Cloudswyft Courses

Data Science on Microsoft Azure Using Python Programming

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The basics of data science on Microsoft Azure using Python programmingLearn data analysis on Microsoft Azure using Python programming

Microsoft Future Ready: DevOps Development, Implementation and Azure Automation

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This course is part of DevOps with Microsoft Azure ExpertTrack where you’ll learn the core values and principles of DevOps and how to put these into practice.Learn how to use DevOps to deliver stable environmentsThis three-week course will give you the skills and knowledge to deliver stable environments rapidly, reliably, and at scale.You’ll learn how to save time by avoiding manual configuration of environments as well as helping with consistency by representing your environments as code.Save time and reduce errors with Azure automationYou’ll gain knowledge on the process of implementing Microsoft Azure Automation and be able to put this knowledge into practice by demonstrating how to configure and implement Azure automation.Delving deep into Azure, you’ll cover areas such as Chef deployments in Azure, Puppet deployments, and how to implement, configure and apply Azure Resource Manager templates.You’ll also develop an understanding of the basics of Desired State Configuration (DSC). From this, you’ll learn how to create a DSC configuration file, import it into the automation account and compile it.Assemble ARM templates and plug into Visual Studio Team ServicesYou’ll learn how to assemble ARM templates and then plug these into Visual Studio Team Services to create pipelines. This skill helps you deliver a more efficient process and reduce your production time.By the end of the course, you’ll understand important DevOps topics such as Azure, AWS, Chef, and the integration between them. You’ll also learn how to take your code from development to production.This course is best suited for developers working in an organisation delivering software.This will also suit IT pros who have some knowledge and experience working with cloud-based solutions and Microsoft Azure, and are looking to expand their skills.The upgrade version of this course will include all the required CloudSwyft Hands-On Lab practice environments to be used by students and learners on this course, powered by CloudSwyft.You will need a computer running Windows, Mac OSX or Linux, and a web browser.

AI Programming with Python for Beginners

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What’s the best way to learn Python programming for beginners? It’s easy for novices to use and learn the powerful programming language.This course is a great introduction to fundamental programming concepts and how to code in Python.You’ll learn the basics such as syntax, variables and types before carrying out data analysis using Python. After you’ve mastered these essentials you’ll progress to handling data structures such as creating and manipulating regular Python lists, NumPy arrays and the Pandas DataFrame.You’ll also learn other Python functions, import packages and control flows along the way, and discover how to perform interesting calculations.This Python online course will also allow you to take your first dive into the world of data visualisation, and you’ll create your first stunning visualisations and customise plots on real data.What is Python used for? Coding and the fundamentals explainedOn this online Python course you’ll find out why Python is the best programming language for AI and machine learning. Machine learning with Python teaches computers to learn from and recognise specific patterns. Python AI is also capable of making predictions, estimating potential answers and more.You’ll discover how coding with Python allows developers to create advanced networks with powerful data-management capabilities.Unlock quick and easy data visualisation techniquesHow can you use Python to turn vast quantities of data into useful, easy-to-understand data?The course will show you the different ways Python can be used to present high-volume information, from histograms and heat maps to raincloud plots, to help you analyse and make better data-based decisions.This course is designed for students and professionals looking to launch a career in Python programming.It’s also ideal for students with a basic knowledge of Python who wish to gain more extensive knowledge.

Microsoft Future Ready: Introduction to Python Programming

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Python is the most requested skill in data science job ads today. The demand for these skills grew 435% from 2019, according to Forbes, and is ranked in the top 3 for programming languages by the IEEE Spectrum.This course provides a thorough introduction to Python - the programming language used by Instagram, IBM, Netflix, and Facebook. You’ll build knowledge specific to data science applications, then dive into the world of data visualisation.Start with the basics of Python programmingYou’ll learn basic arithmetic and variables and basic syntax, and how to create and manipulate regular Python lists.You’ll discover how to build and handle data structures such as Python lists, NumPy arrays and Pandas DataFrame, and perform interesting calculations. You’ll also be introduced to Python functions and control flow.Build meaningful data visualisationsLearn how to create stunning data visualisations with Python. You’ll learn how to create and customise plots on real data, and create presentations based on your own data. This is the next key step to getting buy-in on your data analysis by ensuring that you can communicate it to a diverse audience.By the end of the course, you’ll be better equipped to start a career in Python programming - ready to offer employers the most in-demand skill in data science.This course is designed for students and professionals who aim to start their careers in Python programming.

Microsoft Future Ready: Continuous Integration Implementation

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Understand the building blocks of DevOps; Continuous Integration, Continuous Deployment and Continuous DeliveryDevOps is a set of practices that combines software development and IT operations. With it, you can shorten the systems development life cycle and provide continuous delivery with high software quality.During this four-week course, you’ll discover the building blocks of DevOps to give you the strong foundational knowledge to be able to tackle more specific practices. You’ll learn about Continuous Integration, Continuous Delivery and Continuous Deployment, understanding the difference between the concepts, and how they can work together to save time and improve performance.Learn how to use functions within a DevOps development environmentYou’ll delve into a DevOps development environment to understand continuous integration builds, automated testing, and continuous delivery and deployment.You will learn how these function separately and together, and understand how these best practices help with development workflow and productivity. You’ll also strengthen your knowledge of these processes in a development environment.Understand how to implement DevOps practices with VSTS, TFVC and Microsoft AzureYou’ll put your knowledge into practice and learn how to implement the DevOps practices of continuous integration and continuous deployment using Microsoft Visual Studio Team Services (VSTS), Team Foundation Version Control (TFVC) and Microsoft Azure.By the end of the course, you’ll understand how to provision and de-provision infrastructure and databases, and deploy databases in release pipelines. You’ll also know how to perform continuous deployment and delivery using Jenkins and VSTS and Octopus.This course is best suited for developers working in an organisation delivering software.This will also suit IT pros who have some knowledge and experience working with cloud-based solutions and Microsoft Azure, and are looking to expand their skills.The upgrade version of this course will include all the required CloudSwyft Hands-On Lab practice environments to be used by students and learners on this course, powered by CloudSwyft.You will need a computer running Windows, Mac OSX or Linux, and a web browser.

Deep Learning on Azure with Python: Introduction to Deep Learning

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In this hands-on introduction to deep learning, you will learn about different neural network types. You’ll develop your understanding of key deep learning vocabulary, concepts, and algorithm, enabling you to understand how deep learning frameworks work.Deep learning is a highly advanced form of machine learning. At its core are deep learning neural networks, so-called because they are inspired by human learning and brain structures.Unlike most machine learning, deep learning frameworks can process data from unstructured sources. Text analytics, and image and video processing allow deep learning frameworks to acquire information as we do.Get practical experience of Python for deep learningDeep learning algorithms can be used for a range of purposes, automating functions that once would have required human understanding. These include customer service, translation, and image analysis. Deep learning models can even write news stories.You’ll discover deep learning with Python programming. You will learn how to use Microsoft’s Cognitive Toolkit (CNTK) to build end-to-end neural networks, on Microsoft Azure’s cloud-based service.Explore common frameworks for neural networksYou’ll build your skills and understanding in both the analysis and application of deep learning frameworks, including:multi-class Logistic Regression and MLP (Multi-Layered Perceptron)CNN (Convolution Neural Network) for text processingRNN (Recurrent Neural Network) to forecast time-series dataLSTM (Long Short Term Memory) process sequential text dataYou’ll then move on to explore how to build end-to-end models using one or several of these neural networks to recognise hand-written digits.This machine learning and artificial intelligence course is designed for those who would like to learn more about deep learning. Basic knowledge of python programming would be advantageous, as would solid maths and computer science skills.

Dynamics 365: Using Power Platform Applications

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Optimise your operations using Microsoft Power PlatformEnhance your operations within your organisation and learn the capabilities of Microsoft Power Platform. On this four-week course, you’ll learn alongside globally-recognised experts to understand how to enhance your business processes.This course will provide you with a strong foundational knowledge of Microsoft Dynamics 365 and the Power Platform, giving you a solid base to build from.Understand the functions and capabilities of Power PlatformYou’ll delve into Microsoft Power Platform, making the most of its functions to help develop and build complex business solutions.You’ll understand the full breadth of the platform’s capabilities and learn how to analyse and draw data visualisations for more insights. You’ll also learn how to automate routine tasks to help you save time, build virtual agents for communication, and standardise best practices.Gain practical experience and transferable skillsBy the end of this course, you’ll gain knowledge of Power Platform and understand how to put this into practice.You’ll know how to build a basic model-driven application, apply administrator options, and evaluate what platform components are, which will help you in your organisation. You’ll also understand how to manage user’s access to data and functionality within a security process.A good understanding of Power Platform gives you the ability to turn your data into useful insights and how to use these insights to automate your business processes.This course is part of the ExpertTrack Business Applications with Microsoft Dynamics 365 and Power Platform for Sales Functional Consultants, improving your skills in your role as Sales Functional Consultant.This course provides a thorough introduction to the Microsoft Power Platform and is aimed at general business users.

Essential Mathematics for Data Science and Machine Learning: Using R

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This course is designed to help you get up to speed on the key concepts and notation on which machine learning an AI are based. This course is not a full math curriculum. It’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning.We’ll start with some basic algebra to get started with equations and functions, then we’ll dive into some differential calculus to explore derivatives and optimisation. We’ll also look at some linear algebra and cover vectors and matrices, before finally getting to grips with some statistics and probability.On completion of this course, you will be able to:Apply basic Mathematical principles required in Data AnalyticsDemonstrate your understanding of Algebra FundamentalsDemonstrate your understanding of Quadratic Equations and FunctionsDemonstrate understanding of Differential Calculus FoundationsDemonstrate understanding of Vectors and MatricesDemonstrate understanding of Statistics and ProbabilityThis course is designed to fill the gaps for students who missed the key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.Individuals in the following roles will also find this course useful:Data AnalystsProgrammers

Advanced and Applied AI on Microsoft Azure

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Develop machine learning skills with Python and explore Azure's AI functionalityEnhance your understanding of machine learning and AI using Microsoft Azure and Python.

Microsoft Future Ready: How To Run Containers In A DevOps Production Environment

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Learn how to run containers using Azure Container Service and Azure Service FabricContainers can run on your local machine but if you run them in production, it unlocks a powerful and consistent way of bringing software to production.These production environment containers run on a set of machines managed by cluster orchestration. During this four-week course, you’ll learn how to create, deploy and configure your containerised applications on Microsoft Azure using Azure Container Service (ACS) and Azure Service Fabric (ASF).Understand how to manage your container cluster with VSTSThis course will show you how to manage and operate your container cluster. You’ll learn how to configure the cluster and how to manage them so that you have constant insights into how your application is doing in production. With this knowledge, you’ll learn how you can scale up and down based on demand, and deploy containers without downtime, minimising disruption.You will gain a solid understanding of continuous delivery infrastructure like Visual Studio Team Services (VSTS) as you use it to deploy your containers in a cluster.Delve into DevOps practices and how you can manage clusters and containersBy the end of the course, you will understand how a cluster works, how to set it up, and how you can manage your DevOps production environments, including proactive monitoring.You will be able to carry out useful processes such as setting up a production cluster to host containers and deploy containerised applications to different orchestrators.This course is part of DevOps with Microsoft Azure ExpertTrack where you’ll learn the core values and principles of DevOps and how to put these into practice.This course is best suited for developers working in an organisation delivering software.This will also suit IT pros who have some knowledge and experience working with cloud-based solutions and Microsoft Azure, and are looking to expand their skills.The upgrade version of this course will include all the required CloudSwyft Hands-On Lab practice environments to be used by students and learners on this course, powered by CloudSwyft.You will need a computer running Windows, Mac OSX or Linux, and a web browser.

AI Design and Engineering with Microsoft Azure

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Use the foundations of machine learning to develop your AI engineering careerDevelop AI Engineering skills using Microsoft Azure, on the path to role-based certifications.

Microsoft Future Ready: Using Python Programming to Explore the Principles of Machine Learning

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This course is part of the Advanced and Applied AI on Microsoft Azure ExpertTrack, helping you develop AI and machine learning skills and prepare you for the relevant Microsoft Microcredentials.Take your knowledge of machine learning and Python programming to the next level with this course offering both theoretical and practical experience.During this data science course, you’ll gain a strong understanding of the theories of machine learning before enhancing your practical knowledge by building, validating and deploying machine learning models.You’ll also learn how to use Python programming and Azure Notebooks to help you build and derive insights.Delve into AI concepts and basic machine learningBuild your understanding of relationships in complex data through basic machine learning and AI concepts.You’ll learn the theories which drive AI technology today as well as the core principles of machine learning categories including regression techniques and how algorithms behave and learn in machines.Learn how to deploy machine learning modelsThis course will help bridge the gap between IT and data science in putting a model into production, teaching you how to effectively deploy machine learning models.Gain practical experience using Python programming and Azure NotebooksYou’ll understand the importance of evaluating your data before developing algorithms and also get hands-on experience of using Python and Azure Notebooks to evaluate data. These powerful tools will help you gather insights from machine learning models once they have been deployed.During the course, you’ll learn how to clean data sets, collect output data, request rates, responses, failure rates and more with Python and Azure Notebooks.This course is for anyone looking to build their understanding of AI and machine learning.

Deep Learning on Azure with Python: Reinforcement Learning

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This machine learning course focuses on reinforcement learning and how it uses artificial intelligence to find the best possible solution to complex problems involving multiple decisions.Use reinforcement learning for complex problem solvingReinforcement learning acknowledges the multifaceted, multilevel nature of the problems we use machine learning to solve. These challenges might be viewed as a sequence, with each resolved challenge creating or limiting possibilities to solve the next.Framing these challenges as relational learning problems allows us to explore every potential path through a sequence of decisions. This allows artificial intelligence to determine the most effective or efficient solution to complex problems.Reinforcement learning can be applied to neural networks used in deep learning, helping us to build more refined algorithms.Explore dynamic programming algorithms and moreThis course will give you an introduction to reinforcement learning using Python, in Microsoft Azure. You’ll learn how to frame relational learning problems. You’ll get an introduction to common relational learning algorithms, including dynamic programming algorithms and temporal difference learning. And you’ll discover Project Malmo – a platform for AI experimentation built in Minecraft.Frame reinforcement learning problems in Azure with PythonBy the end of this course, you will have developed a clear understanding of reinforcement learning techniques, and the relevant formal notation. You’ll then be able to apply these in Microsoft Azure Cognitive Services, using Python programming.This machine learning and artificial intelligence course is designed for those who would like to learn more about deep learning. Basic knowledge of python programming would be advantageous, as would solid maths and computer science skills.

Microsoft Future Ready: Principles of Machine Learning with Python Programming

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This course is part of the Ethics Laws and Implementing an AI Solution on Microsoft Azure ExpertTrack, helping you understand AI ethics and laws while developing advanced AI and machine learning skills.During this course, you’ll learn the theory of machine learning before gaining practical experience in building, validating and deploying machine learning models.You’ll also delve into Python and Azure Notebooks, two powerful tools to help you learn how to build and derive insights.Learn the fundamentals of machine learning and AI theoryThis course will teach you the concepts and theories which drive AI technology today. You’ll learn the core principles of machine learning categories, including supervised and unsupervised learning, regression techniques and how algorithms behave and learn in machines.Gain practical experience in how to deploy machine learning modelsYou’ll learn how to effectively deploy machine learning models, and understand the gap between IT and data science in putting a model into production. This will further your knowledge in the field and help you understand how this practice sits in the workplace.Get hands-on experience using Python programming and Azure NotebooksIn this course you’ll learn the importance of evaluating your data before developingalgorithms. You’ll explore how to prepare and clean data sets as well as feature engineering techniques.Following the theory, you’ll get hands-on experience of the ways you can evaluate data using Python and Azure Notebooks.By the end of the course, you’ll understand the basic machine learning concepts, and gain experience using powerful tools such as Python with state-of-the-art machine learning algorithms. This knowledge will further help you understand the relationship in complex data.This course is part of the Microsoft Professional Program Certificate in Data Science and the Microsoft Professional Program in Artificial Intelligence. It provides a foundational understanding of machine learning using python, useful for anyone new to learning python, or wishing to use python to build machine learning solutions.The practical elements of this course involve building end-to-end workflows by combining the concepts and algorithms that will be introduced throughout the course. For the most part, you’ll be given the code you need to complete the exercises, but a basic knowledge of Python syntax will improve your understanding of what’s going on in the labs and demonstrations.