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Free Download Text Analytics With Ai Using Snowflake Cortex And Power Bi
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.66 GB | Duration: 4h 13m
Use Snowflake Cortex to extract sentiment, summaries & topics – visualize & explore insights with Power BI & Copilot
What you’ll learn

Use Snowflake Cortex AI functions to analyse unstructured text at scale
Perform sentiment analysis and text classification on real customer data
Build and deploy a Streamlit app using Snowflake Cortex
Design and publish a professional Power BI dashboard using Microsoft Copilot
Prompt AI tools effectively to generate code and insights
Complete two end-to-end text analytics projects from raw data to insight
Requirements
A computer with internet access
Basic familiarity with SQL would help but is not mandatory: knowing what SELECT and WHERE is enough. We explain everything as we go.
Some experience with data or spreadsheets – you don’t need to be a data analyst, just comfortable working with rows and columns.
A free Snowflake account (we walk you through setting one up at the beginning)
Description
Artificial intelligence is transforming how organisations work with data – and one of the biggest opportunities right now is applying AI to text. Customer reviews, support tickets, survey responses, and feedback forms are full of insight, but reading and categorising them manually simply doesn’t scale. This course teaches you how to do it automatically, accurately, and at scale using tools that are already available to you.In this course, you will use Snowflake Cortex – Snowflake’s built-in suite of AI functions – to translate, summarise, score sentiment, classify topics, and identify churn risk and urgency directly inside your data platform. You will then use Microsoft Copilot in Power BI to turn those enriched datasets into professional, interactive dashboards, with AI helping you every step of the way.No machine learning background is needed. No complex setup. Everything is done inside Snowflake’s notebook environment and Power BI Desktop, and every step is explained clearly as you go.Two complete, real-world projectsProject 1Customer Reviews AnalysisAnalyse product reviews using Cortex AI functions. Translate, summarise, and classify text. Then build a Streamlit app to explore the results interactively.Project 2Telco Support TicketsProcess a real support ticket dataset. Score sentiment, flag churn risk, assign urgency, and visualise everything in a polished Power BI dashboard built with the help of Copilot.What you will work withSnowflake Cortex functions – Translate, Summarize, Sentiment, Classify, AI_AGG, and moreSnowflake Notebooks and the Cortex Code assistant for generating and refining SQL and PythonStreamlit – to build and deploy an interactive app inside SnowflakePower BI Desktop and Copilot – for AI-assisted dashboard design, DAX generation, and KPI cardsPrompting best practices – how to get reliable, accurate results from AI tools in a data contextWho this is designed forThis course is designed for anyone working with unstructured text who wants to apply AI practically – without needing a data science degree. If you have some familiarity with data and a willingness to learn, you have everything you need to get started.By the end of the course, you will have completed two portfolio-ready projects, gained hands-on experience with Snowflake Cortex, Power BI and Microsoft Copilot, and developed a workflow you can apply immediately to your own data.
Anyone working with customer feedback, reviews, or support tickets,Data analysts who want to add AI to their toolkit,BI professionals and Power BI users who want to work faster with Copilot,Professionals curious about practical A,Students and career changers breaking into data
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https://www.udemy.com/course/text-analytics-with-ai-using-snowflake-cortex-and-power-bi/

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Free Download Testing React Native Apps
Released 8/2024
By Galaxies.dev
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 13 Lessons ( 1h 13m ) | Size: 327 MB

Testing is an essential part of the React Native development process. It ensures that your app works as expected and that new features don’t break existing functionality. In this course, you’ll learn how to test your React Native apps with Jest, React Testing Library, and also Maestro for E2E tests.
What will I learn?
? Setup tests in your React Native app
? Write unit tests with Jest
? Test components with React Testing Library
? Mock modules and test fetch requests
? Run E2E tests with Maestro
Get ready to level up your React Native app development skills with comprehensive testing techniques. Enroll in "Testing React Native Apps" now and ensure your apps are bug-free and reliable!
Homepage
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https://galaxies.dev/course/test-react-native-apps

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Testing in Node.js
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 26m | 106 MB
Instructor: Ian Marshall​

Learn to use the Node.js native test runner and assertion library for your software testing needs.

What you’ll learn

Test your JavaScript code with Node.js. This course, Testing in Node.js, walks you through the techniques and syntax needed to work with the Node.js native test runner and assertion library. You’ll learn to automate your tests, check test coverage, and produce reports using Node.js.

More Info

Free Download Testing and Exception Handling in Spring Framework 7
Published 3/2026
MP4 | Video: h264, 720p | Audio: AAC, 48 KHz
Language: English | Size: 475.36 MB | Duration: 1h 42m 37s
Without proper testing and exception handling, Spring applications can fail unpredictably in production, leaving you without a reliable way to detect, diagnose, or prevent issues.

In this course, Testing and Exception Handling in Spring Framework 7, you’ll gain the ability to build resilient Spring applications through effective testing strategies and centralized error handling.
First, you’ll explore the differences between unit testing and Spring integration testing, along with the key libraries used to implement them.
Next, you’ll learn how to write effective unit tests using JUnit and Mockito to validate business logic and service-layer components.
Finally, you’ll discover how to create comprehensive Spring integration tests and implement centralized exception handling and input validation across your applications.
When you’re finished with this course, you’ll have the skills and knowledge of Spring testing and exception handling needed to build stable, maintainable, and production-ready applications.
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https://www.pluralsight.com/courses/testing-exception-handling-spring-framework-7

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Terraform: Resource Lifecycle Management
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 3m | Size: 274 MB​

Terraform is an amazing toolset for automating infrastructure in the public and private cloud. This course expands on your foundational knowledge by exploring advanced resource lifecycle management.

What you’ll learn
Terraform is an essential tool for managing your infrastructure as code. In this course, Terraform: Resource Lifecycle Management, you’ll gain the ability to handle advanced requirements and unique infrastructure challenges. First, you’ll explore how to explicitly manage dependencies and customize resource lifecycles. Next, you’ll discover how to discover and import existing resources. Finally, you’ll learn how to refactor configurations and migrate resources without impacting uptime. When you’re finished with this course, you’ll have the skills and knowledge of Terraform resource management needed to handle advanced infrastructure as code operations.

Terraform: Managing Multiple Configurations
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 57m | Size: 225 MB​

Terraform is an amazing toolset for automating infrastructure in the public and private cloud. This course expands on your foundational knowledge by exploring the use of data sources to make dynamic and connected configurations.

What you’ll learn
Terraform is an essential tool for managing your infrastructure as code. In this course, Terraform: Managing Multiple Configurations, you’ll gain the ability to implement data sources to augment existing configurations and tie multiple root modules together. First, you’ll explore how data sources are implemented and evaluated. Next, you’ll discover how Terraform remote state can be used to connect configurations. Finally, you’ll learn how to use external data sources to share configuration information and trigger automatic evaluations. When you’re finished with this course, you’ll have the skills and knowledge of Terraform data sources needed to build dynamic and robust root modules.

Terraform On Azure With Iac Devops Sre | Real-World 25 Demos
Last updated 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 19.89 GB | Duration: 30h 30m​

Load Balancer, Application Gateway, Traffic Manager, VM, VMSS, Azure DevOps, MySQL Server, DNS Zones, IaC, NAT Gateway

What you’ll learn
You will learn to master Terraform in a Real-world perspective with 25 demo’s on Azure Cloud with 20+ Azure Services and 30+ Terraform Concepts
You will build Azure Virtual Network 4-Tier design with Subnets and Network Security Groups
You will build infrastructure incrementally on Azure Cloud from simple VM to complex designs like load balancing across regions using Azure Traffic Manager with
You will build Standard Load Balancer External and Internal with Inbound NAT Rules using Terraform
You will learn and build Virtual Machine Scale Sets Manual and Autoscaling Profile concepts like default, recurrence and Fixed Time profiles with Terraform
You will build Azure Public and Private DNS Zones using Terraform
You will implement IaC DevOps usecase using Azure DevOps Build and Release Pipleines for your Terraform Configurations
You will build Azure Application Gateway usecases like Context Path Routing, Mutlsite Hosting, SSL, HTTP to HTTPS Redirect and AG Error Pages
You will build a DNS to DB usecase with Azure MySQL Single Server with Virtual Network Rules and Service Endpoint concepts in Azure
You will learn in detail about Terrafrom State, Local and Remote Backends.
You will learn and implement all Terraform Provisioners
You will learn and implement Terraform Modules with 2 types (Public Modules and Local Modules) and also publish a module to Terraform Public Registry
You will implement Terraform Remote State Datasource concept for Load balancing across regions using Azure Traffic Manager and Azure Standard Load Balancer

Requirements
You must have an Azure Cloud account to follow with me for hands-on activities.
You don’t need to have any basic knowledge of Terraform. Course will get started from very very basics of Terraform and take you to very advanced levels
From Azure cloud perspective, also for complex topics like Standard Load Balancer, Application Gateway, Virtual Machine Scale Sets, we first implemnt via Azure Portal and then automate them via Terraform.

Description
******* Course Overview *******Welcome to this Amazing course on Terraform on Azure with IaC DevOps SRE | Real-World 25 Demos. Below is the list of modules covered in this course. Course Modules01. Infrastructure as Code (IaC)02. Install Tools on MacOs, LinuxOS and WindowsOS03. Command Basics04. Language Syntax05. Settings Block06. Providers Block07. Multiple Providers usage08. Dependency Lock File Importance09. Resources Syntax and Behavior10. Azure Virtual Network, Subnets and Network Security Groups11. Azure Linux Virtual Machine, Network Interfaces and Public IP12. Azure Bastion Host Linux VM and Azure Bastion Service13. Azure Standard Load Balancer using Portal14. Azure Standard Load Balancer Basics 15. Azure Standard Load Balancer – Inbound NAT Rules16. Terraform Meta-Argument Count with Azure LB and VMs17. Terraform Meta-Argument for_each with Azure LB and VMs18. Azure Virtual Machine Scale Sets Basics – Manual Scaling19. Azure Virtual Machine Scale Sets Basics – Auto Scaling 4 Profiles20. Azure Internet LB + Web VMSS + Internal LB + App VMSS21. Azure Private DNS Zones22. Delegate DNS Domain to Azure DNS23. Azure Public DNS Zones24. Terraform Remote State Storage using Azure Storage Account25. Azure Traffic Manager and Terraform Remote State Datasource26. Azure Application Gateway using Azure Portal27. Azure Application Gateway using Terraform28. Azure Application Gateway Context Path Routing using Terraform29. Azure Application Gateway Multisite Hosting using Terraform30. Azure Application Gateway SSL + HTTP to HTTPS Redirect + AG Error Pages31. Azure Application Gateway SSL from Azure Key Vault using Terraform32. Azure DevOps for Terraform Projects – IaC (Infrastructure as Code) DevOps33. Azure MySQL Single Server with Terraform34. Terraform Modules – Use Public Registry Module35. Terraform Azure Static Website36. Terraform Modules – Build Local Terraform Module37. Terraform Modules – Publish to Terraform Public Registry38. Terraform Module SourcesAzure Services01. Resource Group02. Virtual Network03. Subnets04. Network Security Groups05. Virtual Machines06. Network Interfaces07. Public IPs08. Load Balancer – Internet and Internal09. Traffic Manager11. Virtual Machine Scale Sets 12. Autoscaling13. Private DNS Zones14. Public DNS Zones15. Application Gateway16. Storage Accounts17. Azure DevOps – Build Pipelines18. Azure DevOps – Release Pipelines19. Azure MySQL Single Server20. Azure NAT Gateway Terraform Concepts covered01. Settings Block02. Providers Block03. Multiple Providers usage04. Dependency Lock File Importance05. Resources Syntax and Behavior06. Resources Meta-Argument – depends_on07. Resources Meta-Argument – count08. Resources Meta-Argument – for_each09. Resources Meta-Argument – lifecycle10. Input Variables – Basics11. Input Variables – Assign When Prompted12. Input Variables – Assign with terraform.tfvars13. Input Variables – Assign with tfvars var-file argument14. Input Variables – Assign with auto tfvars15. Input Variables – Lists16. Input Variables – Maps17. Input Variables – Sensitive Input Variables18. File Function19. Output Values20. Local Values21. Datasources22. Backends – Remote State Storage23. File Provisioner24. remote-exec Provisioner25. Null Resource26. Modules from Public Registry27. Build Local Module28. Dynamic Blocks29. base64encode function30. filebase64 function31. element function32. lookup function33. Remote State DatasourceEach of my courses comes withAmazing Hands-on Step By Step Learning ExperiencesReal Implementation ExperienceFriendly Support in the Q&A section30-Day "No Questions Asked" Money Back Guarantee!

Who this course is for:
Infrastructure Architects or Sysadmins or Developers who are planning to master Terraform from Real-World perspective on Azure Cloud,Any beginner who is interested in learning IaC Infrastructure as Code current trending tool Terraform,Anyone who want to implement IaC DevOps on Azure DevOps using Build and Release Pipelines

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Free Download Terraform Fundamentals Infrastructure as Code, Providers, and State for Terraform Associate (004)
Released 3/2026
By Ned Bellavance
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 56m 53s | Size: 166 MB

Managing infrastructure manually is error-prone and time-consuming, making it difficult to scale and maintain consistency across environments.
What you’ll learn
Managing infrastructure manually is error-prone and time-consuming, making it difficult to scale and maintain consistency across environments. In this course, Terraform Fundamentals: Infrastructure as Code, Providers, and State for Terraform Associate (004), you’ll gain the ability to deploy and manage infrastructure using Terraform. First, you’ll explore infrastructure as code concepts and Terraform’s role in the IaC landscape. Next, you’ll discover how to work with Terraform providers to interact with different platforms. Finally, you’ll learn how to manage Terraform state to track your infrastructure. When you’re finished with this course, you’ll have the skills and knowledge of Terraform fundamentals needed to deploy and manage infrastructure using Terraform.
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TensorFlow for Deep Learning Bootcamp
2025-02-12
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 56.96 GB | Duration: 62h 57m​

Learn TensorFlow by Google. Become an AI, Machine Learning, and Deep Learning expert!

What you’ll learn
Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
Increase your skills in Machine Learning, Artificial Intelligence, and Deep Learning
Understand how to integrate Machine Learning into tools and applications
Learn to build all types of Machine Learning Models using the latest TensorFlow 2
Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
Using real world images to visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy
Applying Deep Learning for Time Series Forecasting
Gain the skills you need to become a TensorFlow Developer
Be recognized as a top candidate for recruiters seeking TensorFlow developers

Requirements
Mac / Windows / Linux – all operating systems work with this course!
No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful

Description
Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow & Deep Learning Expert!Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD. By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer!Here is a full course breakdown of everything we will teach (yes, it’s very comprehensive, but don’t be intimidated, as we will teach you everything from scratch!):The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.This course will be very hands on and project based. You won’t just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 – TensorFlow FundamentalsIntroduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and NumPyUsing @tf.function (a way to speed up your regular Python functions)Using GPUs with TensorFlow1 – Neural Network Regression with TensorFlowBuild TensorFlow sequential models with multiple layersPrepare data for use with a machine learning modelLearn the different components which make up a deep learning model (loss function, architecture, optimization function)Learn how to diagnose a regression problem (predicting a number) and build a neural network for it2 – Neural Network Classification with TensorFlowLearn how to diagnose a classification problem (predicting whether something is one thing or another)Build, compile & train machine learning classification models using TensorFlowBuild and train models for binary and multi-class classificationPlot modelling performance metrics against each otherMatch input (training data shape) and output shapes (prediction data target)3 – Computer Vision and Convolutional Neural Networks with TensorFlowBuild convolutional neural networks with Conv2D and pooling layersLearn how to diagnose different kinds of computer vision problemsLearn to how to build computer vision neural networksLearn how to use real-world images with your computer vision models4 – Transfer Learning with TensorFlow Part 1: Feature ExtractionLearn how to use pre-trained models to extract features from your own dataLearn how to use TensorFlow Hub for pre-trained modelsLearn how to use TensorBoard to compare the performance of several different models5 – Transfer Learning with TensorFlow Part 2: Fine-tuningLearn how to setup and run several machine learning experimentsLearn how to use data augmentation to increase the diversity of your training dataLearn how to fine-tune a pre-trained model to your own custom problemLearn how to use Callbacks to add functionality to your model during training6 – Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)Learn how to scale up an existing modelLearn to how evaluate your machine learning models by finding the most wrong predictionsBeat the original Food101 paper using only 10% of the data7 – Milestone Project 1: Food VisionCombine everything you’ve learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods. Our model well and truly beats the original Food101 paper.8 – NLP Fundamentals in TensorFlowLearn toreprocess natural language text to be used with a neural networkCreate word embeddings (numerical representations of text) with TensorFlowBuild neural networks capable of binary and multi-class classification using:RNNs (recurrent neural networks)LSTMs (long short-term memory cells)GRUs (gated recurrent units)CNNsLearn how to evaluate your NLP models9 – Milestone Project 2: SkimLit Replicate a the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster)10 – Time Series fundamentals in TensorFlowLearn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)Prepare data for time series neural networks (features and labels)Understanding and using different time series evaluation methodsMAE – mean absolute errorBuild time series forecasting models with TensorFlowRNNs (recurrent neural networks)CNNs (convolutional neural networks)11 – Milestone Project 3: (Surprise)If you’ve read this far, you are probably interested in the course. This last project will be good. we promise you, so see you inside the course TensorFlow is growing in popularity and more and more job openings are appearing for this specialized knowledge. As a matter of fact, TensorFlow is outgrowing other popular ML tools like PyTorch in job market. Google, Airbnb, Uber, DeepMind, Intel, IBM, Twitter, and many others are currently powered by TensorFlow. There is a reason these big tech companies are using this technology and you will find out all about the power that TensorFlow gives developers. We guarantee you this is the most comprehensive online course on TensorFlow. So why wait? Make yourself stand out by becoming a TensorFlow Expert and advance your career.See you inside the course!

Who this course is for:
Anyone who wants to become a top 10% TensorFlow Developer and be at the forefront of Artificial Intelligence, Machine Learning, and Deep Learning, Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow, Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning, Anyone looking to master building ML models with the latest version of TensorFlow

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