89 Algorithm courses in Glasgow delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise. While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos. Overview This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore: Exploring AI Introduction to Machine Learning Introduction to Deep Learning Introduction to Data Science Artificial Intelligence (AI) in Software Testing Implementing AI in Test Automation Innovative AI Test Automation Tools for the Future Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements. Exploring AI AI-Initiatives The Priority: Excellence AI- Intelligence Types The Machine Learning Types The Quality Learning Initiative The Inception in Academics AI - Importance & Applications The Re-visit Learning Re-visited via AI Teaching in the world of AI Exploring AI for Self-Development AI In Academics Beyond Academics Introduction to Machine Learning What is Machine Learning? Why Machine Learning? Examples - Algorithms behind Machine Learning Introduction to Deep Learning What is Deep Learning? Why Deep Learning? Example - Deep Learning Vs Machine Learning Introduction to Data Science What is Data Science? Why Data Science? Examples - Use Cases of Data Science Artificial Intelligence (AI) in Software Testing What is AI in Software Testing? The Role of AI Testing Why do we Need AI in Software Testing? Pros and Cons of AI in Software Testing Applications of AI in Software Testing Is it time for Testers or QA Teams to worry about AI? Automated Testing with Artificial Intelligence Implementing AI in Test Automation Training the AI Bots Challenges with AI-powered Applications Examples - Real World use cases using Artificial Intelligence Demo - Facial Emotion Detection Using Artificial Intelligence Demo - Text Analysis API Using Artificial Intelligence Demo - EYE SPY Mobile App Using Artificial Intelligence Innovative AI Test Automation Tools for the Future Tools used for Implementing AI in Automation Testing What is NEXT? AI Test Automation Demo using Testim

Duration 3 Days 18 CPD hours This course is intended for Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek an understanding of the parallel framework architecture. In this course, students will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. Introduction to the Parallel Framework Architecture Describe the parallel processing architecture Describe pipeline and partition parallelism Describe the role of the configuration file Design a job that creates robust test data Compiling & Executing Jobs Describe the main parts of the configuration file Describe the compile process and the OSH that the compilation process generates Describe the role and the main parts of the Score Describe the job execution process Partitioning & Collecting Data Understand how partitioning works in the Framework Viewing partitioners in the Score Selecting partitioning algorithms Generate sequences of numbers (surrogate keys) in a partitioned, parallel environment Sorting Data Sort data in the parallel framework Find inserted sorts in the Score Reduce the number of inserted sorts Optimize Fork-Join jobs Use Sort stages to determine the last row in a group Describe sort key and partitioner key logic in the parallel framework Buffering in Parallel Jobs Describe how buffering works in parallel jobs Tune buffers in parallel jobs Avoid buffer contentions Parallel Framework Data Types Describe virtual data sets Describe schemas Describe data type mappings and conversions Describe how external data is processed Handle nulls Work with complex data Reusable Components Create a schema file Read a sequential file using a schema Describe Runtime Column Propagation (RCP) Enable and disable RCP Create and use shared containers Balanced Optimization Enable Balanced Optimization functionality in Designer Describe the Balanced Optimization workflow List the different Balanced Optimization options. Push stage processing to a data source Push stage processing to a data target Optimize a job accessing Hadoop HDFS file system Understand the limitations of Balanced Optimizations Additional course details: Nexus Humans KM404 IBM InfoSphere Advanced DataStage - Parallel Framework (v11.5) 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 KM404 IBM InfoSphere Advanced DataStage - Parallel Framework (v11.5) 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 3 Days 18 CPD hours This course is intended for Experienced system administrators or network administrators, Network professionals who have experience working with VMware NSX Advanced Load Balancer and are responsible for designing or deploying Application Delivery Controllers solutions Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer components and main functions Describe NSX Advanced Load Balancer Global Server Load Balancing architecture Explain NSX Advanced Load Balancer key features and benefits Understand and apply a Global Server Load Balancing design framework Deploy and configure NSX Advanced Load Balancer Global Server Load Balancing infrastructure Explain and Configure Global Server Load Balancing Application components such as Global Server Load Balancing Service, Global Server Load Balancing Pools and Health Monitors with related components Gather relevant information and perform basic troubleshooting of Global Server Load Balancing applications leveraging built-in NSX Advanced Load Balancer tooling Describe and Configure NSX Advanced Load Balancer application and infrastructure monitoring This 3-day course prepares you to lead VMware NSX Advanced Load Balancer (Avi Networks) Global Server Load Balancing design and deployment projects by providing an understanding of general design processes, frameworks and configurations. You look at the design and deployment considerations for Global Server Load Balancing as part of an overall software-defined data center design. This course covers key NSX Advanced Load Balancer (Avi Networks) Global Server Load Balancing features and functionalities offered in the NSX Advanced Load Balancer 18.2 release. Access to a software-defined data center environment is provided through hands-on labs to reinforce the skills and concepts presented in the course. Course Introduction Introductions and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic virtual services components such as Application Profiles, Network Profiles, Pools and Health Monitors DNS Foundations Review, discuss and explain DNS fundamentals Describe NSX Advanced Load Balancer DNS and IPAM providers Global Server Load Balancing Introduce Global Server Load Balancing concepts and benefits Explain and configure NSX Advanced Load Balancer infrastructure Explain and configure DNS Virtual Service components Explain and configure GSLB Service Engine Group Describe and configure GSLB Sites Explain and configure basic GSLB Services, to include pools and health monitors Describe GSLB Service Load Balancing algorithms Explain and configure Data and Control Plane-based Health Monitors Describe GSLB Health Monitor Proxy Global Server Load Balancing Advanced Topics Explain and configure advanced GSLB service properties such as different type of pool members, Host Header and TLS SNI extensions handling within GSLB Health Monitors Describe EDNS Client Subnet Describe Geo-aware Global Server Load Balancing Design and configure Geo-aware Global Server Load Balancing Describe and leverage DNS Policies to customize client experience Explain and configure Topology-aware Global Server Load Balancing Explain and configure GSLB 3rd party sites Describe GSLB Health Monitor sharding Describe GSLB Service Engine sizing implications Troubleshooting NSX Advanced Load Balancer GSLB Solution Introduce Infrastructure and Application troubleshooting Concepts Describe Control Plane and Data Plane-based troubleshooting Describe GSLB Infrastructure troubleshooting Describe GSLB Services troubleshooting Explain Health Monitors troubleshooting Describe Geo-aware and Topology-based GSLB Services troubleshooting Explain Application Analytics and Logs Describe Client Logs analysis Leverage CLI for advanced data plane troubleshooting Monitoring NSX Advanced Load Balancer Solution Describe NSX Advanced Load Balancer Events Describe and configure NSX Advanced Load Balancer Alerts Describe NSX Advanced Load Balancer monitoring capabilities leveraging SNMP, Syslog and Email

Duration 5 Days 30 CPD hours This course is intended for This basic course is intended for anyone who is new to software development and wants, or needs, to gain an understanding of the fundamentals of coding and basics of Java and object-oriented programming concepts. Attendees might include: Technically-minded attendees who want or who want to begin the process of becoming an OO application developer Technical team members from non-development roles, re-skilling to move into software and application development roles within an organization Recent college graduates looking to apply their college experience to programming skills in a professional environment, or perhaps needing to learn the best practices and standards for programming within their new organization Technical managers tasked with overseeing programming teams, or development projects, where basic coding knowledge and exposure will be useful in project oversight or communications needs Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in basic coding with Java, coupling the most current, effective techniques with the soundest industry practices. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn: The steps involved in the creation and deployment of a computer program What OO programming is and what the advantages of OO are in today's world To work with objects, classes, and OO implementations The basic concepts of OO such as encapsulation, inheritance, polymorphism, and abstraction The basic constructs that all programming languages share The basic Java constructs supporting processing as well as the OO orientation How to use Java exception handling About and how to use classes, inheritance and polymorphism About use collections, generics, autoboxing, and enumerations How to take advantage of the Java tooling that is available with the programming environment being used in the class Getting Started with Programming, OO and Java Basics for Non-Developers is a skills-focused, hands-on coding course that teaches students the fundamentals of programming object oriented (OO) applications with Java to a basic level, using sound coding skills and best practices for OO development. This course is presented in a way that enables interested students to embrace the fundamentals of coding as well as an introduction to Java, in a gentle paced environment that focuses on coding basics.Students are introduced to the application development cycle, structure of programs, and specific language syntax. The course introduces important algorithmic constructs, string and character manipulation, dynamic memory allocation, standard I/O, and fundamental object-oriented programming concepts. The course explains the use of inheritance and polymorphism early on so the students can practice extensively in the hands-on labs. Structured programming techniques and error handling are emphasized. The course includes the processing of command line arguments and environment variables, so students will be able to write flexible, user-friendly programs. Students will leave this course armed with the required skills to begin their journey as a Java programmer using modern coding skills and technologies. Introduction to Computer Programming Introduction to Programming Programming Tools Programming Fundamentals Thinking About Objects Program Basics Programming Constructs Java: A First Look The Java Platform Using the JDK The Eclipse Paradigm Writing a Simple Class OO Concepts Object-Oriented Programming Inheritance, Abstraction, and Polymorphism Getting Started with Java Adding Methods to the Class Language Statements Using Strings Specializing in a Subclass Essential Java Programming Fields and Variables Using Arrays Java Packages and Visibility Advanced Java Programming Inheritance and Polymorphism Interfaces and Abstract Classes Exceptions Java Developer's Toolbox Utility Classes Enumerations and Static Imports Formatting Strings Collections and Generics Introduction to Generics Collections

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Building Recommendation Systems with Python (TTAI2360) 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 Building Recommendation Systems with Python (TTAI2360) 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 Blockchain Architects Blockchain DevelopersApplication Developers Blockchain System AdministratorsNetwork Security Architects Cyber Security ExpertsIT Professionals w/cyber security experience Overview Those who attend the Security for Blockchain Professionals course and pass the exam certification will have a demonstrated knowledge of:Identifying and differentiating between security threats and attacks on a Blockchain network.Blockchain security methods, best practices, risk mitigation, and more.All known (to date) cyber-attack vectors on the Blockchain.Performing Blockchain network security risk analysis.A complete understanding of Blockchain?s inherent security features and risks.An excellent knowledge of best security practices for Blockchain System/Network Administrators.Demonstrating appropriate Blockchain data safeguarding techniques. This course covers all known aspects of Blockchain security that exist in the Blockchain environment today and provides a detailed overview of all Blockchain security issues, including threats, risk mitigation, node security integrity, confidentiality, best security practices, advanced Blockchain security and more. Fundamental Blockchain Security Cryptography for the Blockchain Hash Functions Public Key Cryptography Elliptic Curve Cryptography A Brief Introduction to Blockchain The Blocks The Chains The Network Promises of the Blockchain Blockchain Security Assumptions Digital Signature Security Hash Function Security Limitations of Basic Blockchain Security Public Key Cryptography Review Real-Life Public Key Protection Cryptography and Quantum Computers Lab 1 (Tentative) Finding Hash Function Collisions Reversible hash function Hash function with poor non-locality Hash function with small search space Breaking Public Key Cryptography Brute Forcing a Short Private Key Brute Forcing a Poorly-Chosen Private Key Consensus in the Blockchain Blockchain Consensus and Byzantine Generals Blockchain Networking Review Byzantine Generals Problem Relation to Blockchain Byzantine Fault Tolerance Introduction to Blockchain Consensus Security Blockchain Consensus Breakthrough Proof of Work What is Proof of Work? How does Proof of Work Solve BGP? Proof of Work Security Assumptions Attacking Proof of Work Proof of Stake What is Proof of Stake? How does Proof of Stake Solve BGP? Proof of Stake Security Assumptions Attacking Proof of Stake General Attacks on Blockchain Consensus Other Blockchain Consensus Algorithms Lab 2 (Tentative) Attacking Proof of Work Performing a 51% Attack Performing a Selfish Mining Attack Attacking Proof of Stake Performing a XX% Attack Performing a Long-Range Attack Malleable Transaction Attacks Advanced Blockchain Security Mechanisms Architectural Security Measures Permissioned Blockchains Checkpointing Advanced Cryptographic Solutions Multiparty Signatures Zero-Knowledge Proofs Stealth Addresses Ring Signatures Confidential Transactions Lab 3 (Tentative) Permissioned Blockchains 51% on a Checkpointed Blockchain Data mining on a blockchain with/without stealth addresses Zero-Knowledge Proof Simulation Trying to fake knowledge of a ZKP Module 4: Blockchain for Business Introduction to Ethereum Security What is Ethereum Consensus in Ethereum Smart Contracts in Ethereum Ethereum Security Pros and Cons of Ethereum Blockchains Introduction to Hyperledger Security What is Hyperledger Consensus in Hyperledger Smart Contracts in Hyperledger Hyperledger Security Pros and Cons of Hyperledger Blockchains Introduction to Corda Security What is Corda Consensus in Corda Smart Contracts in Corda Corda Security Pros and Cons of Corda Blockchains Lab 4 Blockchain Risk Assessment What are the Risks of the Blockchain? Information Security Information Sensitivity Data being placed on blockchain Risks of disclosure Regulatory Requirements Data encryption Data control PII protection Blockchain Architectural Design Public and Private Blockchains Open and Permissioned Blockchains Choosing a Blockchain Architecture Lab 5 Exploring public/private open/permissioned blockchains? Basic Blockchain Security Blockchain Architecture User Security Protecting Private Keys Malware Update Node Security Configuring MSPs Network Security Lab 6 (TBD) Smart Contract Security Introduction to Smart Contracts Smart Contract Security Considerations Turing-Complete Lifetime External Software Smart Contract Code Auditing Difficulties Techniques Tools Lab 7 (Tentative) Try a couple of smart contract code auditing tool against different contracts with built-in vulnerabilities Module 8: Security Implementing Business Blockchains Ethereum Best Practices Hyperledger Best Practices Corda Best Practices Lab 8 Network-Level Vulnerabilities and Attacks Introduction to Blockchain Network Attacks 51% Attacks Denial of Service Attacks Eclipse Attacks Routing Attacks Sybil Attacks Lab 9 Perform different network-level attacks System-Level Vulnerabilities and Attacks Introduction to Blockchain System Vulnerabilities The Bitcoin Hack The Verge Hack The EOS Vulnerability Lab 10 Smart Contract Vulnerabilities and Attacks Introduction to Common Smart Contract Vulnerabilities Reentrancy Access Control Arithmetic Unchecked Return Values Denial of Service Bad Randomness Race Conditions Timestamp Dependence Short Addresses Lab 11 Exploiting vulnerable smart contracts Security of Alternative DLT Architectures What Are Alternative DLT Architectures? Introduction to Directed Acyclic Graphs (DAGs) DAGs vs. Blockchains Advantages of DAGs DAG Vulnerabilities and Security Lab 12 Exploring a DAG network

Duration 1 Days 6 CPD hours This course is intended for To gain the most from attending this course you should possess the following incoming skills: Basic knowledge of programming concepts and syntax in Python. Familiarity with common data formats such as CSV, JSON, and XML. Experience using command-line interfaces and basic text editing tools. Understanding of basic machine learning concepts and algorithms. Overview Working in an interactive learning environment, led by our engaging expert, you will: Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions. Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4. Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements. Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases. Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions. Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex. Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development. Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts. Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment. Introduction to Prompt Engineering Overview of prompt engineering and its importance in AI applications Major applications of prompt engineering in business Common challenges faced in prompt engineering Overview of GPT-4 and its role in prompt engineering Key terminology and concepts in prompt engineering Getting Things Ready: Text Preprocessing and Data Cleansing Importance of data preprocessing in prompt engineering Techniques for text cleaning and normalization Tokenization and n-grams Stop word removal and stemming Regular expressions and pattern matching GPT-4 Tokenization and Input Formatting GPT-4 tokenization and its role in prompt engineering Understanding and formatting GPT-4 inputs Context windows and token limits Controlling response length and quality Techniques for handling out-of-vocabulary tokens Prompt Design and Optimization Master the skills to design, optimize, and test prompts for various business tasks. Designing effective prompts for different tasks Techniques for prompt optimization GPT-4 system and user parameters for controlling behavior Importance of prompt testing and iteration Best practices for prompt engineering in business applications Advanced Techniques and Tools in Prompt Engineering Learn advanced techniques and tools for prompt engineering and their integration in business applications. Conditional text generation with GPT-4 Techniques for handling multi-turn conversations Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground Integration of GPT-4 with other software platforms and tools Monitoring and maintaining prompt performance Code Generation and Testing with Prompt Engineering Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects. Introduction to code generation with AI models like GPT-4 Designing prompts for code generation across programming languages Techniques for specifying requirements and constraints in prompts Generating and interpreting code snippets using AI-driven solutions Integrating generated code into existing projects and codebases Best practices for testing and validating AI-generated code Ethics and Responsible AI Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business. Ethical considerations in prompt engineering Bias in AI systems and its impact on prompt engineering Techniques to minimize bias and ensure fairness Best practices for responsible AI deployment in business applications Monitoring and addressing ethical concerns in prompt engineering

Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators or network administrators, software and DevOps engineers Overview By the end of the course, you should be able to meet the following objectives: Describe VMware NSX Advanced Load Balancer architecture Describe VMware NSX Advanced Load Balancer components and main functions Explain VMware NSX Advanced Load Balancer key features and benefits Describe and leverage VMware NSX Advanced Load Balancer REST API Describe and leverage VMware NSX Advanced Load Balancer SDKs with extended focus on Python SDK Leverage REST API and SDK features and functions to provision application delivery components Describe and leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations Describe and leverage VMware NSX Advanced Load Balancer Github, Docker (avinetworks/avitools), Ansible Galaxy and other open source resources to accelerate the automation planning and implementation Leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations to provision infrastructure components Leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations to automate and streamline application delivery services provisioning This three-day, fast-paced course provides comprehensive training on how to automate infrastructure and application components of VMware NSX Advanced Load Balancer (Avi Networks) solution. This course covers key application delivery features of NSX Advanced Load Balancer (Avi Networks) features and functionality offered in VMware NSX Advanced Load Balancer 18.2 release and focuses on how to plan and implement automation of infrastructure and application components leveraging REST API, SDK or automation solutions such as Ansible, Terraform or similar. Access to a software-defined data center environment is provided through hands-on labs to reinforce the skills and concepts presented in the course. Course Introduction Introductions and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases, and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic virtual services components such as Application Profiles, Network Profiles,Pools and Health Monitors Pools Configuration Concepts Explain and deep dive on Pool configuration options Describe multiple load balancing algorithms Explain multiple Health Monitor types Explain multiple Persistent profiles Explain and configure Pool Groups Leveraging NSX Advanced Load Balancer REST API Explain NSX Advanced Load Balancer automation vision Explain and introduce NSX Advanced Load Balancer REST API Describe NSX Advanced Load Balancer REST API methods and capabilities Describe NSX Advanced Load Balancer REST API session handling properties such authentication, API versioning and tenancy model Deep dive on NSX Advanced Load Balancer REST API Object Model Explain and investigate NSX Advanced Load Balancer REST API leveraging browser and command line utilities Explain and interact with NSX Advanced Load Balancer REST API leveraging browser, Postman and Curl Explain Swagger-based API Documentation Explain and leverage NSX Advanced Load Balancer Inventory API Explain and leverage NSX Advanced Load Balancer methods such as GET, PUT, POST and PATCH and associated queries, filters and parameters Deep dive on NSX Advanced Load Balancer PATCH method Explain and leverage NSX Advanced Load Balancer Analytics API Explain and leverage NSX Advanced Load Balancer MACRO API NSX Advanced Load Balancer Software-Defined Kits (SDKs) and ControlScripts Introduce NSX Advanced Load Balancer SDKs Describe, install and leverage NSX Advanced Load Balancer Python SDK Deep dive on NSX Advanced Load Balancer Python SDK Describe and leverage Golang SDK Leverage NSX Advanced Load Balancer open source resources such as Github, etc to accelerate SDKs adoption Describe NSX Advanced Load Balancer Events and Alerts framework Introduce ControlScripts foundations Leverage ControlScripts to automate configuration changes and alerts remediation Automating NSX Advanced Load Balancer Application Delivery Services with Ansible and Terraform Introduce NSX Advanced Load Balancer Configuration Orchestration and Management vision Introduce and explain Ansible foundations Describe Ansible and NSX Advanced Load Balancer Ansible capabilities Deep dive and implement NSX Advanced Load Balancer Ansible Core configuration modules (avinetworks/avisdk) Deep dive and implement Ansible NSX Advanced Load Balancer Declarative configuration role (avinetworks/aviconfig) Leverage Swagger NSX Advanced Load Balancer REST API models to develop and implement Ansible playbooks Explain application delivery configuration automation approach and models Apply configuration automation models with Ansible Introduce and explain Terraform foundations Describe Terraform and NSX Advanced Load Balancer Terraform capabilities Deep dive and implement NSX Advanced Load Balancer Terraform Provider Leverage Swagger NSX Advanced Load Balancer REST API models to develop and implement Terraform plans Apply configuration automation models with Terraform Automating NSX Advanced Load Balancer Infrastructure with Ansible and Terraform Introduce NSX Advanced Load Balancer infrastructure Automation vision Describe infrastructure deployment approach and capabilities Describe Ansible and NSX Advanced Load Balancer Ansible Infrastructure deployment approach and capabilities Describe Terraform and NSX Advanced Load Balancer Terraform deployment approach and capabilities Leverage Terraform to deploy Controllers and perform system configuration, including control plane cluster setup Leverage Terraform to provision Cloud, Service Engine Groups and Service Engine components Describe and leverage Ansible roles to deploy Controllers and perform initial system configuration, including control plane cluster setup Leverage Ansible declarative and core roles to provision Cloud, Service Engine Groups and Service Engine components Describe and implement combined Terraform + Ansible model to streamline NSX Advanced Load Balancer solution deployment