2985 Code courses

This is a complete crash course about KNIME for beginners. Here, we will learn how to do data cleaning and data preparation without any code, using KNIME. We will also cover data visualization using Tableau and Power BI Desktop. Then we will understand the predictive analytics capabilities of KNIME and finally, cover machine learning in KNIME.

Learning Disability Nursing with SEND and Dyslexia Diploma Enhance your expertise in Learning Disability Nursing with our comprehensive Diploma course. Learn specialised approaches in Learning Disability Nursing. Navigate the laws of SEND and Dyslexia with ease. Learning Outcomes: Master key practices in Learning Disability Nursing. Apply legal frameworks to Learning Disability Nursing. Identify risk factors related to Dyslexia. Interpret the SEND Code of Practice in Nursing contexts. Distinguish between different categories of Dyslexia. More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Key Modules from Learning Disability Nursing with SEND and Dyslexia Diploma: Learning Disability Nursing: Acquire foundational knowledge and practical skills in Learning Disability Nursing. Approaches in Learning Disability Nursing: Discover evidence-based approaches in Learning Disability Nursing for optimal patient care. The Law and SEND: Master the legal frameworks guiding Learning Disability Nursing, particularly in relation to SEND. The SEND Code of Practice: Apply the SEND Code of Practice effectively within Learning Disability Nursing contexts. Categories of Dyslexia: Distinguish between various categories of Dyslexia and their implications in Learning Disability Nursing. Dyslexia Risk Factors: Identify and manage risk factors associated with Dyslexia within the scope of Learning Disability Nursing.

Salad Growing Course at Trill Farm Garden: learn how to produce salad consistently through the year

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Welcome to a brand-new course where you can learn about how to build a personal portfolio website from scratch with only three core technologies-HTML, CSS, and JS-and host the website and see it go live.

Define multimode system terminology Describe goals and applications of multimode systems Detail basic component layouts of multimode systems Define microgrid systems and diagram component layouts for microgrid applications List applications for multimode systems Distinguish between back-up and self-consumption use cases Examine daily and annual data to perform a load analysis Review battery bank sizing Identify PV array sizing methods and variables for multimode systems Calculate minimum PV array size to meet load requirements Calculate what percentage of overall annual consumption will be offset by selected PV array size Analyze data required to specify a multimode inverter Differentiate between sizing considerations for internal and external AC connections Describe various configurations for stacking and clustering multiple inverters Describe when and why advanced inverter functions are used Discuss the equipment and designs needed for advanced multimode functions Analyze each advanced multimode function List data needed to perform an accurate financial analysis of systems that use advanced multimode functions Describe factors that can affect the financial analysis of systems using advanced multimode functions Describe the National Electrical Code (NEC®) Articles that apply to the different parts of PV and energy storage systems (ESS) Identify specific requirements for ESS and systems interconnected with a primary power source List relevant building & fire codes Communicate specific requirements for workspace clearances, disconnects, & OCPD Describe PV system requirements that affect ESS installation List ESS labeling requirements Review DC coupled systems, including advantages and disadvantages Discuss MPPT charge controller operations and options Review charge controller sizing for grid-tied systems Design a DC coupled multimode PV system for a residential application Define operating modes of an AC coupled PV system while grid-connected or in island mode Explain charge regulation methods of grid-direct inverter output Review AC coupled PV system design strategies Evaluate equipment options for AC coupled multimode applications Design an AC coupled multimode PV system for a residential application Define Energy Storage System (ESS) Describe criteria for evaluating energy storage system configurations and applications Design ESS system for back-up power Describe large-scale energy storage system applications and functions; review use case examples Analyze equipment configuration options for large-scale AC and DC coupled systems Formulate questions to enable design optimization of large-scale energy storage systems Note: SEI recommends working closely with a qualified person and/or taking PV 202 for more information on conductor sizing, electrical panel specification, and grounding systems. These topics will be part of this course, but they are not the focus.

In this course, we'll learn about runtime semantics and build an interpreter for a programming language from scratch. In the process, we'll build and understand a full programming language semantics.

Unleashing the Power of Deep Learning: Mastering Neural Network with R Dive into the fascinating realm of artificial intelligence with our course, 'Deep Learning Neural Network with R.' Imagine a world where machines learn and make decisions, mimicking the intricacies of the human brain. This course is your gateway to unlocking the secrets of deep learning, focusing on neural networks implemented using the versatile R programming language. Immerse yourself in hands-on projects, from creating single-layer neural networks for agriculture analysis to mastering multi-layer neural networks for predicting deaths in wars. The journey begins with reviewing datasets and creating dataframes, leading you through running neural network code and generating insightful output plots. Join us in this captivating exploration, where coding meets creativity, and algorithms come to life. Learning Outcomes Master the fundamentals of single-layer neural networks, gaining the skills to analyze agricultural datasets effectively. Acquire proficiency in implementing multi-layer neural networks, specifically tailored for predicting outcomes in complex scenarios like deaths in wars. Develop hands-on experience in creating and manipulating dataframes for enhanced data analysis. Gain a deep understanding of neural network syntax, commands, and code execution in the R programming language. Hone your ability to generate meaningful output plots, transforming raw data into visually compelling insights. Why choose this Deep Learning Neural Network with R course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Deep Learning Neural Network with R course for? Aspiring data scientists and analysts eager to delve into the world of deep learning. R programming enthusiasts looking to enhance their skills with practical applications. Students and professionals in computer science, statistics, or related fields. Individuals seeking to understand the implementation of neural networks in real-world scenarios. Anyone fascinated by the intersection of coding, data analysis, and artificial intelligence. Career path Machine Learning Engineer: £40,000 - £70,000 Data Scientist: £35,000 - £60,000 Artificial Intelligence Researcher: £45,000 - £80,000 Research Scientist (Machine Learning): £50,000 - £90,000 Data Analyst (AI/ML): £30,000 - £55,000 Senior AI Developer: £60,000 - £100,000 Prerequisites This Deep Learning Neural Network with R does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Deep Learning Neural Network with R was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Section 01: Single Layer Neural Networks Project - Agriculture (Part - 1) Reviewing Dataset 00:14:00 Creating Dataframes 00:09:00 Generating Output 00:12:00 Section 02: Single Layer Neural Networks Project - Agriculture (Part - 2) Running Neural Network Code 00:11:00 Importing Dataset 00:09:00 Neural Network Plots for Hidden Layer 1 00:08:00 Section 03: Multi-Layer Neural Networks Project - Deaths in wars (Part - 1) Syntax and Commands for MLP 00:11:00 Running the Code 00:08:00 Testing for Dataframes 00:13:00 Predict Results 00:08:00 Section 04: Multi-Layer Neural Networks Project - Deaths in wars (Part - 2) Creating R Folder 00:14:00 Generating Output Plot 00:12:00 Testing and Predicting the Outputs 00:16:00

Welcome to this course on SwiftUI animations iOS 16. This is a fun-to-code course with multiple hands-on projects geared toward various skill levels. Each project is marked 'Easy', 'Intermediate', or 'Advanced', allowing you to start coding projects according to your skill level and gradually move on to the higher levels when ready.

This course offers everything you need to become a React developer, from basic to advanced concepts. The course delves deep into custom hooks, Tailwind CSS, React Router, Redux, Firebase, and React Skeleton. You will learn to build real-world apps with React (eCommerce, Movie Informer, Todolist Manager, Blog, and Word Counter).