ML Algorithms in Python

Machine Learning In Python

The use of big data has made it possible for machine learning developers to leverage their existing programming skills in order to create new programs. Machine learning is a subset of computer science that refers to the study of software algorithms that yield useful results from relatively small amounts of data. Algorithms are very specific forms of programming and are typically used to solve problems by searching through potentially terabytes of data. In recent years, machine learning has found a diverse array of applications in fields such as retailing, content development, and financial markets.

  • Visual Representation 
  • Data Mining 
  • Data Visualization 
  • Supervised Learning 

Visual Representation: 

Machine learning can be implemented on a desktop computer by requiring developers to use a data visualization tool to create a visual representation of their program’s inputs and outputs. Visualization tools can include libraries such as PyCharts or Scikitch to create chart types like bars and pie charts. These types of visualizations can be easily manipulated using keyboard shortcuts or mouse movement. A popular visualization tool for Python is the Scikitch library, which allows for easy creation of scatter plots and neural networks.

Data Mining: 

Data mining is the process of discovering previously unknown or hard to find patterns and relationships between known data items. This concept is heavily used in marketing and business to provide better insight into product demographics. For example, if you’re interested in finding out the most popular brands among teenage girls, you would look at their usage in television advertising and different media advertisement. By gathering all the data and determining relationships between attributes of brands and consumer behaviors, marketers are able to create targeted campaigns that are successful in engaging consumer audiences. Data mining is heavily used in Python programming in the field of data mining.

Data Visualization: 

Data visualization is only one facet of machine learning that can be implemented in Python programming. Another popular concept is the decision tree. This concept is similar to what is described above in data mining. However, a decision tree allows for the data mined from the program to be visualized using a hierarchy of nested nodes. Graph visualization provides more detailed representation of the data mined in a decision tree.

Supervised Learning: 

The final main concept in the list of top machine learning algorithms used in Python programming is supervised learning. This concept refers to the training of an artificial neural network (ANN), which is a system that contains a feeder application that allows it to receive data or inputs, without any knowledge from the user. Once learned, the user is completely dependent on the accuracy of the output. One example of an informative ANN is the Google news API, which serves news feeds to users based on certain keywords. Because this feeder service is entirely voluntary, Google has come up with a great way to ensure its quality by flagging unacceptable content and manually adding new news items as they become relevant.

Although machine learning algorithms used in Python programming comes with an enormous advantage over traditional approaches, there are also some disadvantages. One disadvantage of an automated software application like Google News is that it may not be enough to make full use of the provided training data. Since the feeder application has no human interaction, it can fail to recognize or decipher relevant information, hence resulting in inaccurate or outdated results. Furthermore, the size of the News feed in particular can be a limiting factor for News applications, as it limits the amount of information to be shared.


However, the biggest weakness when it comes to using news feeds in Python programs is that the user is completely in the dark as far as how the feed actually reads. In other words, the user is not able to examine the original source code and determine how it works. Therefore, the application’s ability to make intelligent guesses is very much dependent on the input parameters. Therefore, even though news applications have an edge over their more traditional counterparts, the fact remains that if one cannot compete with the source code, one will always fall behind when it comes to accuracy.

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