229 Microsoft Azure courses delivered Online

Discover the thrilling world of artificial intelligence with the 'Machine Learning Course with Python'. Immerse yourself in a voyage from foundational concepts, unveiling the mysteries behind algorithms, to diving deep into the cores of preprocessing, regression, and classification. Crafted meticulously, this course introduces Python as the catalyst, opening doors to data-driven decision-making and predictive analysis, empowering your journey in the ever-evolving field of machine learning. Learning Outcomes Grasp the foundational knowledge of various machine learning algorithms. Attain proficiency in preprocessing data for optimal outcomes. Master the nuances of regression analysis using Python. Delve into the intricacies of classification techniques. Enhance problem-solving abilities with practical Python-driven machine learning applications. Why choose this Machine Learning Course with Python course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Machine Learning Course with Python Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Machine Learning Course with Python course for? Aspiring data scientists eager to harness the power of machine learning. Python enthusiasts aiming to delve into its applications in AI. Professionals in the tech industry seeking a transition into data roles. Academics and researchers wanting to employ machine learning in their work. Business analysts aiming to leverage predictive analytics for better insights. Career path Data Scientist: £40,000 - £70,000 Machine Learning Engineer: £50,000 - £80,000 AI Researcher: £45,000 - £75,000 Data Analyst: £30,000 - £50,000 Python Developer: £35,000 - £65,000 Business Intelligence Developer: £40,000 - £60,000 Prerequisites This Machine Learning Course with Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Course with Python was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Introduction to Machine Learning Algorithms Introduction to types of ML algorithm 00:02:00 Module 02: Preprocessing Importing a dataset in python 00:02:00 Resolving Missing Values 00:06:00 Managing Category Variables 00:04:00 Training and Testing Datasets 00:07:00 Normalizing Variables 00:02:00 Normalizing Variables - Python Code 00:03:00 Summary 00:01:00 Module 03: Regression Simple Linear Regression - How it works? 00:04:00 Simple Linear Regreesion - Python Implementation 00:07:00 Multiple Linear Regression - How it works? 00:01:00 Multiple Linear Regression - Python Implementation 00:09:00 Decision Trees - How it works? 00:05:00 Random Forest - How it works? 00:03:00 Decision Trees and Random Forest - Python Implementation 00:04:00 Module 04: Classification kNN - How it works? 00:02:00 kNN - Python Implementation 00:10:00 Decision Tree Classifier and Random Forest Classifier in Python 00:10:00 SVM - How it works? 00:04:00 SVM - Python Implementation 00:06:00 Assignment Assignment - Machine Learning Course with Python 00:00:00

Duration 4 Days 24 CPD hours This course is intended for This four-day course is intended for Windows Server Hybrid Administrators who have experience working with Windows Server and want to extend the capabilities of their on-premises environments by combining on-premises and hybrid technologies. Windows Server Hybrid Administrators who already implement and manage on-premises core technologies want to secure and protect their environments, migrate virtual and physical workloads to Azure Iaas, enable a highly available, fully redundant environment, and perform monitoring and troubleshooting. This course teaches IT Professionals to configure advanced Windows Server services using on-premises, hybrid, and cloud technologies. The course teaches IT Professionals how to leverage the hybrid capabilities of Azure, how to migrate virtual and physical server workloads to Azure IaaS, and how to secure Azure VMs running Windows Server. The course also teaches IT Professionals how to perform tasks related to high availability, troubleshooting, and disaster recovery. The course highlights administrative tools and technologies including Windows Admin Center, PowerShell, Azure Arc, Azure Automation Update Management, Microsoft Defender for Identity, Azure Security Center, Azure Migrate, and Azure Monitor. Prerequisites An understanding of the following concepts as related to Windows Server technologies: High availability and disaster recovery Automation Monitoring Troubleshooting 1 - Secure Windows Server user accounts Configure user account rights Protect user accounts with the Protected Users group Describe Windows Defender Credential Guard Block NTLM authentication Locate problematic accounts 2 - Hardening Windows Server Describe Local Password Administrator Solution Configure Privileged Access Workstations Secure domain controllers Analyze security configuration with Security Compliance Toolkit Secure SMB traffic 3 - Windows Server update management Explore Windows Update Outline Windows Server Update Services server deployment options Define Windows Server Update Services update management process Describe the process of Update Management 4 - Secure Windows Server DNS Implement split-horizon DNS Create DNS policies Implement DNS policies Secure Windows Server DNS Implement DNSSEC 5 - Implement Windows Server IaaS VM network security Implement network security groups and Windows IaaS VMs Implement adaptive network hardening Implement Azure Firewall and Windows IaaS VMs Implement Windows firewall with Windows Server IaaS VMs Choose the appropriate filtering solution Deploy and configure Azure firewall using the Azure portal Capture network traffic with network watcher Log network traffic to and from a VM using the Azure portal 6 - Audit the security of Windows Server IaaS Virtual Machines Describe Azure Security Center Enable Azure Security Center in hybrid environments Implement and assess security policies Protect your resources with Azure Security Center Implement Azure Sentinel 7 - Manage Azure updates Describe update management Enable update management Deploy updates View update assessments Manage updates for your Azure Virtual Machines 8 - Create and implement application allowlists with adaptive application control Describe adaptive application control Implement adaptive application control policies 9 - Configure BitLocker disk encryption for Windows IaaS Virtual Machines Describe Azure Disk Encryption and server-side encryption Configure Key Vault for Azure Disk Encryption Encrypt Azure IaaS Virtual Machine hard disks Back up and recover data from encrypted disks Create and encrypt a Windows Virtual Machine 10 - Implement change tracking and file integrity monitoring for Windows IaaS VMs Implement Change Tracking and Inventory Manage Change Tracking and Inventory Manage tracked files Implement File Integrity Monitoring Select and monitor entities Use File Integrity Monitoring 11 - Introduction to Cluster Shared Volumes Determine the functionality of Cluster Shared Volumes Explore the architecture and components of Cluster Shared Volumes Implement Cluster Shared Volumes 12 - Implement Windows Server failover clustering Define Windows Server failover clustering Plan Windows Server failover clustering Implement Windows Server failover clustering Manage Windows Server failover clustering Implement stretch clusters Define cluster sets 13 - Implement high availability of Windows Server VMs Select high-availability options for Hyper-V Consider network load balancing for Hyper-V VMs Implement Hyper-V VM live migration Implement Hyper-V VMs storage migration 14 - Implement Windows Server File Server high availability Explore the Windows Server File Server high-availability options Define Cluster Shared Volumes Implement Scale-Out File Server Implement Storage Replica 15 - Implement scale and high availability with Windows Server VM Describe virtual machine scale sets Implement scaling Implement load-balancing VMs Create a virtual machine scale set in the Azure portal Describe Azure Site Recovery Implement Azure Site Recovery 16 - Implement Hyper-V Replica Define Hyper-V Replica Plan for Hyper-V Replica Configure and implement Hyper-V Replica Define extended replication Define Azure Site Recovery Implement Site Recovery from on-premises site to Azure Implement Site Recovery from on-premises site to on-premises site 17 - Protect your on-premises infrastructure from disasters with Azure Site Recovery Azure Site Recovery overview Workloads supported for protection with Azure Site Recovery Run a disaster recovery drill Failover and failback 18 - Implement hybrid backup and recovery with Windows Server IaaS Describe Azure Backup Implement recovery vaults Implement Azure Backup policies Recover Windows IaaS Virtual Machines Perform file and folder recovery Perform backup and restore of on-premises workloads Manage Azure Virtual Machine backups with Azure Backup service 19 - Protect your Azure infrastructure with Azure Site Recovery What is Azure Site Recovery Prepare for disaster recovery with Azure Site Recovery Run a disaster recovery drill Failover and failback using Azure Site Recovery 20 - Protect your virtual machines by using Azure Backup Azure Backup features and scenarios Back up an Azure virtual machine by using Azure Backup Restore virtual machine data 21 - Active Directory Domain Services migration Examine upgrade vs. migration Upgrade a previous version of Active Directory Domain Services to Windows Server 2022 Migrate to Active Directory Domain Services in Windows Server 2022 from a previous version Explore the Active Directory Migration Tool 22 - Migrate file server workloads using Storage Migration Service Storage Migration Service overview and usage scenarios Storage migration requirements Migrate a server with Storage migration Evaluate storage migration considerations 23 - Migrate Windows Server roles Describe the Windows Server Migration Tools Install the Migration Tools Migrate roles using the Migration Tools 24 - Migrate on-premises Windows Server instances to Azure IaaS virtual machines Plan your migration Describe Azure Migrate Perform server assessment Assess physical servers with Azure Migrate Migrate Windows Server workloads by using Azure Migrate 25 - Upgrade and migrate Windows Server IaaS virtual machines Describe Azure Migrate Migrate Windows Server workloads by using Azure Migrate Describe storage migration Migrate file servers by using Storage Migration Service 26 - Containerize and migrate ASP.NET applications to Azure App Service Azure Migrate App Containerization overview 27 - Monitor Windows Server performance Use Performance Monitor to identify performance problems Use Resource Monitor to review current resource usage Review reliability with Reliability Monitor Implement a performance monitoring methodology Use Data Collector Sets to analyze server performance Monitor network infrastructure services Monitor virtual machines running Windows Server Monitor performance with Windows Admin Center Use System Insights to help predict future capacity issues Optimize the performance of Windows Server 28 - Manage and monitor Windows Server event logs Describe Windows Server event logs Use Windows Admin Center to review logs Use Server Manager to review logs Use custom views Implement event log subscriptions 29 - Implement Windows Server auditing and diagnostics Describe basic auditing categories Describe advanced categories Log user access Enable setup and boot event collection 30 - Troubleshoot Active Directory Recover objects from the AD recycle bin Recover the AD DS database Recover SYSVOL Troubleshoot AD DS replication Troubleshoot hybrid authentication issues 31 - Monitor Windows Server IaaS Virtual Machines and hybrid instances Enable Azure Monitor for Virtual Machines Monitor an Azure Virtual Machine with Azure Monitor Enable Azure Monitor in hybrid scenarios Collect data from a Windows computer in a hybrid environment Integrate Azure Monitor with Microsoft Operations Manager 32 - Monitor your Azure virtual machines with Azure Monitor Monitoring for Azure VMs Monitor VM host data Use Metrics Explorer to view detailed host metrics Collect client performance counters by using VM insights Collect VM client event logs 33 - Troubleshoot on-premises and hybrid networking Diagnose DHCP proble

This comprehensive course, 'SQL for Data Science, Data Analytics, and Data Visualization,' covers essential SQL concepts and tools for working with data. Participants will learn to manipulate, analyze, and visualize data using SQL Server, Azure Data Studio, and other relevant tools. The course also delves into advanced SQL commands, stored procedures, and data import/export, making it ideal for aspiring data professionals. Learning Outcomes: Set up and configure SQL Server and SQL Azure Data Studio for data analysis. Master SQL statements for data manipulation, data structure, and user management. Utilize SQL queries, joins, and aggregate functions for efficient data analysis. Understand SQL constraints, views, and advanced commands for in-depth data exploration. Create and implement SQL stored procedures to automate tasks. Leverage Azure Data Studio for data visualization and perform data analysis with SQL. Why buy this SQL for Data Science, Data Analytics and Data Visualization? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the SQL for Data Science, Data Analytics and Data Visualization there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This SQL for Data Science, Data Analytics and Data Visualization course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This SQL for Data Science, Data Analytics and Data Visualization does not require you to have any prior qualifications or experience. You can just enrol and start learning.This SQL for Data Science, Data Analytics and Data Visualization was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This SQL for Data Science, Data Analytics and Data Visualization is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Getting Started Introduction 00:03:00 How to get course requirements 00:03:00 Getting started on Windows, Linux or Docker 00:01:00 How to ask great questions 00:01:00 FAQ's 00:01:00 Section 02: SQL Server setting up Section Introduction 00:02:00 Microsoft SQL Server Installation 00:19:00 SQL Server Management Studio (SSMS) Installation 00:08:00 How to connect MS SQL (Windows Authentication) 00:04:00 How to connect MS SQL (SQL Server Authentication) 00:03:00 Download and Restore Sample Database 00:07:00 Section 03: SQL Azure Data Studio What is Azure Data Studio 00:06:00 Azure Data Studio Installation steps 00:07:00 Azure Data Studio to Connect SQL Server 00:09:00 Create a Database 00:07:00 Create a Table 00:09:00 Insert Data rows 00:07:00 View the Data returned by Query 00:03:00 Section 04: SQL Database basic SSMS Section Introduction 00:01:00 Overview of Databases8 00:11:00 Creating Database 00:05:00 SQL Data Types 00:03:00 Column Data Types on SSMS 00:04:00 Creating Table 00:09:00 Overview of Primary Key and Foreign Key 00:04:00 Primary Key 00:04:00 Foreign Key 00:07:00 Creating Temporary tables 00:06:00 Section 05: SQL Statements for DATA Section Introduction 00:01:00 Insert statement 00:08:00 Update statement 00:05:00 Delete statement 00:03:00 Section 06: SQL Data Structure statements Section Introduction 00:01:00 CREATE table statement 00:03:00 DROP statement 00:02:00 ALTER statement 00:05:00 TRUNCATE statement 00:04:00 COMMENT in query 00:01:00 RENAME 00:02:00 Section 07: SQL User Management Create Database user 00:04:00 GRANT permissions 00:06:00 REVOKE permissions 00:04:00 Section 08: SQL Statement Basic Section Introduction 00:01:00 SQL Statement basic 00:03:00 SELECT Statement 00:07:00 SELECT DISTINCT 00:03:00 SELECT with column headings 00:03:00 Column AS statement 00:02:00 Section 09: Filtering Data rows SELECT WHERE Clause - theory 00:04:00 SELECT WHERE Clause - practical 00:07:00 Section 10: Aggregate functions Sum() 00:08:00 Min()-Max() 00:06:00 Section 11: SQL Query statements Order By statement 00:05:00 SELECT TOP clause in SQL 00:04:00 BETWEEN command 00:08:00 IN operator 00:04:00 Wildcard Characters and LIKE 00:05:00 Section 12: SQL Group by statement Section Introduction 00:01:00 Group by - theory8 00:03:00 Group by - practical 00:05:00 HAVING statement 00:04:00 Section 13: JOINS for Multiple table Data Analysis Overview of Joins 00:02:00 What are Joins 00:02:00 Inner join 00:08:00 Left outer join 00:03:00 Right outer join 00:02:00 Full outer join 00:01:00 Union 00:03:00 Cartesian Product with the Cross Join 00:03:00 Query Exercise 00:01:00 Solution for Query Exercise 00:01:00 Section 14: SQL Constraints Section introduction 00:01:00 Check constraint 00:07:00 NOT NULL constraint 00:08:00 UNIQUE constraint 00:05:00 Section 15: Views Creating Views 00:04:00 Reporting with multiple tables 00:03:00 Section 16: Advanced SQL commands Section Introduction 00:01:00 Timestamp 00:04:00 Extract from timestamp 00:03:00 Mathematical scalar functions 00:04:00 String functions 00:05:00 Sub Query 00:03:00 SELECT with calculations 00:06:00 Section 17: SQL Stored procedures Create stored procedure 00:05:00 Stored procedure with parameter 00:04:00 Section 18: Azure Data Studio Visualization Installing SandDance Extension 00:03:00 Visualization Charts 00:05:00 Multiple Table Data Charts 00:06:00 Section 19: Azure Studio SQL for Data Analysis Type Decision for Data Analysis 00:13:00 Data Analysis with Case Statement and String Text 00:09:00 Section 20: Import & Export data Section Introduction 00:01:00 Import Flat File 00:05:00 Import .csv or excel file 00:03:00 Export Data to Excel or any format 00:06:00 Section 21: Backup and Restore Database Section Introduction 00:01:00 Creating Database backup 00:04:00 Restoring Database backup 00:04:00

If you aim to enhance your Machine Learning & Artificial Intelligence skills, our comprehensive Machine Learning & Artificial Intelligence course is perfect for you. Designed for success, this Machine Learning & Artificial Intelligence course covers everything from basics to advanced topics in Machine Learning & Artificial Intelligence. Each lesson in this Machine Learning & Artificial Intelligence course is crafted for easy understanding, enabling you to become proficient in Machine Learning & Artificial Intelligence. Whether you are a beginner or looking to sharpen your existing skills, this Machine Learning & Artificial Intelligence is the ideal choice. With our Machine Learning & Artificial Intelligence exclusive bundle, you will get a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. Courses are Included in This Machine Learning & Artificial Intelligence Bundle: Course 01: Machine Learning Basics Course 02: Hands-on Machine Learning Project - Auto Image Captioning for Social Media Course 03: Machine Learning with Python Course Course 04: Project on Deep Learning - Artificial Neural Network Course 05: Data Science & Machine Learning with R Training Course 06: ChatGPT for Marketing and Productivity with AI Tools Learning Outcomes of this Machine Learning & Artificial Intelligence Understand the foundational concepts of machine learning and its applications. Gain practical experience through hands-on machine learning projects and tools. Master Python programming for effective machine learning applications and tasks. Develop deep learning models using artificial neural networks effectively. Apply data science techniques using R for comprehensive data analysis. Learn to utilise AI tools for marketing and enhancing productivity in business. Why Choose Our Course? FREE Machine Learning & Artificial Intelligence certificate accredited Get a free student ID card with Machine Learning & Artificial Intelligence Training Get instant access to this Machine Learning & Artificial Intelligence course. Learn Machine Learning & Artificial Intelligence from anywhere in the world Machine Learning & Artificial Intelligence is affordable and simple to understand This bundle is an entirely online, interactive lesson with voiceover audio Lifetime access to the Machine Learning & Artificial Intelligence course materials The Machine Learning & Artificial Intelligence comes with 24/7 tutor support So enrol now in this Machine Learning & Artificial Intelligence Today to advance your career! Start your learning journey straightaway with Machine Learning & Artificial Intelligence! This Machine Learning & Artificial Intelligence's curriculum has been designed by Machine Learning & Artificial Intelligence experts with years of Machine Learning & Artificial Intelligence experience behind them. The Machine Learning & Artificial Intelligence course is extremely dynamic and well-paced to help you understand Machine Learning & Artificial Intelligence with ease. You'll discover how to master Machine Learning & Artificial Intelligence skills while exploring relevant and essential topics. *** Course Curriculum *** Course 01: Machine Learning Basics Section 01: Introduction Section 02: Regression Section 03: Predictors Section 04: Minitab Section 05: Regression Trees Section 06: Binary Logistics Regression Section 07: Classification Trees Section 08: Data Cleaning Section 09: Data Models Section 10: Learning Success Course 02: Hands-on Machine Learning Project - Auto Image Captioning for Social Media Section 01: Introduction Section 02: Building the Auto Image Captioning Section 03: Deployment of Machine Learning App Assessment Process of Machine Learning & Artificial Intelligence Once you have completed all the courses in the Machine Learning & Artificial Intelligence bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards. CPD 60 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Machine Learning & Artificial Intelligence bundle is suitable for everyone. Requirements You will not need any prior background or expertise in this Machine Learning & Artificial Intelligence. Career path This Machine Learning & Artificial Intelligence bundle will allow you to kickstart or take your career in the related sector to the next stage. Certificates CPD Accredited Digital certificate Digital certificate - Included CPD Accredited Hard copy certificate Hard copy certificate - £29 If you are an international student, you will be required to pay an additional fee of 10 GBP for international delivery, and 4.99 GBP for delivery within the UK, for each certificate

Duration 1 Days 6 CPD hours This course is intended for This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics. Overview Working in an interactive learning environment, led by our engaging OpenAI expert you'll: Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API. set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources. Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities. Use the OpenAI API to access GPT-3 and generate high-quality text Learn how to use Whisper to improve the quality of text generation. Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals. Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Introduction to OpenAI and Azure Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. Explore OpenAI and its products Explore Azure and its offerings for AI and Machine Learning Get Hands-On: Setting up an OpenAI environment on Azure Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. Create an Azure virtual machine and installing the OpenAI SDK Configure the OpenAI environment and connecting to Azure resources Explore OpenAI GPT-3 Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. Review GPT-3 and its capabilities Use the OpenAI API to access GPT-3 Get Hands-on: Building a GPT-3 based chatbot on Azure Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. Setup an Azure Function and creating a chatbot Integrate GPT-3 with the chatbot OpenAI Whisper Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. Explore Whisper and its capabilities Use Whisper to improve the quality of text generation OpenAI DALL-E Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Explore DALL-E and its capabilities Use the OpenAI API to access DALL-E What?s Next: Keep Going! Other ways OpenAI can impact your day to day Explore great places to check for expanded tools and add-ons for Azure OpenAI Where to go for help and support Quick Look at Generative AI and its Business Implications Understanding Generative AI Generative AI in Business Ethical considerations of Generative AI

Duration 1 Days 6 CPD hours This course is intended for The primary audience for this course is database developers who plan to migrate their MySQL or Postgres DB workloads to Azure SQL DB. The secondary audience for this course is MySQL/Postgres administrators to raise awareness of the features and benefits of Azure SQL DB. Overview At the end of this course, the students will have learned: Migrate on-premises MySQL to Azure SQL DB for MySQL Migrate on-premises PostgreSQL to Azure SQL DB for PostgreSQL This course will enable the students to understand Azure SQL Database, and educate the students on what is required to migrate MySQL and PostgreSQL workloads to Azure SQL Database. Migrate to Azure SQL DB for MySQL & PostgreSQL OSS databases overview Common OSS database workloads Customer challenges in migration Migrate on-premises MySQL to Azure SQL DB for MySQL Configure and Manage Azure SQL DB for MySQL Migrate on-premises MySQL to SQL DB for MySQL Application Migration Post-migration considerations Migrate on-premises PostgreSQL to Azure SQL DB for PostgreSQL Configure and Manage Azure SQL DB for PostgreSQL Migrate on-premises MySQL to SQL DB for PostgreSQL Application Migration Post-migration considerations

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Platform Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing,storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Identify the purpose of projects on Google Cloud. Understand how Azure's resource hierarchy differs from Google Cloud's Understand the purpose of and use cases for Identity and Access Management. Understand how Azure AD differs from Google Cloud IAM. List the methods of interacting with Google Cloud. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine Understand the basics of networking in Google Cloud. Understand how Azure VPC differs from Google VPC. Understand the similarities and differences between Azure VM and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure. Deploy applications using Google Compute Engine Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Azure Blob compares to Cloud Storage. Compare Google Cloud?s managed database services with Azure SQL. Learn how to choose among the various storage options on Google Cloud. Load data from Cloud Storage into BigQuery Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Azure App Service. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Google Cloud Deployment Manager differs from Azure Resource Manager. Understand the purpose of integrated monitoring, alerting, and debugging Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from Azure Data Lake. Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from Azure's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from Azure to Google Cloud.

Duration 1 Days 6 CPD hours This course is intended for Software developers, system administrators, and IT professionals who are focused on Microsoft Windows Overview Configuring Microsoft Windows and Microsoft SQL Server in Google Compute Engine. Deploying ASP.NET MVC applications to Google Compute Engine. Deploying .NET Core applications to Google Compute Engine, Google Compute Engine, and Google Container Engine Learn how to create Windows virtual machines on Google Cloud so that you can deploy and run Microsoft Windows applications. In this course, you'll learn how to run SQL Server in Compute Engine, how to deploy instances across Google Cloud zones, and how to get more out of ASP.NET on Compute Engine, Google Container Engine, and App Engine. Introduction to Google Cloud Platform Scope and structure of GCP. Options for Windows deployment on GCP. GCP interfaces. Windows Workloads on Google Compute Engine Google Compute Engine virtual machine options. Integrating Active Directory with Google Compute Engine virtual machines. Options for running SQL Server in Google Compute Engine. Configuring SQL Server for high availability. Developing ASP.NET MVC applications Model-view-controller structure. Using Microsoft Visual Studio?s Web Project template to develop in ASP.NET. Deploying applications to Microsoft Internet Information Server (IIS) in GCE. Configuring Resilient Workloads Deploying instances across GCP zones. Using instance groups to create pools of virtual machines. Load balancing Windows applications. Delivering Next-Generation ASP.NET Core on GCP Understanding .NET Core and EF Core. Options for deploying ASP.NET Core applications on Google Cloud Platform. Deploying ASP.NET Core applications on Google Compute Engine. Deploying ASP.NET Core applications on Google Container Engine. Deploying ASP.NET Core applications on Google App Engine. Additional course details: Nexus Humans Develop and Deploy Windows Applications on Google Cloud Platform 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 Develop and Deploy Windows Applications on Google Cloud Platform 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.

Duration 1 Days 6 CPD hours This course is intended for The primary audience for this course is database developers who plan to migrate their MongoDB or Cassandra DB workloads to Azure using Cosmos DB. Overview Building Globally Distributed Applications with Cosmos DB Migrate MongoDB Workloads to Cosmos DB Migrate Cassandra DB Workloads to Cosmos DB This course will teach the students what is Cosmos DB and how you can migrate MongoDB and Cassandra workloads to Cosmos DB. Building Globally Distributed Applications with Cosmos DB Cosmos DB overview Cosmos DB APIs Provisioning Throughput Partitioning/Sharding Best Practices Migrate MongoDB Workloads to Cosmos DB Understand Migration Benefits Migration Planning Data Migration Application Migration Post-migration considerations Migrate Cassandra DB Workloads to Cosmos DB Understand Migration Benefits Migration Planning Data Migration Application Migration Post-migration considerations Additional course details: Nexus Humans DP-060T00 Migrate NoSQL workloads to Azure Cosmos DB 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 DP-060T00 Migrate NoSQL workloads to Azure Cosmos DB 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.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies