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Program 1
<html><head><title>Margin Demo</title>
<style>
.display1
{
border: 2px;
border-style: double;
margin-top: 20px;
margin-right: 25px;
margin-bottom: 30px;
margin-left: 25px;
margin:20px 15px 30px 20px;
}
.display2
{
border: 2px;
border-style: double;
border-color:brown;
margin:20px 15px 30px 20px
}
</style>
</head>
<body >
<div class="display1">
This being a beginner’s tutorial, I will try to make it as simple as it could be.
Have you ever went for grocery shopping? What do you do before going to the market?
I always prepare a list of ingredients beforehand. Also, I make the decision according to the previous purchasing experience. Then, I go and purchase the items. But, with the rising inflation, it’s not too easy to work in the budget. I have observed that my budget gets deviated a lot of times.
This happens because the shopkeeper changes the quantity and price of a product very often. Due to such factors, I have to modify my shopping list. It takes a lot of effort, research and time to update the list for every change.
This is where Machine Learning can come to your rescue. Still confused?
Don’t worry! Read this DataFlair’s latest Machine learning tutorial to get deep insight and understand why machine learning is trending.
</div>
<div class="display2">
After knowing what machine learning is, let’s take a quick introduction to machine learning and start the tutorial.
With the help of Machine Learning, we can develop intelligent systems that are capable of taking decisions on an autonomous basis. These algorithms learn from the past instances of data through statistical analysis and pattern matching. Then, based on the learned data, it provides us with the predicted results.
Data is the core backbone of machine learning algorithms. With the help of the historical data, we are able to create more data by training these machine learning algorithms.
For example, Generative Adversarial Networks are an advanced concept of Machine Learning that learns from the historical images through which they are capable of generating more images. This is also applied towards speech and text synthesis.
Therefore, Machine Learning has opened up a vast potential for data science applications. Machine Learning combines computer science, mathematics, and statistics. Statistics is essential for drawing inferences from the data.
</div>
<div class="display2">
After knowing what machine learning is, let’s take a quick introduction to machine learning and start the tutorial.
With the help of Machine Learning, we can develop intelligent systems that are capable of taking decisions on an autonomous basis. These algorithms learn from the past instances of data through statistical analysis and pattern matching. Then, based on the learned data, it provides us with the predicted results.
Data is the core backbone of machine learning algorithms. With the help of the historical data, we are able to create more data by training these machine learning algorithms.
For example, Generative Adversarial Networks are an advanced concept of Machine Learning that learns from the historical images through which they are capable of generating more images. This is also applied towards speech and text synthesis.
Therefore, Machine Learning has opened up a vast potential for data science applications. Machine Learning combines computer science, mathematics, and statistics. Statistics is essential for drawing inferences from the data.
</div>
</body>
</html>