Data Science for Beginners: Machine Learning

Data Science for Beginners: Machine Learning

As is revealed in discussions over at runrex.com, as a result of the high amount of data production by applications, the increase of computation power in the last couple of years, as well as the development of better algorithms, we have heard more and more about Machine Learning. It has now become part of our daily lives with industries in every sector trying to benefit from Machine Learning. Devices like Fitbit or intelligent home assistants like Google Home utilize Machine Learning. Given that Machine Learning is around us, and that as an aspiring data scientist, you will need to be familiar with the concept according to the gurus over at guttulus.com, you must know what it is all about. This article will look to help by highlighting some of the things you need to be aware of as far as Machine Learning is concerned.

What is Machine Learning?

As outlined over at runrex.com, Machine Learning is a subfield of artificial intelligence, AI, whose goal is to understand the structure of data and fit that data into models than not only be understood but can also be utilized by people. Machine Learning differs from traditional computational approaches in that while algorithms are sets of explicitly programmed instructions used by computers to calculate or problem-solve in traditional computing, Machine Learning algorithms allow for computers to train on data inputs and use statistical analysis to output values that fall within a specific range instead. This means that Machine Learning facilitates computers in building models from sample data to automate decision-making processes based on data inputs. It is also worth pointing out that Machine Learning is a continuously developing and evolving field, something you have to consider when working with Machine Learning methodologies.

What are some of the Machine Learning Methods?

As is explained over at guttulus.com, in ML, tasks are generally classified into broad categories based on how learning is received or how feedback on the learning is given to the system developed. The two most widely adopted Machine Learning methods are:

• Supervised Learning

Supervised Learning, from discussions over at runrex.com, trains algorithms based on example input and output data that is labeled by humans. The computer is provided with example inputs that are labeled with their desired outputs. It is designed to help the algorithm “learn” by comparing its actual output with the “taught” output to find errors and then modify the model accordingly. Supervised Learning uses patterns to predict label values on additional unlabeled data, with an example being the use of historical data to predict statistically likely future events.

• Unsupervised Learning

Unsupervised Learning provides the algorithm with no labeled data to allow it to find structure within its input data. Here, data is unlabeled, which means that the learning algorithm is left to find commonalities among its input data. The gurus over at guttulus.com point out that unlabeled data are more abundant than labeled data which makes Machine Learning methods that facilitate unsupervised learning very valuable. It is commonly used for transactional data and is often used to detect anomalies including for fraudulent credit card purchases, as well as recommender systems that recommend to customers what products they should buy next.

What are some of the popular approaches to Machine Learning?

Given that ML is a continuously developing field, it is worth pointing out that approaches to Machine Learning are also continuously being developed. The following are some of the most popular approaches being used in Machine Learning currently.

• K-nearest neighbor

This algorithm is a pattern recognition model that can be used for classification as well as regression. Regression, from discussions over at runrex.com, is used to examine the relationship between one dependent and one independent variable. This algorithm is often abbreviated as k-NN, with the ‘k’ in k-nearest neighbor being a positive integer that is typically small.

• Decision Tree Learning

Generally, decision trees are employed to visually represent decisions and show or inform decision making. When it comes to Machine Learning and data mining, decision trees are used as a predictive model, and these models map observations about data to conclusions about data’s target value. Decision tree learning is aimed at creating a model that will predict the value of a target based on input variables.

• Deep Learning

This attempts to imitate how the human brain can process light and sound stimuli into vision and hearing. Therefore, a deep learning architecture is inspired by biological neural networks and is made up of multiple layers in an artificial neural network made up of hardware and GPUs. Deep learning uses a cascade of nonlinear processing unit layers to extract or transform features or representations of the data with the output of one layer serving as the input of the following layer. Of all the ML algorithms currently in use, deep learning absorbs the most data and has been able to beat humans in some cognitive tasks, which has made it the approach with the most potential in the AI space.

What are some of the programming languages used in Machine Learning?

Finally, we are going to highlight some of the commonly used programming languages as far as ML is concerned. They include:

• Python- It is the most sought-after programming language in the Machine Learning field because of the increased development of deep learning frameworks available for this language as per the gurus over at guttulus.com. It has a readable syntax as well as the ability to be used as a scripting language which is why it is both powerful and straightforward for preprocessing data and working directly with data.
• Java- Although it is not the first choice for those who are new to programming and want to learn Machine Learning, it is preferred by those who have a background in Java development to apply ML. It tends to be used more than Python for network security, including fraud detection and cyberattack cases.
• R- This open-source programming language is used primarily for statistical computing. Although it is not usually used in industrial production environments, it has risen in popularity as far as industrial applications are concerned because of increased interest in data science.
• C++- This is the programming language of choice for Machine Learning and AI in game or robot applications. Because of their proficiency and control in C++ and C, embedded computing hardware developers and electronics engineers are more likely to favor these two programming languages.

The above tries to cover some of the things you should know about Machine Learning, but it barely scratches the surface as far as this topic is concerned, which is why you should check out the excellent runrex.com and guttulus.com if you need more information on the same.