36 Python courses in London

REST and RESTCONF training course description An introduction to REST and RESTCONF using Python. The course progresses from how to use them onto how they work and then looks at using them from within Python all the time on network devices. What will you learn Explain what REST and RESTCONF are. Use the REST API on network device. Use RESTCONF. REST and RESTCONF training course details Who will benefit: Network engineers. Prerequisites: Python for network engineers. Duration 1 day REST and RESTCONF training course contents Using REST Curl, Browser plugins, Postman, RESTClient, Python. Hands on Using the REST API on network devices. What is REST? What is REST? Architecture, APIs, RESTful APIs, APIs over HTTP/HTTPS, URIs, resources, HTTP methods, GET, POST, PUT, DELETE. CRUD. Comparison with other APIs. Hands on REST analysis with Wireshark. Rest conventions Passing parameters, return values, HTTP status, JSON. XML. Hands on Configuring REST on network devices, changing format of responses, POST requests, using parameters. Configuring network devices with REST Invoking multiple RPCs. Hands on Device configuration with REST. The request library RESTFUL APIs in Python, the request library, Installation, example to retrieve the interface configuration. Hands on Using the Python requests library on network devices. RESTCONF What is RESTCONF? YANG and NETCONF, relationship with REST, RESTCONF URIs, A RESTCONF example with ietf-interfaces, RESTCONF responses. PATCH. Hands on Using RESTCONF to update a network device configuration.

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.

Network automation training course description This course concentrates on the technical side of tools and languages for network DevOps rather than the soft skills. These tools include Python, Ansible, Git and NAPALM By the end of the course delegates should be able to recognise the tools that they can use to automate their networks and be able to use the knowledge gained to feel confident approaching network automation. What will you learn Describe network DevOps. Choose network automation tools. Explain the role of various network automation technologies including: Python Ansible Git NAPALM Network automation training course details Who will benefit: Those wishing to learn about the tools of network automation. Prerequisites: Introduction to data communications. Duration 1 day Network automation training course contents What is DevOps and network automation Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Unit testing. Hype vs reality. Benefits and features. Network monitoring and troubleshooting Traditional methods, SNMP. Netflow and xflow. Traditional automation. Streaming telemetry. Event driven automation. gRPC, Protocol buffers. Configuration management Catch 22 and initial configuration. ZTP, POAP. Traditional automation. TFTP. Ansible vs the rest (chef, salt, puppet). Jinja2 and templating. How ansible works. Network programmability Programming languages. Linux, shell scripting. Python vs the rest. Off box vs on box automation. Python network libraries Sockets pysnmp, ncclient, paramiko, netmiko, pyez, NAPALM. APIs Proprietary APIs, CLI, NETCONF, RETCONF. YANG, XML, YAML, JSON. Other tools Git, GitHub, Jenkins, JIRA and others.

Why Learn Sketchup and Stable Diffusion Rendering Course? Course Link SketchUp and Stable Diffusion Rendering Course. An AI image creation course designed to explore AI image creation techniques and master the use of advanced AI technology. You'll learn Ai 3D modeling, advanced rendering, and lighting techniques. Duration: 16 hrs. Method: 1-on-1 Online Over Zoom is also available. Schedule: Tailor your own schedule by pre-booking a convenient hour of your choice, available from Mon to Sat between 9 am and 7 pm. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. Sketchup and Stable Diffusion Rendering Course (16 hours) Module 1: Introduction to Sketchup (2 hours) Overview of Sketchup software and interface navigation Basic drawing tools and geometry creation techniques Module 2: Texturing and Materials (2 hours) Applying textures and customizing materials Exploring texture mapping and material libraries Module 3: Lighting and Shadows (2 hours) Understanding lighting principles and light placement Creating realistic shadows and reflections Module 4: Advanced Modeling Techniques (3 hours) Creating complex shapes and utilizing advanced tools Working with groups, components, and modifiers Module 5: Stable Diffusion Rendering (2 hours) Introduction to stable diffusion rendering Configuring rendering settings for optimal results Module 6: Scene Composition and Camera Setup (2 hours) Exploring composition principles and camera perspectives Managing scenes and creating walkthrough animations Module 7: Rendering Optimization (2 hours) Optimizing models for faster rendering Using render passes and post-processing techniques Module 8: Project Work and Portfolio Development (1 hour) Applying skills to complete a real-world project Showcasing work in a professional portfolio Optional: Installing Stable Diffusion and Python (Additional 10 hours) Module 1: Introduction to Stable Diffusion and Python Overview of Stable Diffusion and Python's significance Module 2: System Requirements Hardware and software prerequisites for installation Module 3: Installing Python Step-by-step installation process for different OS Module 4: Configuring Python Environment Setting up environment variables and package managers Module 5: Installing Stable Diffusion Downloading and installing the Stable Diffusion package Module 6: Setting Up Development Environment Configuring IDEs for Python and Stable Diffusion Module 7: Troubleshooting and Common Issues Identifying and resolving common installation errors Module 8: Best Practices and Recommendations Managing Python and Stable Diffusion installations Module 9: Practical Examples and Projects Hands-on exercises demonstrating usage of Stable Diffusion and Python Module 10: Advanced Topics (Optional) Exploring advanced features and techniques Stable Diffusion UI v2 | A simple 1-click way to install and use https://stable-diffusion-ui.github.io A simple 1-click way to install and use Stable Diffusion on your own computer. ... Get started by downloading the software and running the simple installer. Learning Outcomes: Upon completing the Sketchup and Stable Diffusion Rendering Course, with a focus on AI image rendering, participants will: Master AI Image Rendering: Gain expertise in using AI-powered rendering techniques to create realistic and high-quality visualizations. Utilize Sketchup for 3D Modeling: Navigate the software, proficiently use drawing tools, and create detailed 3D models. Optimize Renderings: Apply AI-based rendering to optimize model visuals, achieving faster rendering times and superior image quality. Implement AI-driven Lighting and Shadows: Utilize AI algorithms for lighting placement, shadows, and reflections, enhancing realism in renderings. Create Professional Portfolio: Showcase AI-rendered projects in a professional portfolio, highlighting advanced image rendering skills. Note: The course focuses on AI image rendering using Sketchup and Stable Diffusion techniques, empowering participants with cutting-edge skills for creating exceptional visual representations.

Who is this course for? Sketchup Artificial Intelligence Training Course. Mastering SketchUp Artificial Intelligence (AI) is essential for designers, offering automation, efficiency, and innovative solutions. It saves time, enhances visualizations, fosters collaboration, and future-proofs skills, ensuring a competitive edge in the design industry. Click here for more info: Website How to Book? 1-on-1 training. Customize your schedule from Mon to Sat from 9 am to 7 pm Call to book  Duration: 16 hours. Method: In-person or Live Online Sketchup and (Artificial Intelligence) Stable Diffusion Rendering Course (16 hours) Module 1: Sketchup Fundamentals (2 hours) Sketchup software overview and interface navigation Introduction to basic drawing tools and fundamental geometry creation techniques Module 2: Texturing and Material Mastery (2 hours) Application of textures and customization of materials Exploration of texture mapping and comprehensive material libraries Module 3: Illumination and Shadows (2 hours) Comprehending lighting principles and strategic light placement Crafting realistic shadows and reflections Module 4: Advanced Modeling Techniques (3 hours) Creating intricate shapes and harnessing advanced modeling tools Efficiently managing groups, components, and modifiers Module 5: Stable Diffusion Rendering (2 hours) Initiating stable diffusion rendering Optimizing rendering settings for superior outcomes Module 6: Scene Composition and Camera Configuration (2 hours) Exploring composition principles and camera perspectives Scene management and creation of captivating walkthrough animations Module 7: Rendering Optimization Strategies (2 hours) Techniques for optimizing models to expedite rendering Application of render passes and post-processing methods Module 8: Real-World Projects and Portfolio Building (1 hour) Application of acquired skills in completing authentic projects Professional portfolio presentation techniques Optional: Stable Diffusion and Python Installation (Additional 10 hours) Module 1: Introduction to Stable Diffusion and Python Comprehensive understanding of Stable Diffusion and Python's significance Module 2: System Prerequisites Hardware and software requirements for successful installation Module 3: Python Installation Guide Step-by-step installation process for various operating systems Module 4: Configuring Python Environment Configuration of environment variables and package managers Module 5: Stable Diffusion Installation Downloading and installing the Stable Diffusion package Module 6: Setting Up the Development Environment Configuration of integrated development environments (IDEs) for Python and Stable Diffusion Module 7: Troubleshooting and Common Issues Identification and resolution of common installation errors Module 8: Best Practices and Recommendations Effective management of Python and Stable Diffusion installations Module 9: Practical Applications and Projects Hands-on exercises exemplifying the practical usage of Stable Diffusion and Python Module 10: Advanced Topics (Optional) Exploration of advanced features and techniques Stable Diffusion https://stablediffusionweb.com https://stable-diffusion-ui.github.io https://stability.ai/stable-diffusion Upon successful completion of the Sketchup and Stable Diffusion Rendering Course with a focus on AI image rendering, participants will achieve the following: 1. Mastery of AI Image Rendering: Attain expertise in employing AI-powered rendering techniques to produce realistic and top-quality visualizations. 2. Proficiency in Sketchup for 3D Modeling: Navigate the software adeptly, utilize drawing tools with proficiency, and craft intricate 3D models. 3. Enhanced Rendering Optimization: Implement AI-based rendering to enhance model visuals, resulting in faster rendering times and superior image quality. 4. Application of AI-driven Lighting and Shadows: Employ AI algorithms for precise lighting placement, shadows, and reflections, elevating the realism of renderings. 5. Development of a Professional Portfolio: Present AI-rendered projects within a polished professional portfolio, highlighting advanced image rendering capabilities. 1. Mastering Sketchup: Attain proficiency in Sketchup, a renowned and user-friendly 3D modeling software, equipping you with the skills needed to adeptly create and manipulate 3D models. 2. Advanced Rendering Expertise: Explore stable diffusion rendering, an avant-garde technique that simplifies the creation of realistic and high-quality renderings. Broaden your rendering capabilities, producing visually stunning representations of your designs. 3. Practical Industry Applications: Cultivate practical skills relevant to diverse industries, encompassing architecture, interior design, product development, and visualization. Elevate your professional portfolio with captivating renderings that showcase your design prowess. 4. Interactive Learning: Participate in hands-on exercises and projects that promote active learning and the practical application of concepts. Benefit from personalized feedback and expert guidance, ensuring your continuous progress throughout the course. 5. Career Advancement: Elevate your career prospects by adding valuable skills to your toolkit. Proficiency in crafting detailed 3D models and impressive renderings through stable diffusion techniques opens doors to diverse job opportunities within the design and visualization sector. 6. Flexibility and Convenience: Access course materials online and learn at your own pace. Enjoy the flexibility of tailoring the coursework to your schedule, allowing you to harmonize your learning journey with other commitments. Course Advantages: Tailored Learning: Enjoy personalized 1-on-1 sessions, accommodating your schedule from Monday to Saturday, 9 am to 7 pm. Mastery of Sketchup: Develop proficiency in the widely-used and user-friendly 3D modeling software, enabling efficient creation and manipulation of 3D models. Advanced Rendering Proficiency: Acquire expertise in stable diffusion rendering for producing realistic, high-quality renderings that enhance the visual appeal of your designs. Practical Applicability: Develop practical skills applicable across diverse domains, including architecture, interior design, product development, and visualization, enriching your professional portfolio. Interactive Practical Experience: Engage in hands-on exercises with personalized guidance from seasoned instructors, ensuring consistent progress in your skillset. Career Progression: Boost your career opportunities by gaining valuable skills in 3D modeling and generating impressive renderings through stable diffusion techniques. Comprehensive Support: Benefit from free portfolio reviews, mock interviews, and career advice, providing additional resources to enhance your professional journey.

Overview Data and visual analytics are emerging fields concerned with analysing, modelling, and visualizing complex high-dimensional data. It can be analysed and visualised with many languages like Python, R Programming and more. This course will help to attain the skills and give in-depth knowledge to the participant's enhanced way of modelling, analysing and visualizing techniques.  The course will highlight practical challenges including composite real-world data and will also comprise several practical studies

This course presents an approach for dealing with security and privacy throughout the entire software development lifecycle. You will learn about vulnerabilities that undermine security, and how to identify and remediate them in your own projects.

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

For pupils aged 9 - 16 | Delivered in UK Schools by Real World App and Games Developers Our micro:bit Workshop teaches your class about the micro:bit, making some apps and games with them during the workshop (we bring our own micro:bits too if your school doesn't yet have any!). We’ll introduce them to MakeCode, the coding language that the micro:bit uses, and teach them the fundamentals of coding before we work on some really fun projects! For older students, we can even use Python with the micro:bit!

Regular expressions training course description Regular expressions are an extremely powerful tool for manipulating text and data. They are now standard features in a wide range of languages and popular tools, including Python and MySQL. Regular expressions allow you to code complex and subtle text processing that you never imagined could be automated. Once you've mastered regular expressions, they'll become an invaluable part of your toolkit. You will wonder how you ever got by without them. What will you learn Use Regular Expressions. Troubleshoot Regular Expressions. Compare RE features among different versions. Explain how the regular expression engine works. Optimize REs. Match what you want, not what you don't want. Regular expressions training course details Who will benefit: Anyone looking to use regular expressions. Prerequisites: None. Duration 1 day Regular expressions training course contents Introduction to Regular Expressions Solving real problems, REs as a language, the filename analogy, language analogy, RE frame of mind, searching text files: egrep, egrep metacharacters, start and end of the line, character classes, matching any character with dot, alternation, ignoring differences in capitalization, word boundaries, optional items, other quantifiers: repetition, parentheses and backreferences, the great escape, expanding the foundation, linguistic diversification, the goal of a RE, more examples, RE nomenclature, Improving on the status quo. Extended introductory examples A short introduction to Perl, matching text with regular expressions, toward a more real-world example, side effects of a successful match, Intertwined regular expression, intermission, modifying text with regular expressions, example: form letter, example: prettifying a stock price, automated editing, a small mail utility, adding commas to a number with lookaround, text-to-HTML conversion, that doubled-word thing. Regular expression features and flavours The regex landscape, origins of REs, care and handling of REs, Integrated handling, procedural and object-oriented handling, search-and-replace example. strings character encodings and modes, strings as REs, character-encoding issues, unicode, regex modes and match modes, common metacharacters and features, character representations, character classes and class-like constructs, anchors and other 'zero-width assertions', comments and mode modifiers, grouping capturing conditionals and control. The mechanics of expression processing Two kinds of engines, new standards, regex engine types, from the department of redundancy department, testing the engine type, match basics, about the examples, rule 1: the match that begins earliest wins, engine pieces and parts, rule 2: the standard quantifiers are greedy, regex-directed versus text-directed, NFA engine: regex-directed, DFA engine: text-directed, first thoughts: NFA and DFA in comparison, backtracking, two important points on backtracking, saved states, backtracking and greediness, more about greediness and backtracking, problems of greediness, multi-character 'quotes', lazy quantifiers, greediness and laziness, laziness and backtracking, possessive quantifiers and atomic grouping, possessive quantifiers ?, +, *+, ++ and {m,n}+, the backtracking of lookaround, is alternation greedy? taking advantage of ordered alternation, NFA DFA and posix, the longest-leftmost', posix and the longest-leftmost rule, speed and efficiency. Practical regex techniques Continuation lines, matching an IP address, working with filenames, matching balanced sets of parentheses, watching out for unwanted matches, matching delimited text, knowing your data and making assumptions, stripping leading and trailing whitespace, matching and HTML tag, matching an HTML link, examining an HTTP URL, validating a hostname, plucking a hostname, plucking a URL, parsing CSV files. Crafting an efficient expression Efficiency vs. correctness, localizing greediness, global view of backtracking, more work for POSIX NFA, work required during a non-match, being more specific, alternation can be expensive, benchmarking, know what you re measuring, benchmarking with Python, common optimisations, the mechanics of regex application, pre-application optimizations, optimizations with the transmission, optimization of the regex itself, techniques for faster expressions, common sense techniques, expose literal text, expose anchors, lazy versus greedy: be specific, split into multiple REs, mimic initial-character discrimination, use atomic grouping and possessive quantifiers, lead the engine to a match, unrolling the loop, observations, using atomic grouping and possessive quantifiers, short unrolling examples, unrolling C comments, the free flowing regex, a helping hand to guide the match, a well-guided regex is a fast regex.