It is all about extracting knowledge from data. Machine Learning is a research field at the intersection of statistics, artificial intelligence, and computer science and is known as predictive analytics or statistical learning. In recent years, the application of machine learning methods has become universal in everyday life. Now, anyone can easily find it from automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing yourself or your friends in your photos. Many modern websites and devices have machine learning algorithms at their core. Every time when you look at a complex website like Facebook, Amazon, or Netflix, it is likely that every part of the site contains multiple machine learning models. As we know, machine learning uses many algorithms for constructing mathematical models and making predictions using ancient facts or information. Nowadays, it is being used for many tasks along with photo recognition, speech popularity, e-mail filtering, and plenty of more.
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Why Machine Learning?
Everyone knows humans are the most intelligent species on the earth. But with passing time, he needs something which is more intelligent than him, possibly for his/her protection. He always wants machines that follow his instructions and do some specific task.
As we know, in the early days of “intelligent” applications, many systems used hand-coded rules of “if” and “else” decisions to process data or adjust to user input. Now just think of a spam filter whose job is to move the incoming email messages to a spam folder. You could make up a blacklist of words that would cause an email to be marked as spam. We can take this as an example of using an expert-designed rule system to design an “intelligent” application.
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Nowadays, almost every smartphone can detect a face in an image. Yet, face detection was an unsolved problem until as recently as 2001. One of the major problems is that how pixels are “perceived” by the computer differs from how humans perceive a face. This difference in representation makes it basically impossible for a human to come up with an excellent set of rules to describe what makes up a face in a digital image. Using machine learning, however, simply presenting a program with a comprehensive collection of images of faces is enough for an algorithm to determine what characteristics they need to identify a face.
So, simply we can say that there are a lot of things that take enough time for a human to get done. But the machine took just a few seconds to get it done. Even the tasks which are too complex for human beings, they can do it easily and in a few seconds.
How Machine Learning Works?
Machine Learning is nothing but a form of artificial intelligence that teaches computers to think similarly to humans. Basically, by learning and improving the experience. It works by exploring data and identifying patterns and involves the least human obstructions. Simply, we can say any task which can be completed with a data-defined pattern or even with a set of rules can be automated with machine learning. By using these, some tasks that before only humans can do, now machines are doing easily. From reviewing resumes to Facebook ads. Basically, there are two main types of machine learning:
- Supervised Machine Learning
- Unsupervised Machine Learning
Supervised Machine Learning It is one of the most commonly used and successful types of machine learning.
We use supervised learning whenever we need to predict a certain outcome from an input, and we have examples of input/output pairs. Usually, we build a machine learning model from these input/output pairs, which comprise our training set. And our primary goal is to make accurate predictions for new, never-before-seen data. It mostly requires human effort to build the training set but afterward automates and often speeds up an otherwise laborious or infeasible task.
We mainly solve two problems using supervised machine learning:
Classification and Regression.
- i) Classification
It sometimes separates classification into binary classification, which is the special case of distinguishing between exactly two classes, and multiclass classification, which is classification over two classes. Simply, you can think of binary classification as trying to answer a yes/no question. Moreover, classifying emails as either spam or not spam is an example of a binary classification problem. We find it in this binary classification task, the yes/no question being asked would be “Is this email spam?”
- ii) Regression
For regression tasks, the goal is to predict a continuous number or a floating-point number of programming terms. The basic example of regression is Predicting a person’s annual income from their education, their age, and where they live. Now while predicting income, the predicted value is an amount and can be any number in a range. Another beautiful example of a regression task is predicting the yield of a corn farm given attributes such as previous yields, weather, and the number of employees working on the farm. The yield again can be here an arbitrary number.
Unsupervised Machine Learning
It subsumes almost all kinds of machine learning where there is no known output, no teacher to instruct the learning algorithm. Basically, there are two types of unsupervised machine learning: Transformation of Dataset and clustering.
- i) Transformation of Dataset
These are basically the algorithms that create a new representation of the data, which might be easier for humans or other machine learning algorithms to understand compared to the original representation of the data. A common application of unsupervised transformations is dimensionality reduction, which takes a high-dimensional representation of the data comprising many features, and finds a new way to represent this data that summarizes the essential characteristics with fewer features. And a very common application for dimensionality reduction is a reduction to two dimensions for visualization. Another application for unsupervised transformations is finding the parts or components that “make up” the data.
Let’s consider an example of this- topic extraction on collections of text documents. Here, the primary task is to find the unknown topics that are talked about in each document and to learn what topics appear in each document. This can be useful for tracking.
- ii) Clustering
It helps in partitioning data into distinct groups of similar items. Now let’s consider the example of uploading photos to a social media site. Simply to allow you 133 to organize your Pictures, probably the site wants to group together pictures that show the same person. However, the site doesn’t know which pictures show whom, and it doesn’t know how many people appear in your photo collection. And possibly, a sensible approach would be to extract all the faces and divide them into groups of faces that look similar to each other. Hopefully, these correspond to the same person, and they can group together the images for you.
Now you might get a bit of an idea of how machine learning works. But the question is? Why Python for Machine Learning?
Why Python for Machine Learning?
Nowadays, Python has become the acronym for many data science applications. And it combines the power of general-purpose programming languages with the ease of use of domain-specific scripting languages like MATLAB or R. Furthermore, Python has libraries for data loading, visualization, statistics, natural language processing, image processing, and more. Thus, this vast toolbox provides data scientists with a large array of general- and special-purpose functionality. One of the major advantages of using Python is the ability to interact directly with the code, using a terminal or other tools like the Jupyter Notebook. Machine learning and data analysis are iterative processes in which the data drives the analysis. And it is also essential for these processes to have tools that allow quick iteration and easy interaction. Moreover, Python also allows for the creation of complex graphical user interfaces (GUIs) and web services and for integration into existing systems.
Hope this article helps you a lot for understanding this topic. If you’re interested in free online courses with certificates, So enroll today on Great Learning Programme.
