1001 Python courses

Sharpen your HTML, CSS, and JavaScript by working on a large variety of projects. In this course, we'll be working through a project a day using vanilla JavaScript. Over the next 50 days, you'll have 50 small, unique, DOM-oriented projects under your belt.

This course is designed for complete beginners, where you will develop iPhone applications by building five complete apps using SwiftUI 3 code and Xcode 13. You will not only build the apps but also learn how to submit and upload apps to the App Store and share your creation with the world.

Duration 5 Days 30 CPD hours This course is intended for Experienced VMware administrators, automation and orchestration specialists, system integrators, and private cloud and public cloud administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the features and benefits of integrating vRealize Orchestrator and vRealize Automation Describe the role of vRealize Orchestrator workflows and content elements in automation Use the vRealize Orchestrator client to access and navigate the vRealize Orchestrator platform Use the vRealize Orchestrator client to import and run vRealize Orchestrator library workflows Design, develop, and run custom reusable vRealize Orchestrator workflows Integrate vRealize Automation with vRealize Orchestrator to deliver custom IT services Use the vRealize Automation event broker service to trigger specific vRealize Orchestrator workflows or ABX Actions Leverage the event broker to extend IaaS (Infrastructure-as-a-Service) machine lifecycle processes Use XaaS to extend vRealize Automation into other enterprise systems Use VMware APIs to run vRealize Orchestrator workflows Use the vSphere Client Code Capture feature During this five-day course, you focus on using VMware vRealize© Orchestrator? to extend the functionality of VMware vRealize© Automation?. You learn how to provide XaaS (Anything as a Service) and implement Machine Lifecycle Extensibility using the VMware vRealize© Automation? Event Broker. You also learn how to create vRealize Orchestrator workflows and vRealize Automation ABX actions. You learn about various features, including basic scripting implementation along with logic processing to implement a variety of functions to use in your environment. This course teaches implementing debugging, loops, conditions, and user interactions in vRealize Orchestrator. The course introduces the new vRealize Orchestrator HTML 5 interface, along with API calls and REST functions, to give you the groundwork to implement a variety of plugins and scripts. This course is designed to give you the tools to craft custom solutions in the product. COURSE INTRODUCTION * Introductions and course logistics * Course objectives OVERVIEW OF VREALIZE AUTOMATION AND VREALIZE ORCHESTRATOR * Define the purpose of vRealize Automation * Outline the purpose of vRealize Orchestrator * Describe the main components of vRealize Automation * Describe the main components of vRealize Orchestrator CREATING SCHEMA ELEMENTS * Invoking JavaScript from a vRealize Orchestrator workflow * Invoking a vRealize Orchestrator Workflow from a vRealize Orchestrator workflow * Invoking an action from a vRealize Orchestrator workflow * Using vRealize Orchestrator workflows both synchronously and asynchronously WORKING WITH VARIABLES * Defining inputs, outputs, and variables in vRealize Orchestrator workflows * Binding variables in vRealize Orchestrator workflows * Wrapping vRealize Orchestrator workflows * Using APIs and the API Explorer * Creating actions in vRealize Orchestrator * Using vRealize Orchestrator input forms * Handling user interactions in vRealize Orchestrator HANDLING EXCEPTIONS, LOGGING, AND DEBUGGING * Handling exceptions in vRealize Orchestrator workflows * Using logs in vRealize Orchestrator workflows * Debugging vRealize Orchestrator workflows BRANCHING AND LOOPING * Using branching in vRealize Orchestrator workflows * Using loops in vRealize Orchestrator workflows WORKING WITH ASSETS * Using configuration elements in vRealize Orchestrator * Using resources in vRealize Orchestrator * Using packages in vRealize Orchestrator WORKING WITH PLUG-INS * Downloading and installing Plug-Ins * Using the SSH plug-In in vRealize Orchestrator * Using the REST plug-in in vRealize Orchestrator * Using the vRealize Automation plug-in in vRealize Orchestrator * Using the PowerShell plug-in in vRealize Orchestrator WORKING WITH VERSIONING AND GIT * Using versioning in vRealize Orchestrator * Using Git in vRealize Orchestrator SCHEDULING, SLEEPING, AND WAITING * Using scheduling in vRealize Orchestrator * Using sleeping in vRealize Orchestrator * Using waiting in vRealize Orchestrator INTRODUCTION TO VREALIZE AUTOMATION EXTENSIBILITY * Introduction to extensibility * Using ABX actions * Using Python * Using Nodejs * Using PowerShell * Using vRealize Automation Lifecycle EXTENDING VREALIZE AUTOMATION WITH EVENT BROKER * Overview of vRealize Automation Event Broker * Creating vRealize Automation subscriptions * Data exchange between vRealize Automation and vRealize Orchestrator USING ABX ACTIONS * Overview of Action Based Extensibility (ABX) * Comparison of vRealize Orchestrator and ABX * Creating ABX Actions scripts, REST, and flows * Using Day-2 Actions in vRealize Automation * Describe the visualization capabilities of NSX Network Detection and Response WORKING WITH SERVICES, CUSTOM RESOURCES, AND RESOURCE ACTIONS * Using vRealize Orchestrator as a content source in vRealize Automation * Creating custom resources in vRealize Automation * Creating resource actions in vRealize Automation * Using Day-2 Actions in vRealize Automation USING VSPHERE CLIENT CODE CAPTURE * Enabling vSphere Client code capture * Using vSphere Client code capture to capture code in vRO, Javascript, PowerCLI or other languages. * Using the captured code in vRealize Orchestrator workflows or actions.

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

Through this course, you will learn how to arrange Kafka a producer and consumer and Kafka Streams and Connectors accurately. You will also gain the skills needed to coordinate Kafka with existing application stages and to pass the Apache Kafka certification exam.

This course covers all the key concepts that will help you prepare for and pass the AWS Certified Cloud Practitioner certification exam for the latest CLF-C01. A practical-based course where you will gain practical knowledge about AWS Cloud through videos and demo sessions.

A carefully structured course loaded with lab exercises that will help you learn all about implementing Microsoft Defender for Endpoint platform the right way. The course's learning path aligns with the SC-200: Microsoft Security Operations Analyst Exam.

This course will help you qualify for the Microsoft SC 900 exam, and this certification is targeted at those looking to familiarize themselves with the fundamentals of security, compliance, and identity across cloud-based and related Microsoft services.

Discover Microsoft Fabric's architecture, master Data Engineering with OneLake and Spark, and elevate your skills in data warehousing and real-time processing. Compare SQL and KQL for better insights, and improve storytelling using Power BI. Finally, you will end with practical data science techniques and data management methods.

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning?  . Machine learning classes in pune [https://www.sevenmentor.com/machine-learning-course-in-pune.php] The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune [https://www.sevenmentor.com/machine-learning-course-in-pune.php] Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune [https://www.sevenmentor.com/machine-learning-course-in-pune.php]