individuals interested in developing end-to-end solutions for business intelligence and analytics. Data Scientists: Professionals aiming to strengthen their data engineering capabilities, especially in large-scale data processing using technologies like Spark. IT Professionals: Those involved in IT roles looking to specialize in data engineering within the Microsoft ecosystem. Students and Graduates: Individuals pursuing education or recently graduated in fields related to data science, computer science, or information technology. Technology Enthusiasts: Individuals with a keen interest in learning about the latest tools and best practices in the field of data engineering. Professionals Transitioning Careers: Individuals seeking a career change into the field of data engineering or related roles. Anyone Interested in Microsoft Data Technologies: Enthusiasts looking to gain expertise in Microsoft technologies for data engineering and analytics. 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Applying Reverse Engineering Techniques in ZBrush and Rhino English | 1h 48m | AVC (.mp4) 1280x720 30fps | AAC 48KHz 2ch | 1.43 Gb Genre: eLearning In this series of ZBrush and Rhino tutorials, we'll learn to reverse engineer a 3D scanned mesh and convert it to a sculpt-able mesh. You'll learn to import the data into Rhino, convert it to a NURB model and prepare it for rapid prototyping. By the end of this ZBrush and Rhino tutorial, you'll be able to use these industry techniques in Rhino and ZBrush as part of your modeling pipeline. DOWNLOAD http://rapidgator.net/file/56317ca12d92660326f03aafde0c468f/Applying.part1.rar.html http://rapidgator.net/file/4c40231def9926e233595b2cdaa37295/Applying.part2.rar.html http://rapidgator.net/file/ea18cc71cf1a6231394381bb85cc40a1/Applying.part3.rar.html http://rapidgator.net/file/89b188a0cdd297173d48daa9f3d31105/Applying.part4.rar.html http://rapidgator.net/file/ae7d6e297e7900e0a2d793817871a092/Applying.part5.rar.html http://rapidgator.net/file/c46431bbcbbb8be3b92d8865686130d3/Applying.part6.rar.html http://uploaded.net/file/8yrxd0ul/Applying.part1.rar http://uploaded.net/file/kiopkh63/Applying.part2.rar http://uploaded.net/file/hogmcjpo/Applying.part3.rar http://uploaded.net/file/swlczfe3/Applying.part4.rar http://uploaded.net/file/xvq5lp2r/Applying.part5.rar http://uploaded.net/file/pzr2xho9/Applying.part6.rar http://www.uploadable.ch/file/jAPRqmCZ6d5b/Applying.part1.rar http://www.uploadable.ch/file/Awq8GXMgNDes/Applying.part2.rar http://www.uploadable.ch/file/G5Vk8AftuDW5/Applying.part3.rar http://www.uploadable.ch/file/6BVNJbV7JCmp/Applying.part4.rar http://www.uploadable.ch/file/PkZpXp6tyNUY/Applying.part5.rar http://www.uploadable.ch/file/4mashTaY3w4p/Applying.part6.rar http://www.hitfile.net/27PT/Applying.part1.rar.html http://www.hitfile.net/27Pe/Applying.part2.rar.html http://www.hitfile.net/27Mi/Applying.part3.rar.html http://www.hitfile.net/27Sh/Applying.part4.rar.html http://www.hitfile.net/27Mq/Applying.part5.rar.html http://www.hitfile.net/27OF/Applying.part6.rar.html

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Pluralsight - Software Engineering Essentials English | 4h 57m | AVC (.mp4) 1024x768 15fps | AAC 48KHz 2ch | 502 Mb Genre: eLearning Software engineers are in high demand, yet software engineering is often misunderstood. What is software engineering and why is it important? What are the building blocks of software engineering? And what are the processes and methods that differentiate it as a dis[beeep]line? This course aims to answer all of the above questions, and give the audience the absolute essential knowledge about software engineering. The IEEE defines Software Engineering as "the application of a systematic, dis[beeep]lined, and quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software." Software engineering is a dis[beeep]line applied throughout the entire software lifecycle, spanning the early phases of system specifications all the way to maintenance after system deployment. So why use an engineering approach to software? Quite simply, because the alternative would be to use an ad-hoc or disordered approach. An engineering approach means predictability and quantifiable results through the application of theories, methodologies, frameworks, and tools. When applied efficiently, the result is high-quality software created in a cost-effective manner. DOWNLOAD http://rapidgator.net/file/02fe86eb61a857c068787de246f68265/Software.part1.rar.html http://rapidgator.net/file/cc6195fc7edfe25013db0686876ce364/Software.part2.rar.html http://rapidgator.net/file/b42ff916c856dab3204962ea79598ad9/Software.part3.rar.html http://rapidgator.net/file/06e4f9842f80043db5797b3b550a8653/Software.part4.rar.html http://rapidgator.net/file/fc76654b106354486c86d81f90ce5616/Software.part5.rar.html http://uploaded.net/file/8odbcpd6/Software.part1.rar http://uploaded.net/file/kxe6d7iu/Software.part2.rar http://uploaded.net/file/fsltxqvn/Software.part3.rar http://uploaded.net/file/bvqgf9i9/Software.part4.rar http://uploaded.net/file/y6vlxkls/Software.part5.rar http://www.uploadable.ch/file/UQANHvMumZg4/Software.part1.rar http://www.uploadable.ch/file/JWpJPU5RqKWY/Software.part2.rar http://www.uploadable.ch/file/2x79AJVJ9jnz/Software.part3.rar http://www.uploadable.ch/file/8JzfDPqJS7kn/Software.part4.rar http://www.uploadable.ch/file/wHvh5EvRxXSn/Software.part5.rar http://turbobit.net/0j92vc4cq98x/Software.part1.rar.html http://turbobit.net/dq6r3eourv60/Software.part2.rar.html http://turbobit.net/lsicn7w0or08/Software.part3.rar.html http://turbobit.net/m8dgi6tshyuv/Software.part4.rar.html http://turbobit.net/z5tybq7506b5/Software.part5.rar.html