Cloud computing provides a simple way to access servers, storage, databases, and a broad set of application services over the Internet. Cloud platforms such as Amazon Web Services (AWS) own and maintain the network-connected hardware required for these application services, while you provision and use what you need via a web application. Whether you are just starting out or looking to build on your existing skills, this AWS course is a great way to advance your cloud journey.

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Implement a reliable application architecture using the patterns and best practices recommended by AWS

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Get up to speed with effectively securing your cloud systems
Learn
Get familiar with the fundamentals of cloud-based technologies
Create a virtualized network inside the Azure cloud computing network
Distinguish between on-premise and hosted solutions
Understand the differences between SaaS, PaaS, and IaaS
Identify various cloud computing vulnerabilities
Implement and secure your own environment within Microsoft Azure

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Get to grips with TensorFlow 2.0 and scikit-learn
Learn
Fundamentals of machine learning (and introducing the benefits of scikit-learn)
Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning in scikit-learn
How to identify a problem, select the right model, and optimize it to get the best desired outcome: insights into data
TensorFlow 2.0 for deep learning with neural networks

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Gain a thorough understanding of Azure Stream Analytics
Discover how to ingest data using Azure Event Hubs
Find out how to archive data from Event Hubs to Azure Data Lake
Provision Event Hubs, Data Lake, and SQL Server in Azure
Listen to an event hub and stream the data to a SQL Server table
Communicate with event hubs for effortless data transfer
Discover how event hub groups work

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Effectively apply Advanced Analytics to large datasets using the power of PySpark
Learn
Gain a solid knowledge of vital Data Analytics concepts via practical use cases
Create elegant data visualizations using Jupyter
Run, process, and analyze large chunks of datasets using PySpark
Utilize Spark SQL to easily load big data into DataFrames
Create fast and scalable Machine Learning applications using MLlib with Spark
Perform exploratory Data Analysis in a scalable way
Achieve scalable, high-throughput and fault-tolerant processing of data streams using Spark Streaming

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Understand the user in-depth and apply the best UX practice and design principles
Learn
Explore the world of user-centered design and transform the way you create experiences
Create products that provide maximal value to the customer, to save you time and money
Develop strong empathy for the user, to help you create usable software
Create credible and desirable software for the user, to improve sales conversions and help users stay loyal to your products

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Key Features
Gain proficiency in building desktop applications with Electron
Understand how to use Vanilla JS and React with Electron
Create custom menu items, system tray apps, and more

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Learn Flutter - Google's mobile development framework - and build Flutter iOS and Android apps using a single codebase

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Learn
Master end-to-end ACI fabric deployment
Perform initial APIC turn-up and configurations, Commission Leaves, and Spines
All aspects of ACI policy objects, interfaces, switch profiles, groups, and policies
Get to grips with ACI tenants, application profiles, Endpoint Groups (EPGs), contracts, and policies
Learn how objects and policies relate to each other-multi-tenancy in ACI
How to extend the ACI fabric beyond the datacenter
Configure external L2OUTs and L3OUTs

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