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Duration 3 Days 18 CPD hours This course is intended for Programmers Application DevelopersSystem Architects Network ArchitectsNetwork Security Architects IT Professionals w/programming experience Overview Those who attend the Ethereum Development course and pass the exam certification will have a demonstrated knowledge of:An excellent overall understanding of the Ethereum architecture and Solidity language.All functional components (including smart contracts) required to develop an Ethereum Blockchain.The understanding of how to instantiate an Ethereum application on the network.An in-depth understanding of how transactions are created and implemented on an Ethereum network. This 3 day instructor-led course is designed for programmers and developers who want to take a comprehensive deep dive in writing smart contracts and building applications that interact with them. This course provides detailed overviews of Ethereum, smart contracts, and the development language, Solidity. The student will be exposed to Ethereum?s adaptable feature set which allows the developer to design decentralized applications for countless applications. Students will also participate in hands-on programming lab sessions to learn, develop, and advance their skills in Ethereum development. The Ethereum Development Training Course is designed for those seeking an in-depth understanding and development experience of the Ethereum Blockchain platform. Students will participate in approximately 50% programming lab time providing practical experience, enhancing their knowledge and existing skill set. What is Blockchain and Smart Contracts? What is Blockchain and how does it work? Bitcoin vs Ethereum Smart Contract How you can use Smart Contracts? Advantages of Smart Contracts Solidity LAB TASK Types of Variables in Solidity Public and Private Code Public Variables and Functions Private Variables and Functions Internal Functions External Functions Smart Contract Constructors Constant Variables Setting Variables LAB CHALLENGE Smart Contracts With WEB3.JS LAB Installing & Running the Ethereum TestRPC Installing Web3.js Changing the Environment in Remix Creating the UI Using Web3.js to Connect & Interact with the Smart Contract SOLUTION LAB CHALLENGE Smart Contract Events With WEB3.JS AIM LAB The Current Contract Defining the Smart Contract Event Updating the UI LAB CHALLENGE Functions, Mappings and Structs THEORY Functions Mappings Structs LAB The Smart Contract Creating a Modifier Using the Modifier Web3 UI?s Modifier Handling LAB Creating a Struct Creating the Mapping Map Addition Get from the Mapping The Full Contract Count from Map LAB CHALLENGE Inheritance and Deployment THEORY Object-oriented programming Inheritance LAB Current Contract Creating a Base Contract Changing from Strings to bytes Continuing the Project Installing MetaMask Deploy Contract to the Ropsten Test Network LAB Update the HTML Form Update the JavaScript Using the App LAB CHALLENGE Embark Framework and It's Deployment Blockchain (Ethereum) Decentralized Storage (IPFS) Decentralized Communication Web Technologies LAB Installing Embark Framework Hello World with Embark Framework First Contract Deployment with Embark Framework Solidity Smart Contracts Testing THEORY What is Software Testing? LAB LAB CHALLENGE Contracts Management With Factories THEORY Factory LAB LAB CHALLENGE IPFS Files Hosting THEORY IPFS FILES HOSTING LAB 1. Get your hosting server 2. Install IPFS on the Server LAB CHALLENGE End to End Development of DAPP AIM LAB Setup the Project Program the Smart Contract Create the Front-end of the application Deploy the App with IPFS LAB CHALLENGE

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.