Machine Learning in Python

Machine Learning

Machine Learning (M.L) is studied as a branch of Artificial Intelligence.

As the name suggests, it means enabling a machine to learn a task from user data. The only condition is that there should not be any explicit programming to make it learn. Thus, machine learning involves making computer programs that can learn from user data by their own self to perform the necessary task.

Machine learning is broadly classified under 4 categories. Hence there can be 4 different ways in making a machine learning algorithm. These are:

• Supervised M.L
• Unsupervised M.L
• Semi-supervised M.L
• Reinforcement M.L

Although all 4 algorithms are important, we use the first 2 algorithms the most. Let us take a simple example to understand them better.

Supervised M.L

In a newspaper, we see a lot of categories of news. After that some might be political, some might be regional or some might be on science and technology. But the point we should notice is that news is divided into certain categories.

Now when these type of data are clubbed together and put to analysis, then this data will be known as labeled data or classified data. Because, before putting them to analysis, we have all of the details strictly classified under one category. Hence the algorithms under this category are known as Supervised M.L Algorithms.

Unsupervised M.L

Now, suppose we have a situation of corruption in any popular sports team. Then there might be a chance that few news agencies might try and fit it under political news category while some might fit it under the sports category. Either of them can be true. But for a computational program, such situations can be ambiguous. These data work on special algorithms. They are classified under the Unsupervised M.L Algorithms.

Application of Machine Learning

We use Machine Learning is a variety of situations. Firstly one of the popular topics of interest is data classification techniques using M.L. Secondly there can be many other applications like Text to speech conversion, Mood analysis using voice frequencies, Pattern predictors, etc.

Application of Machine Learning can be roughly explained in 4 steps:

• Extract the data.
• Breaking the data into pieces.
• Analyzing the data in those bits.
• Classifying the data.

Collecting important data from the pile of data is called Extracting the Data. The data can be of a variety of types. However, that discussion would be a little bit advanced for this part of the article.

After filtering the important data, we break them into pieces. It means we store each little punctuation and alphabets in a table. After that, we analyze the table.

The data is classified into the required categories after the analysis.

We follow this basic procedure in every data classification format. To know more about machine learning, please visit this site.