Hyper Text Markup Language.

Author note:-

Throughout this tutorial we are not only going to take a lot of practice, instead, but we will also focus on giving you practical skills to build your first website. In this frontend course, you will surely get a piece of good knowledge about what we have mentioned in the previous page like all Popular libraries, webmaster, how to create a proper sitemap to give the user a good experience, how to get revenue from the website, what kind of hosting are available in the market And what service is best for a specific project website, and at the end of this journey, you will have a lot of projects in your hands.


HTML For A Web Page. A web application. A Static Website. A Simple Portfolio. A Blog.

Difference Between HTML and HTML5 :

HTML5 is the fifth version of HTML. Many elements are removed or modified from HTML5.

If we want to understand what exactly changed in this new version, we have in the table below almost everything that really matters to us as a big

It works with all old browsers. It supported by all new browser like Firefox, Mozilla, Chrome, Safari, etc
Elements like nav, header were not present. New element for web structure like nav, header, footer etc.
Older version of HTML are less mobile-friendly. HTML5 language is more mobile-friendly.
Does not allow JavaScript to run in browser. This is possible due to JS Web worker API in HTML5.

HTML Editors

  • Notepad
  • Notepad++
  • Sublime Text 3
  • VS code

We have many editors present in the market it's up to you which one you liked, My Suggestion is to use a visual code editor for better understanding and for to get a web developer experience. Next, we will discuss the Construction of an HTML Page. See you there...

According to QWIKLABS:  When you complete this activity, you can earn the badge displayed above! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!

Lab Name Lab Code Lab Link Youtube Link
AI Platform: Qwik Start GSP076
Google Cloud Speech API: Qwik Start GSP119 #
Cloud Natural Language API: Qwik Start GSP097
Entity and Sentiment Analysis with the Natural Language API GSP038
Speech to Text Transcription with the Cloud Speech API GSP048 #

According to QWIKLABS:  Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.

Lab Name Lab Code Lab Link Youtube Link
Introduction to SQL for BigQuery and Cloud SQL GSP281
Rent-a-VM to Process Earthquake Data GSP008
Weather Data in BigQuery GSP009
Distributed Image Processing in Cloud Dataproc GSP010
Analyzing Natality Data Using AI Platform and BigQuery GSP012 #
Predict Baby Weight with TensorFlow on AI Platform GSP013

According to QWIKLABS:  Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model.

Lab Name Lab Code Lab Link Youtube Link
BigQuery: Qwik Start - Command Line GSP071 #
Creating a Data Warehouse Through Joins and Unions GSP413 #
Creating Date-Partitioned Tables in BigQuery GSP414 #
Troubleshooting and Solving Data Join Pitfalls GSP412 #
Working with JSON, Arrays, and Structs in BigQuery GSP416 #
Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors GSP814 #

According to QWIKLABS:  Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this fundamental-level quest, you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and highly available databases that run on GKE containers, Cloud SQL will give you the knowledge and experience needed so you can start integrating this service right away.

Lab Name Lab Code Lab Link Youtube Link
Introduction to SQL for BigQuery and Cloud SQL GSP281
Cloud SQL for MySQL: Qwik Start GSP151 #
Loading Data into Google Cloud SQL GSP196 #
Cloud SQL with Terraform GSP234 #
Using Ruby on Rails with Cloud SQL for PostgreSQL GSP109 #
APIs Explorer: Cloud SQL GSP423 #
Connect to Cloud SQL from an Application in Kubernetes Engine GSP449 #