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Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for Solution Architects Overview At the end of this course, you will be able to: Apply the AWS Well-Architected Framework Manage multiple AWS accounts for your organization Connect an on-premises datacenter to AWS cloud Move large data from an on-premises datacenter to AWS Design large datastores for AWS cloud Understand different architectural designs for scalability Protect your infrastructure from DDoS attack Secure your data on AWS with encryption Enhance the performance of your solutions Select the most appropriate AWS deployment mechanism Building on concepts introduced in Architecting on AWS, Advanced Architecting on AWS is intended for individuals who are experienced with designing scalable and elastic applications on the AWS platform. Building on concepts introduced in Architecting on AWS, this course covers how to build complex solutions which incorporate data services, governance, and security on AWS. This course introduces specialized AWS services, including AWS Direct Connect and AWS Storage Gateway to support Hybrid architecture. It also covers designing best practices for building scalable, elastic, secure, and highly available applications on AWS. Module 1: AWS Account Management Multiple accounts Multi-account patterns License management Manage security and costs with multiple accounts AWS Organizations AWS Directory Service Hands-on lab: Multi-VPC connectivity using a VPN Module 2: Advanced Network Architectures Improve VPC network connections Enhance performance for HPC workloads VPN connections over AWS AWS Direct Connect AWS Transit Gateway Amazon Route 53 Exercise: Design a hybrid architecture Module 3: Deployment Management on AWS Application lifecycle management Application deployment using containers AWS Elastic Beanstalk AWS OpsWorks AWS CloudFormation Module 4: Data Optimize Amazon S3 storage Amazon ElastiCache AWS Snowball AWS Storage Gateway AWS DataSync Backup and archival considerations Database migration Designing for big data with Amazon DynamoDB Hands-on lab: Build a failover solution with Amazon Route 53 and Amazon RDS Module 5: Designing for large scale applications AWS Auto Scaling Migrating over-provisioned resources Blue-green deployments on AWS Hands-on lab: Blue-green deployment with AWS Module 6: Building resilient architectures DDoS attack overview AWS Shield AWS WAF Amazon GuardDuty High availability using Microsoft SQL Server and Microsoft SharePoint on AWS High availability using MongoDB on Amazon EC2 AWS Global Accelerator Hands-on lab: CloudFront content delivery and automating AWS WAF rules Module 7: Encryption and data security Encryption primer DIY key management in AWS AWS Marketplace for encryption products AWS Key Management Service (AWS KMS) Cloud Hardware Security Module (HSM) Comparison of key management options Hands-on lab: AWS KMS with envelope encryption

Overview This comprehensive course on Master JavaScript with Data Visualization will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Master JavaScript with Data Visualization comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Master JavaScript with Data Visualization. It is available to all students, of all academic backgrounds. Requirements Our Master JavaScript with Data Visualization is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 11 sections • 76 lectures • 06:21:00 total length •Introduction to Getting Started: 00:02:00 •Course Curriculum: 00:05:00 •How to Get Pre-Requisites: 00:02:00 •Getting Started on Windows, Linux or Mac: 00:01:00 •How to ask a Great Questions: 00:02:00 •FAQ's: 00:01:00 •What is JavaScript: 00:09:00 •Choosing Code Editor: 00:03:00 •Installing Code Editor (Sublime Text): 00:04:00 •Installing Code Editor(Visual Studio Code): 00:07:00 •Hello World Program: 00:14:00 •Getting Output: 00:11:00 •Summary: 00:02:00 •Introduction: 00:02:00 •Internal JavaScript: 00:13:00 •External JavaScript: 00:09:00 •Inline JavaScript: 00:04:00 •Async and defer: 00:06:00 •Variables: 00:13:00 •Data Types: 00:10:00 •Numbers: 00:06:00 •Boolean: 00:04:00 •Arrays(): 00:12:00 •Objects: 00:06:00 •Comments: 00:05:00 •Summary: 00:01:00 •Introduction: 00:02:00 •Strings: 00:06:00 •String Formatting: 00:05:00 •String Methods: 00:12:00 •Summary: 00:02:00 •Introduction: 00:02:00 •Arithmetic operators: 00:07:00 •Assignment operators: 00:03:00 •Comparison operators: 00:06:00 •Logical operators: 00:08:00 •Summary: 00:02:00 •Introduction: 00:02:00 •If statement: 00:04:00 •If-else statement: 00:05:00 •If-else-if statement: 00:04:00 •Switch-case statement: 00:09:00 •Summary: 00:01:00 •Introduction: 00:02:00 •While loop: 00:09:00 •Do-while loop: 00:03:00 •For loop: 00:08:00 •Break: 00:02:00 •Continue: 00:03:00 •Coding Exercise: 00:02:00 •Solution for Coding Exercise: 00:02:00 •Summary: 00:02:00 •Introduction: 00:02:00 •Creating a Function: 00:07:00 •Function Call(): 00:07:00 •Function with parameters: 00:05:00 •Function Bind(): 00:06:00 •Summary: 00:01:00 •Introduction: 00:01:00 •How to Use Google chart script: 00:04:00 •Line Graph chart: 00:14:00 •Scatter plots chart: 00:02:00 •Bar chart: 00:04:00 •Pie chart: 00:02:00 •3D Pie chart: 00:02:00 •Summary: 00:01:00 •Introduction: 00:01:00 •Try-catch: 00:05:00 •Try-catch-finally: 00:17:00 •Summary: 00:01:00 •Introduction: 00:01:00 •On Submit Validation: 00:09:00 •Input Numeric Validation: 00:12:00 •Login Form Validation: 00:05:00 •Password Strength Check Validation: 00:04:00 •Summary: 00:01:00

GitHub is a Git repository hosting service, but it adds many of its own features. While Git is a command line tool, GitHub provides a Web-based graphical interface. It also provides access control and several collaboration features, such as a wikis and basic task management tools for every project.

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Property Management: Property Management course Online The Property Management: Property Management course will allow you to learn about the realities of a career in property management or development and discover why it is a good career choice. This Property Management: Property Management course gives you a thorough understanding of a Property Manager's roles and responsibilities and how to develop a strong relationship with the landlords of the properties you manage. This Property Management: Property Management course takes you through the property management process step by step, ensuring that you understand everything expected of you as a Property Manager. Why choose this Property Management: Property Management course from the School of Health Care? Self-paced course, access available from anywhere in the world. High-quality study materials that are easy to understand. Property Management: Property Management course developed by industry experts. After each module, there will be an MCQ quiz to assess your learning. Assessment results are generated automatically and instantly. 24/7 support is available via live chat, phone call, or email. Free PDF certificate after completing the Property Management: Property Management course. Main Course: Property Management course Free Courses Course 01: Level 7 Project Management Course 02: Level 7 Construction Management Course 03: Level 7 Diploma in Facilities Management [ Note: Free PDF certificate as soon as completing the Property Management: Property Management course] Property Management: Property Management course Online Course Curriculum of Property Management: Property Management course Module 01: A Brief Overview Property Management Module 02: Functions of a Property Manager Module 03: Listing Marketing and Properties Module 04: The Leasing and Tenancy Agreement Module 05: UK Property Market Changes: An Opportunity Certificate of Completion You will receive a course completion certificate for free as soon as you complete the Property Management: Property Management course. Who is this course for? Property Management: Property Management course Online Property Management: Property Management course is a great course to take if you want to become an expert in the field. This Property Management: Property Management course is open to those interested in property management, including property owners and managers. In addition, this Property Management: Property Management course is open to students and job seekers who want to improve their resumes and get more experience. Requirements Property Management: Property Management course Online To enrol in this Property Management: Property Management course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Property Management: Property Management course. Be energetic and self-motivated to complete our Property Management: Property Management course. Basic computer Skill is required to complete our Property Management: Property Management course. If you want to enrol in our Property Management: Property Management course, you must be at least 15 years old. Career path Property Management: Property Management course Online This Property Management: Property Management course program can help you get started in the following positions: Property Developer/Agent Real Estate Property Manager

Master C++ Programming with our comprehensive masterclass! From basics to advanced concepts like pointers, structures, and file handling, this course equips you to write efficient C++ code. Perfect for beginners and those looking to refresh their skills.

Advance your Java skills with our Level 2 course. From mastering collections and file handling to understanding multi-threading and lambda expressions, this course culminates in building a User Authentication system, solidifying your Java expertise for real-world applications.

The comprehensive Docker Training for .Net and Angular Developers has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Docker Training for .Net and Angular Developers today, and learn from the very best the industry has to offer! This best selling Docker Training for .Net and Angular Developers has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Docker Training for .Net and Angular Developers is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Docker Training for .Net and Angular Developers is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Docker Training for .Net and Angular Developers is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Docker Training for .Net and Angular Developers , you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Docker Training for .Net and Angular Developers will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Docker Training for .Net and Angular Developers to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

Duration 1 Days 6 CPD hours This course is intended for The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions. Note Generative AI is a fast-evolving field of artificial intelligence, and the Azure OpenAI service is subject to frequent changes. The course materials are maintained to reflect the latest version of the service at the time of writing. Azure OpenAI Service provides access to OpenAI's powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio. In this course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications. Prerequisites Familiarity with Azure and the Azure portal. Experience programming with C# or Python. 1 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 2 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 3 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 4 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 5 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 6 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data Additional course details: Nexus Humans AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.