Introduction to Machine Learning

Introduction to Machine Learning -magoosh

Machine learning is a branch of Artificial Intelligence, commonly known as AI, wherein the data is analysed and is fit data models, which can be later used for predictions.

The goal of machine learning is to partially or fully automate the solution of complex professional tasks in various fields of human activity.

Machine learning has a wide range of applications, some of them include speech recognition, gesture recognition, handwriting recognition, spam detection, credit scoring, fraud detection, bioinformatics, medical diagnostics, etc.

The scope of machine learning applications is constantly expanding. Ubiquitous informatization leads to the accumulation of huge volumes of data in science, production, business, transport, health care. The resulting tasks of forecasting, management and decision making are often reduced to learning by use of precedents. Earlier, when there were no such data, these tasks were either not raised at all, or they were completely solved by other methods.

Methods of Machine Learning

There are two most popular methods of machine learning — Supervised learning and Unsupervised learning. In supervised learning, example input data, along with the corresponding output data, is fed to the machine learning model. In unsupervised learning, only the input data is fed to the model and the corresponding output data is absent. The model finds the underlying structure within the input data on its own in unsupervised learning.

Supervised Learning

In supervised learning, the machine learning model is provided with both the input data and the corresponding labelled output data. The model on the provided data, and then its performance is tested using validation methods, for example, K-fold cross-validation. The machine learning model is tweaked accordingly such that it becomes fairly accurate, but is not overfitted.

For example, suppose you have a dataset of images flowers — rose, sunflower, tulip, along with their respective labels. You can feed this data to a supervised learning algorithm. Once the machine learning model is trained, you can then query an image of one of the above-mentioned flowers and the model will predict the name of the flower present in the image with certain confidence.

Unsupervised Learning

In unsupervised learning, the data provided as input to the machine learning model is unlabelled. The algorithm is designed in such a way that it is able to find commonalities among the input data points.

Unsupervised learning is like learning without the help of a teacher. The machine learns through its observations and is able to find an underlying structure in the unlabelled input data.

Clustering is a very common example of unsupervised learning. Suppose there are several types of molecules, some of them are drug molecules are some of them are not. An unsupervised learning clustering algorithm will make clusters of similar types of molecules. When there is a query about a new molecule, the algorithm will check the molecules of which cluster are most closely related to the new molecule, and accordingly, the nature of the new molecule will be predicted.

Programming Languages

There are several programming languages that can be used to write machine learning programs. You must choose the language to specialize in with machine learning depending on the demand in the market, or the requirement in the company you wish to work in.

Python is a very popular programming language for machine learning as it has many good libraries. TensorFlow, Keras, PyTorch are some of the very good deep learning frameworks of Python. The scikit-learn library is a very popular Python library for machine learning.

In robotic applications or games, C++ is used for machine learning and artificial intelligence programming. Mlpack and Dlib are some of the popular C++ machine learning libraries.

Deeplearning4j is an open-source and distributed deep learning library that is written for Scala and Java. Weka is a collection of several machine learning algorithms that can be used for tasks related to data mining. Java is generally not the first choice of any newbie in machine learning, but is preferred by those who already have an experience in Java and wish to apply machine learning too.

This blog post gave you a brief introduction to Machine Learning. It is a huge field and has a lot to learn. Stay tuned to Magoosh data science blog to learn more about machine learning!

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