13 Python Scripting courses in Liverpool delivered Live Online

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is system installers, system integrators, system administrators, network administrators, and solutions designers. Overview At the end of this course, you will be able to: Describe the NSO's transactional application framework and mapping model options Describe the Reactive Fastmap design pattern and the NSO Configuration Database (CDB) subscriber in the NSO Transaction model Simplify packages to remove the need for subscriber applications, scale orchestration solutions, and integrate NSO with external systems (east-west integration)Describe the Cisco ESC architecture and integration with NSO, and how the NSO VNF Orchestration (VNFO) Release 2 bundle interacts with ESC for orchestration This course explores how to create advanced services using the NSO application framework and Python scripting with both new and existing Layer 3 Multiprotocol Label Switching (MPLS) VPN services. Students will also learn how to manage and scale these services, and how to use NSO Network Functions Virtualization (NFV) orchestration features and Cisco Elastic Services Controller (ESC) to manage Virtualized Network Functions (VNFs). Cisco NSO Programmability NSO Application Framework NSO Python Scripting NSO Python and Template-Based Services Resources Augmenting Cisco NSO Service Service Lifecycle and Integration Options Overview Greenfield Layer 3 MPLS VPN Service Brownfield Layer 3 MPLS VPN Service Managed Services Managed Services Overview Stacked Service Design Overview Design-Managed Network Services Scaling Service Orchestration Cisco NSO Network Functions Virtualization (NFV) Orchestration ETSI MANO Cisco ESC Cisco NSO Orchestration Additional course details: Nexus Humans Cisco Network Services Orchestrator Advanced Design (NSO300) 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 Cisco Network Services Orchestrator Advanced Design (NSO300) 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 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it's often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. Additional course details: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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.

CCNP training course description The Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR) v1.2 course provides the knowledge and skills needed to configure, troubleshoot, and manage enterprise wired and wireless networks. You'll learn to implement security principles within an enterprise network and how to overlay network design using solutions such as SDAccess and SD-WAN. Course content includes 3 days of self-study material. This course helps you prepare for the 350-401 Implementing Cisco Enterprise Network Core Technologies (ENCOR) exam What will you learn Configure, troubleshoot, and manage enterprise wired and wireless networks Implement security principles within an enterprise network Prepare you prepare to take the 350-401 Implementing Cisco Enterprise Network Core Technologies (ENCOR) exam CCNP training course details Who will benefit: Mid-level network engineers, Network administrators, Network support technicians, Help desk technicians. Prerequisites: Implementation of Enterprise LAN networks. Basic understanding of Enterprise routing and wireless connectivity, and Python scripting Duration 5 days CCNP training course content Cisco Enterprise Network Architecture: Access, distribution, core in the hierarchical network. Cisco Switching Paths: Switching mechanisms, TCAM, CAM, process switching, fast switching, and CEF. Implementing Campus LAN Connectivity: Troubleshoot L2 connectivity using VLANs and trunkingBuilding Redundant Switched Topology: STP Implementing Layer 2 Port Aggregation Troubleshoot link aggregation using Etherchannel EIGRP Implement and optimize OSPFv2/v3, including adjacencies, packet types, and areas, summarization, and route filtering for IPv4/v6 Implement EBGP interdomain routing, path selection, and single and dual-homed networkingImplementing Network Redundancy: HSRP and VRRP Implement static and dynamic NAT Virtualization Protocols and TechniquesVPNs and Interfaces: Overlay technologies such as VRF, GRE, VPN, and LISP Wireless Principles: RF, antenna characteristics, and wireless standards.Wireless Deployment: Models available, autonomous AP deployments and cloud-based designs within the centralized Cisco WLC architecture Wireless Roaming and Location ServicesWireless AP Operation: How APs communicate with WLCs to obtain software, configurations, and centralized managementWireless Client Authentication: EAP, WebAuth, and PSK wireless client authentication on a WLC. Troubleshoot wireless client connectivity issues using various available tools Troubleshoot networks using services such as NTP, SNMP, Cisco IP SLAs, NetFlow, and Cisco IOS EEM Explain network analysis and troubleshooting tools, which include show and debug commands, as well as best practices in troubleshootingMulticast Protocols: IGMP v2/v3, PIM DM/SM and RPs Introducing QoS: Concepts and features. Implementing Network Services: Secure administrative access for Cisco IOS devices using CLI access, RBAC, ACL, and SSH, and device hardening concepts to secure devices from less secure applications Using Network Analysis ToolsInfrastructure Security: Scalable administration using AAA and the local database, features and benefits Enterprise Network Security Architecture: VPNs, content security, logging, endpoint security, personal firewalls, and other security features. Automation and Assurance with Cisco DNA Center: Purpose, function, features, and workflow. Intent-Based Networking, for network visibility, proactive monitoring, and application experienceCisco SD-Access Solution: Nodes, fabric control plane, and data plane, VXLAN gatewaysCisco SD-WAN Solution: Components and features of Cisco SD-WAN solutions, including the orchestration, management, control, and data planesBasics of Python Programming: Python components and conditionals with script writing and analysis Network Programmability: NETCONF and RESTCONF APIs in Cisco DNA Center and vManage Labs: Investigate the CAM. Analyze CEF. Troubleshoot VLAN and Trunk Issues. Tuning STP and Configuring RSTP. Configure MSTP. Troubleshoot EtherChannel. Implement Multi-area OSPF. Implement OSPF Tuning. Apply OSPF Optimization. Implement OSPFv3. Configure and Verify Single-Homed EBGP. Implementing HSRP. Configure VRRP. Implement NAT. Configure and Verify VRF. Configure and Verify a GRE Tunnel. Configure Static VTI Point-to-Point Tunnels. Configure Wireless Client Authentication in a Centralized Deployment. Troubleshoot Wireless Client Connectivity Issues. Configure Syslog. Configure and Verify Flexible NetFlow. Configuring Cisco IOS EEM. Troubleshoot Connectivity and Analyze Traffic with Ping, Traceroute, and Debug. Configure and Verify Cisco IP SLAs. Configure Standard and Extended ACLs. Configure Control Plane Policing. Implement Local and Server-Based AAA. Writing and Troubleshooting Python Scripts. Explore JSON Objects and Scripts in Python. Use NETCONF Via SSH. Use RESTCONF with Cisco IOS XE.

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. An Overview of Python Why Python? Python in the Shell Python in Web Notebooks (iPython, Jupyter, Zeppelin) Demo: Python, Notebooks, and Data Science Getting Started Using variables Builtin functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control White space Conditional expressions Relational and Boolean operators While loops Alternate loop exits Sequences, Arrays, Dictionaries and Sets About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Generator Expressions Nested sequences Working with Dictionaries Working with Sets Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Functions Defining functions Parameters Global and local scope Nested functions Returning values Essential Demos Sorting Exceptions Importing Modules Classes Regular Expressions The standard library Math functions The string module Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Python and Data Science Data Science Essentials Pandas Overview NumPy Overview SciKit Overview MatPlotLib Overview Working with Python in Data Science Additional course details: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) 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 Python for Data Science: Hands-on Technical Overview (TTPS4873) 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 is an Introductory level course for experienced Linux system administrators, DevOps engineers, infrastructure automation engineers, and systems design engineers. Ideally students should have familiarity with basic Python scripting. Attendees without programming skills can follow along with the scripting portion of the labs. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert practitioner attendees will explore how to: Describe Ansible concepts and install Red Hat Ansible Engine (optional - we can pre-install is as well if desired, depending on the audience) Deploy Ansible and Configure Ansible to manage hosts and run ad hoc Ansible commands. Implement playbooks Write a simple Ansible playbook and run it to automate tasks on multiple managed hosts. Manage variables and facts Write playbooks that use variables to simplify management of the playbook and facts to reference information about managed hosts. Implement task control; Manage task control, handlers, and task errors in Ansible playbooks. Deploy files to managed hosts Deploy, manage, and adjust files on hosts managed by Ansible. Manage large projects Write playbooks that are optimized for larger, more complex projects. Simplify playbooks with roles Use Ansible roles to develop playbooks more quickly and to reuse Ansible code. Troubleshoot Ansible Troubleshoot playbooks and managed hosts. Automate Linux administration tasks Automate common Linux system administration tasks with Ansible This lab-intensive course is geared toward those responsible for automation of configuration management; consistent and repeatable application deployment; provisioning and deployment of development, testing, and production servers; and integration with DevOps CI/CD workflows. Throughout the course you will explore core Ansible features such as automatic provisioning, configuration management, service deployment and operational processes. Ansible Overview Overview of Architecture Overview of Deployments Inventory Deploying Ansible Installing Configuration Files Running Ad Hoc Commands Dynamic Inventory Playbooks Writing YAML Files Modules Variables and Inclusions Variables Facts Inclusions Task Control Constructing Flow Control Handlers Tags Handling Errors Jinja2 Templates Jinja2 Templates Jinja2 Templates Roles Role Structure Creating Roles Deploying Roles with Ansible Galaxy Optimizing Ansible Configuring Connection Types Configuring Delegation Configuring Parallelism Ansible Vault Configuring Ansible Vault Executing with Ansible Vault Troubleshooting Ansible Troubleshooting Playbooks Troubleshooting Managed Hosts Ansible Tower Ansible Tower overview Installing Account management Hosts Jobs Optional: Ansible in a DevOps Environment Provisioning Vagrant Machines Deploying Vagrant in a DevOps Environment Deploying Docker in a DevOps Environment Additional course details: Nexus Humans Introduction to Ansible: Automation with Ansible (TTDV7580) 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 Introduction to Ansible: Automation with Ansible (TTDV7580) 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 2 Days 12 CPD hours This course is intended for This in an introductory-level course geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies. Attendees should have prior basic Python scripting experience. Students should have some familiarity with tools to be used in this course: PyCharm, Jupyter Notebook and basic GIT. Overview Working within in a hands-on learning environment students will learn to: Become proficient with pytest from day one by solving real-world testing problems Use pytest to write tests more efficiently Scale from simple to complex and functional testing Write and run simple and complex tests Organize tests in fles and directories Find out how to be more productive on the command line Markers and how to skip, xfail and parametrize tests Explore fxtures and techniques to use them effectively, such as tmpdir, pytestconfg, and monkeypatch Convert unittest suites to pytest using little-known techniques The pytest framework is simple to use but powerful enough to cover complex testing integration scenarios. PyTest is considered by many to be the true Pythonic approach to testing in Python. Geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies, Test Automation with Python is a hands-on, two day Python testing course that provides students with the skills required to get started with PyTest right away. Participnats will learn how to get the most out of it in their daily workflow, exploring powerful mechanisms and plugins to facilitate many common testing tasks. Students will also learn how to use pytest in existing unittestbased test suites and will learn some tricks to make the jump to a pytest-style test suite quickly and easily. Python Refresher Python Overview Python Basics Python Lab Introducing PyTest Why Spend time writing test UnitTest Module Why PyTest? Introductory Lab Writing and Running Test Installing PyTest Writing and Running Tests Organizing files and packages Command Line options Configure pytest.ini Install and Config Lab Markers and Parameters Mark Basics Built-in marks Parameterization Markers and Parameters Lab Fixtures Introduction to Fixtures Sharing fixtures with conftest.py files Scopes Autouse Parameterizing fixtures Using marks from fixtures Built-in fixtures Best Practices Fixtures Lab Fixtures Lab 2 Plugins Finding and installing plugins Overview of plugins Plugin Lab From UnitTest to PyTest Use PyTest as a Test Runner Convert asserts with unitest2pytest Handling setup/teardown Managing test hierarchies Refactoring test utilities Migration strategies Additional course details: Nexus Humans Test Automation with Python (TTPS4832) 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 Test Automation with Python (TTPS4832) 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 Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

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

Network automation course description This course is not a soft skills course covering the concepts of DevOps but instead concentrates on the technical side of tools and languages for network DevOps. Particular technologies focussed on are ansible, git and Python enabling delegates to leave the course ready to starting automating their network. Hands on sessions follow all major sections. More detailed courses on individual aspects of this course are available. What will you learn Evaluate network automation tools. Automate tasks with ansible. Use git for version control. Use Python to manage network devices. Use Python libraries for network devices. Network automation course details Who will benefit: Network engineers. Prerequisites: TCP/IP foundation for engineers. Duration 5 days Network automation course contents What is DevOps Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Initial configuration Configuring SSH, ZTP, POAP. Hands on Initial lab configuration. Getting started with ansible The language, the engine, the framework. Uses of ansible, orchestration. The architecture, Controlling machines, nodes, Agentless, SSH, modules. Configuration management, inventories, playbooks, modules, roles. Hands on Installing ansible, running ad hoc commands. Ansible playbooks ansible-playbook, YAML, plays, tasks, handlers, modules. Playbook variables. Register module, debug module. Hands on Running playbooks. Ansible Inventories /etc/ansible/hosts, hosts, groups, static inventories, dynamic inventories. Inventory variables, external variables. Limiting hosts. Hands on Static inventories, variables in inventory files. Ansible modules for networking Built in modules, custom modules, return values. Core modules for network operations. Cisco and/ or Juniper modules. ansible_connection. Ansible 2.6 CLI. Hands on Using modules. Ansible templating and roles Configuration management, full configurations, partial configurations. The template module, the assemble module, connection: local, Jinja2 templates, variables, if, for, roles. Hands on Generating multiple configurations from a template. Network programming and modules Why use Python? Why use ansible? alternatives, ansible tower, Linux network devices. Programming with Python Scripting versus application development, Python interactive mode, Python scripts, Python 2.7 vs Python 3. A simple Python script. Variables, loops, control statements, operators. PEP style guide. Python IDEs. Hands on Simple Python programs. More Python programming Functions. Classes, objects and instances, modules, libraries, packages. Python strings, Python file handling, pip list, pip install, Hands on Python programming with pyping. Git Distributed version control, repositories, Git and GitHub, Alternatives to GitHub, Installing git, git workflows, creating repositories, adding and editing files, branching and merging, merge conflicts. Hands on working with Git. Python and networking APIs, Sockets, Telnetlib, pysnmp, ncclient, ciscoconfparse. Paramiko SSH and Netmiko Integrating Python and network devices using SSH. Netmiko, Netmiko methods. Hands on Netmiko. PyEZ Juniper, NETCONF, installing PyEZ, a first pyEZ script, pyEZ configuration management. Hands on Juniper configuration management with pyEZ. NAPALM What is NAPALM, NAPALM operations, getters, Replace, merge, compare, commit, discard. Hands on Configuration with NAPALM. Integrating ansible and NAPALM. Python and REST REST APIs, enabling the REST API. Accessing the REST API with a browser, cURL, Python and REST, the request library. Hands on Using a REST API with network devices.

Duration 5 Days 30 CPD hours This course is intended for Experienced Programmers and Systems Administrators. Overview Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review. This course is ?skills-centric?, designed to train attendees in core Python and web development skills beyond an intermediate level, coupling the most current, effective techniques with best practices. Working within in an engaging, hands-on learning environment, guided by our expert Python practitioner, students will learn to: ? Create working Python scripts following best practices ? Use python data types appropriately ? Read and write files with both text and binary data ? Search and replace text with regular expressions ? Get familiar with the standard library and its work-saving modules ? Use lesser-known but powerful Python data types ? Create 'real-world', professional Python applications ? Work with dates, times, and calendars ? Know when to use collections such as lists, dictionaries, and sets ? Understand Pythonic features such as comprehensions and iterators ? Write robust code using exception handling An introductory and beyond-level practical, hands-on Python training course that leads the student from the basics of writing and running Python scripts to more advanced features. An Overview of Python What is python? 1 -- An overview of Python What is python? Python Timeline Advantages/Disadvantages of Python Getting help with pydoc The Python Environment Starting Python Using the interpreter Running a Python script Python scripts on Unix/Windows Editors and IDEs Getting Started Using variables Built-in functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control White space Conditional expressions Relational and Boolean operators While loops Alternate loop exits Sequences About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Generator Expressions Nested sequences Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Converting binary data with struct Dictionaries and Sets About dictionaries Creating dictionaries Iterating through a dictionary About sets Creating sets Working with sets Functions Defining functions Parameters Global and local scope Nested functions Returning values Sorting The sorted() function Alternate keys Lambda functions Sorting collections Using operator.itemgetter() Reverse sorting Errors and Exception Handling Syntax errors Exceptions Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages The import statement Module search path Creating Modules Using packages Function and Module aliases Classes About o-o programming Defining classes Constructors Methods Instance data Properties Class methods and data Regular Expressions RE syntax overview RE Objects Searching and matching Compilation flags Groups and special groups Replacing text Splitting strings The standard library The sys module Launching external programs Math functions Random numbers The string module Reading CSV data Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Working with the file system Paths, directories, and filenames Checking for existence Permissions and other file attributes Walking directory trees Creating filters with fileinput Using shutil for file operations 17 ? Advanced data handling Defaultdict and Counter Prettyprinting data structures Compressed archives (zip, gzip, tar, etc.) Persistent data Advanced data handling Defaultdict and Counter Prettyprinting data structures Compressed archives (zip, gzip, tar, etc.) Persistent data Network services Grabbing web content Sending email Using SSH for remote access Using FTP Writing real-life applications Parsing command-line options Detecting the current platform Trapping signals Implementing logging Python Timeline Advantages/Disadvantages of Python Getting help with pydoc