HTML – Introduction to HTML
Welcome to the HTML tutorials section. Here you can learn the basic of HTML. After learning this tutorials you will be able to make your own web pages. If you are already familiar with XML, then HTML will seem easy to learn. I recommend you, not to read this tutorial from head to tale but to spend a quarter or a half hour after reading several lessons, and practice.
Preparing for learning HTML
Creating documents in HTML is not difficult. To start, you need only a notepad and dedication. If you are new in the field and you have not yet read Beginner’s Guide to HTML, I suggest you spend a few minutes reading it.
Web pages are very useful. This explains some of the arguments:
- The simplest way to spread information on the Internet
- Another form to increase your business
- You can tell the world that you have something to say on a personal page
Words to remember
- Tag – used to specify the regions of the HTML document, which a browser will read later.Tags will look like this: <tag>
- Element – is a complete tag, having an opening <tag> and a closing </ tag>.
- Attribute – is used to change the value of an element in HTML. Usually an element has several attributes.
So far just remember that a tag is a command that the browser will execute, an element that is a complete tag and an attribute customize and change an element in HTML.
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Recipes in Project-Based Learning:
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Google –> SETScholars