5837 Practitioner courses

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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 5 Days 30 CPD hours This course is intended for This course is intended for System administrators who are either new to JBoss or have experience with JBoss Enterprise Application Platform 5. Base experience with system administration on Microsoft Windows, UNIX, or Linux© operating systems. Understanding of hardware and networking. Note: No prior knowledge of Java, scripting, or JBoss Developer Studio is required. Overview Upon successful completion of this course, students will be able to take the Red Hat Certified JBoss Administration (RHCJA) Exam. In this course, you'll discover the best practices for installing and configuring Red Hat© JBoss© Enterprise Application Platform (JBoss EAP) 7. Through hands-on labs, you'll learn the essential, real-world tasks that you need to know as a system administrator to effectively deploy and manage applications on JBoss EAP. This course will help prepare you to take the Red Hat Certified Specialist in Enterprise Application Server Administration exam. 1 - OVERVIEW OF JBOSS ENTERPRISE APPLICATION PLATFORM Learn the concepts and terminology of JBoss© Enterprise Application Platform 6, and install and run a JBoss Enterprise Application Platform instance. 2 - CONFIGURE JBOSS ENTERPRISE APPLICATION PLATFORM IN STANDALONE MODE Understand the directory structure of standalone mode and the standalone.xml configuration file, and deploy an application onto a standalone server. 3 - CONFIGURE JBOSS ENTERPRISE APPLICATION PLATFORM IN DOMAIN MODE Set up a domain, including a domain controller managing multiple host controllers. Also learn how to deploy an application onto a domain. 4 - CONFIGURE SERVERS Define a server group on a host, add servers to a server group, and deploy an application to a server group. 5 - USE THE CLI TOOL Learn how the command-line Interface tool can be used to script administrative tasks. 6 - THE DATASOURCE SUBSYSTEM Understand database connection pools in JBoss Enterprise Application Platform and how to configure the datasource subsystem. Learn how to deploy a Java? Database Connectivity (JDBC) driver and define a datasource. 7 - THE LOGGING SUBSYSTEM Configure the logging subsystem, including configuring the built-in handlers and loggers. 8 - THE MESSAGING SUBSYSTEM Understand JMS and HornetQ, including how to define and configure connection factories and destinations (topics and queues). 9 - THE SECURITY SUBSYSTEM Configure the security subsystem, including securing the management interfaces, securing an application, and securing a JMS destination. Also learn how to define an Lightweight Directory Access Protocol (LDAP) and database security realm. 10 - JVM CONFIGURATION Configure the JVM for both standalone and domain modes. 11 - MIGRATING APPLICATIONS TO JBOSS ENTERPRISE APPLICATION PLATFORM 6 Understand classpath and module dependencies, JPA issues, updating security settings, and other tips on how to migrate an application to JBoss Enterprise Application Platform 6. 12 - THE WEB SUBSYSTEM Configure the HTTP and HTTPS Connectors, set up an SSL connection, and configure an AJP Connector. 13 - INTRODUCTION TO CLUSTERING Set up JBoss Enterprise Application Platform 6 in a cluster in both standalone and domain mode. Additional course details: Nexus Humans Red Hat JBoss Application Administration I with Exam (AD249) 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 Red Hat JBoss Application Administration I with Exam (AD249) 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 This course does not have any technical knowledge prerequisites for the learners, besides being proficient in using a computer and the Internet. IT and/or AI knowledge is a benefit but not a hard requirement. Given the rapid development of AI and the broad range of its applications in everyday life, it is crucial for anyone to attend this course to update their digital skills in an ever-changing world. It is expected that all learners have registered for a free account of OpenAI ChatGPT at https://chat.openai.com. Overview Discover how AI relates to other 4th industrial revolution technologies Learn about AI, ML, and associated cognitive services Overview of AI development frameworks, tools and services Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail The core aim of this ?AI for beginners? course is to introduce its audience to Artificial Intelligence (AI) and Machine Learning (ML) technologies and allow them to understand the practical applications of AI in their everyday personal and professional life. Moreover, the course aims to provide a handful of demos and hands-on exercises to allow the learners to familiarize themselves with usage scenarios of OpenAI ChatGPT and other Generative AI (GenAI) models. The content of this course has been created primarily by using the OpenAI ChatGPT model. AI theoretical concepts. Introduction to AI, ML, and associated cognitive services (Computer vision, Natural language processing, Speech analysis, Decision making). How AI relates to other 4th industrial revolution technologies (cloud computing, edge computing, internet of things, blockchain, metaverse, robotics, quantum computing). AI model classification by utilizing mind maps and the distinctive role of Gen AI models. Introduction to the OpenAI ChatGPT model and alternative generative AI models. Familiarization with the basics of the ChatGPT interface (https://chat.openai.com). Talking about Responsible AI: Security, privacy, compliance, copyright, legal challenges, and ethical implications. AI practical applications Overview of AI development frameworks, tools and services. AI aggregators review. Hand-picked AI tool demos: a.Workplace productivity and the case of Microsoft 365 Copilot. b.The content creation industry. Create text, code, images, audio and video with Gen AI. c.Redefining the education sector with AI-powered learning. Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail: a.Prompting and plugin demos. b.Code interpreter demos. Closing words. Discussion with an AI model on the future of AI. Additional course details: Nexus Humans AI for beginners 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 for beginners 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 Overview In this course you?ll learn how to: Containerize and deploy a new Python script Configure the deployment with ConfigMaps, Secrets and SecurityContexts Understand multi-container pod design Configure probes for pod health Update and roll back an application Implement services and NetworkPolicies Use PersistentVolumeClaims for state persistence And more In this vendor agnostic course, you will use Python to build, monitor and troubleshoot scalable applications in Kubernetes. Introduction Objectives Who You Are The Linux Foundation Linux Foundation Training Preparing Your System Course Registration Labs Kubernetes Architecture What Is Kubernetes? Components of Kubernetes Challenges The Borg Heritage Kubernetes Architecture Terminology Master Node Minion (Worker) Nodes Pods Services Controllers Single IP per Pod Networking Setup CNI Network Configuration File Pod-to-Pod Communication Cloud Native Computing Foundation Resource Recommendations Labs Build Container Options Containerizing an Application Hosting a Local Repository Creating a Deployment Running Commands in a Container Multi-Container Pod readinessProbe livenessProbe Testing Labs Design Traditional Applications: Considerations Decoupled Resources Transience Flexible Framework Managing Resource Usage Multi-Container Pods Sidecar Container Adapter Container Ambassador Points to Ponder Labs Deployment Configuration Volumes Overview Introducing Volumes Volume Spec Volume Types Shared Volume Example Persistent Volumes and Claims Persistent Volume Persistent Volume Claim Dynamic Provisioning Secrets Using Secrets via Environment Variables Mounting Secrets as Volumes Portable Data with ConfigMaps Using ConfigMaps Deployment Configuration Status Scaling and Rolling Updates Deployment Rollbacks Jobs Labs Security Security Overview Accessing the API Authentication Authorization ABAC RBAC RBAC Process Overview Admission Controller Security Contexts Pod Security Policies Network Security Policies Network Security Policy Example Default Policy Example Labs Exposing Applications Service Types Services Diagram Service Update Pattern Accessing an Application with a Service Service without a Selector ClusterIP NodePort LoadBalancer ExternalName Ingress Resource Ingress Controller Labs Troubleshooting Troubleshotting Overview Basic Troubleshooting Steps Ongoing (Constant) Change Basic Troubleshooting Flow: Pods Basic Troubleshooting Flow: Node and Security Basic Troubleshooting Flow: Agents Monitoring Logging Tools Monitoring Applications System and Agent Logs Conformance Testing More Resource Labs Additional course details: Nexus Humans Kubernetes for App Developers 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 Kubernetes for App Developers 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 4 Days 24 CPD hours This course is intended for This course is intended for: Network administrators Network engineers with little or no programming or Python experience Network managers Systems engineers Overview After taking this course, you should be able to: Create a Python script Describe data types commonly used in Python coding Describe Python strings and their use cases Describe Python loops, conditionals, operators, and their purposes and use cases Describe Python classes, methods, functions, namespaces, and scopes Describe the options for Python data manipulation and storage Describe Python modules and packages, their uses, and their benefits Explain how to manipulate user input in Python Describe error and exception management in Python Describe Python code debugging methods The Programming for Network Engineers (PRNE) v2.0 course is designed to equip you with fundamental skills in Python programming. Through a combination of lectures and lab experience in simulated network environments, you will learn to use Python basics to create useful and practical scripts with Netmiko to retrieve data and configure network devices. Upon completion of this course, you should have a basic understanding of Python, including the knowledge to create, apply, and troubleshoot simple network automation scripts. Course Outline Introducing Programmability and Python for Network Engineers Scripting with Python Examining Python Data Types Manipulating Strings Describing Conditionals, Loops, and Operators Exploring Classes, Methods, Functions, Namespaces, and Scopes Exploring Data Storage Options Exploring Python Modules and Packages Gathering and Validating User Input Analyzing Exceptions and Error Management Examining Debugging Methods Course Summary

Duration 2 Days 12 CPD hours This course is intended for This class is intended for network engineers and network admins that are either using Google Cloud Platform or are planning to do so. The class is also for individuals that want to be exposed to software-defined networking solutions in the cloud. Overview Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCsInterconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network TiersConfigure Cloud NAT or Private Google Access to provide instances without public IP addresses access to other services Deploy networks declaratively using Cloud Deployment Manager or Terraform Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems Learn about the broad variety of networking options on Google Cloud. This course uses lectures, demos, and hands-on labs to help you explore and deploy Google Cloud networking technologies, including Virtual Private Cloud (VPC) networks, subnets, and firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN; and Cloud NAT. You'll also learn about common network design patterns and automated deployment using Cloud Deployment Manager or Terraform. Google Cloud VPC Networking Fundamentals Recall that networks belong to projects. Explain the differences among default, auto, and custom networks. Create networks and subnets. Explain how IPv4 addresses are assigned to Compute Engine instances. Publish domain names using Google Cloud DNS. Create Compute Engine instances with IP aliases. Create Compute Engine instances with multiple virtual network. Controlling Access to VPC Networks Outline how IAM policies affect VPC networks. Control access to network resources using service accounts. Control access to Compute Engine instances with tag-based firewall rules. Sharing Networks across Projects Outline the overall workflow for configuring Shared VPC. Differentiate between the IAM roles that allow network resources to be managed. Configure peering between unrelated VPC Networks. Recall when to use Shared VPC and when to use VPC Network Peering. Load Balancing Recall the various load balancing services. Configure Layer 7 HTTP(S) load balancing. Whitelist and blacklist IP traffic with Cloud Armor. Cache content with Cloud CDN. Explain Layer 4 TCP or SSL proxy load balancing. Explain regional network load balancing. Configure internal load balancing. Recall the choices for enabling IPv6 Internet connectivity for Google Cloud load balancers. Determine which Google Cloud load balancer to use when. Hybrid Connectivity Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud. Explain Dedicated Interconnect and Partner Interconnect. Describe the workflow for configuring a Dedicated Interconnect. Build a connection over a VPN with Cloud Router. Determine which Google Cloud interconnect service to use when. Explain Direct Peering and Partner Peering. Determine which Google Cloud peering service to use when. Networking Pricing and Billing Recognize how networking features are charged for. Use Network Service Tiers to optimize spend. Determine which Network Service Tier to use when. Recall that labels can be used to understand networking spend. Network Design and Deployment Explain common network design patterns. Configure Private Google Access to allow access to certain Google Cloud services from VM instances with only internal IP addresses. Configure Cloud NAT to provide your instances without public IP addresses access to the internet. Automate the deployment of networks using Deployment Manager or Terraform. Launch networking solutions using Cloud Marketplace. Network Monitoring and Troubleshooting Configure uptime checks, alerting policies and charts for your network services. Use VPC Flow Logs to log and analyze network traffic behavior.

Duration 4 Days 24 CPD hours This course is intended for This course is intended for Network Administrators, Network Engineers, Network Operations Technicians, and students interested in learning about programming. Overview Upon completing this course, the learner will be able to meet these overall objectives:Understand and describe basic and advanced Python terminologiesIdentify and correct Python script errorsPull and reconfigure networking devices using Python scriptsIntroduction to RESTful API utilizing JSON and XML This course focuses on the interoperability of Python programming with Networking to prepare students for Software Defined Networking. This class is designed for students looking to add programming skills in preparation for various ACI & SDN technologies. Basic Constructs Describe and execute strings Describe and execute printing Describe and execute variables Describe and execute built in methods Describe and execute input from users Describe Boolean expressions Advanced Constructs Describe and execute redundant scripts Describe and execute functions Describe and execute classes Scaling Python Describe and execute Reading Files scripts Describe and execute Writing to File scripts Describe and execute substituting strings and integers Describe and execute while loops Describe and execute for loops and lists Describe and execute slicing Updating and edit a list Error checking Dictionaries and Regular Expressions Describe and execute dictionaries Delete dictionaries Describe and execute dictionaries with lists Describe and execute regular expressions (match, search, findall, sub) XML and JSON Describe XML format Describe JSON Format Example of XML Example of JSON What is an XML Schema? Describe SOAP Example of CURL with a Web Based Application Example of a SOAP exchange with a Web based Application Example of REST with a Web based Application Putting It All Together Apply Python principles with a SOAP Client Apply Python principles with REST and WSDL Apply native Python modules Additional course details: Nexus Humans Introducing Python and Web Services Programmability for Network Engineers - v3.0 IPWSP-NE 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 Introducing Python and Web Services Programmability for Network Engineers - v3.0 IPWSP-NE 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 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 5 Days 30 CPD hours This course is intended for This course is intended for: Network administrators Network engineers with little or no programming or Python experience Network managers Systems engineers Overview After taking this course, you should be able to: Create a Python script Describe data types commonly used in Python coding Describe Python strings and their use cases Describe Python loops, conditionals, operators, and their purposes and use cases Describe Python classes, methods, functions, namespaces, and scopes Describe the options for Python data manipulation and storage Describe Python modules and packages, their uses, and their benefits Explain how to manipulate user input in Python Describe error and exception management in Python Describe Python code debugging methods The Programming for Network Engineers (PRNE) v2.0 course is designed to equip you with fundamental skills in Python programming. Through a combination of lectures and lab experience in simulated network environments, you will learn to use Python basics to create useful and practical scripts with Netmiko to retrieve data and configure network devices. Upon completion of this course, you should have a basic understanding of Python, including the knowledge to create, apply, and troubleshoot simple network automation scripts. Course outline Introducing Programmability and Python for Network Engineers Scripting with Python Examining Python Data Types Manipulating Strings Describing Conditionals, Loops, and Operators Exploring Classes, Methods, Functions, Namespaces, and Scopes Exploring Data Storage Options Exploring Python Modules and Packages Gathering and Validating User Input Analyzing Exceptions and Error Management Examining Debugging Methods Course Summary Lab outline Execute Your First Python Program Use the Python Interactive Shell Explore Foundation Python Data Types Explore Complex Python Data Types Use Standard String Operations Use Basic Pattern Matching Reformat MAC Addresses Use the if-else Construct Use for Loops Use while Loops Create and Use Functions Create and Use Classes Use the Python main() Construct Traverse the File Structure Read Data in Comma-Separated Values (CSV) Format Read, Store, and Retrieve Data in XML Format Read, Store, and Retrieve Date in JavaScript Object Notation (JSON) Format Read, Store, and Retrieve Data in a Raw or Unstructured Format Import Modules from the Python Standard Library Import External Libraries Create a Python Module Prompt the User for Input Use Command-Line Arguments Manage Exceptions with the try-except Structure Manage Exceptions with the try-except-finally Structure Use Assertions Use Simple Debugging Methods Use the Python Debugger Code a Practical Debugging Script

Duration 1 Days 6 CPD hours This course is intended for Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services. Describe ways in which customers have used Google Cloud. Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine. Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore. Make basic use of BigQuery, Google's managed data warehouse for analytics. This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies. Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform. 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 Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Lab: Deploying Applications Using Google Compute Engine. Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Introduction to Hybrid and Multi-Cloud computing (Anthos). Lab: Deploying Applications Using Google Container Engine. Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Deployment and Monitoring Understand the purpose of template-based creation and management of resources. Understand the purpose of integrated monitoring, alerting, and debugging. Lab: Getting Started with Stackdriver and Deployment Manager. Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery. Summary and Review Summary and Review. What's Next?.