The term machine learning was first used in 1959 by Arthur Lee Samuel. The term was a by-product of research in pattern recognition and computational learning theory. Machine learning is a branch of Artificial Intelligence that makes a machine capable of making its own decisions.
What is Machine Learning and its Uses
In layman’s language, machine learning provides the capability for the computer to behave like an independent human being in decision making. Machine learning uses statistical and mathematical techniques aide computer systems to learn on their own. They do this by accessing the data at its disposal. As a result, by building such capability into a computer system, the programmer doesn’t have to make changes in the code everytime a different situation arises.
Machine learning in the real world
We all use the revolutionary technology in our daily lives but are not aware of its existence.
Email spam filtering
Certain emails are classified as spam automatically without the user having to categorize them. This process happens by implementing the machine learning concept of classification. In this method, the system identifies keywords to categorize the emails as spam.
The revolutionary Siri, Google Assistant, Alexa, and Cortana all use machine learning to assist the users. The technology saves and refines data based on the assistant’s encounter with the user. With this in mind, it then uses the experience to develop new skills.
When looking for a way to commute between two places, maps help us figure out the fastest route. Under these circumstances, the process uses data collected over time. This analysis of data provides an insight into the traffic conditions in a particular area during a specific period. Moreover, the technology also helps predict the time of peak traffic in any given area.
The ride estimate when booking a cab between places is also courtesy of revolutionary technology. The algorithm analyzes the shortest and fastest way along with the demand. As a result, it then gives the rider an estimate of the cost of the commute.
Video footage surveillance
Viewing multiple video feeds could become tiring for human eyes. Consequently, this can result in missing important details. In light of this, machine learning algorithms ensure no detail goes unnoticed. It then raises an alarm on coming across suspicious footage.
Machine Learning Algorithms
Algorithms are broadly classified into three different categories:
In supervised learning, the machine is trained with a huge data set to create a model. The model created helps predicts the outcome based on new data provided. Programmers call this form of training supervised learning.
They further divide supervised learning into two subcategories:
When the result of a problem is expected to be continuous, this technique comes into play. For example, predicting the value of a stock based on input from the market. The value of a stock can vary between a few cents to many dollars. Furthermore, it can take any value in between. So, to predict these kinds of values, we use regression algorithms.
When a particular problem can result in two or more classes, the classification algorithm is helpful. For example, whether the mail is spam or not? Does the picture contain a cat or not? Therefore, such problems with two or more specific solutions use classification algorithms.
In this form of learning, the machine is not trained in the beginning to identify the correct set of results. In contrast, the machine learns on its own as it collects and analyzes data. Unlike supervised learning, the data used here unstructured and noisy. As a result, this can lead to a distortion in the data. However, unsupervised learning helps categorize various articles based on content. A technique known clustering makes implementation possible. However, the technique is difficult to implement. Therefore, it’s not as widely used as its supervised counterpart.
The most popular unsupervised learning algorithm is:
The algorithm uses the similarities between data to cluster them into groups. For example: division of news reports based on content, clustering a set of tweets based on information they hold, or clustering a set of images based on the objects in the background.
The most intense subcategory of machine learning is reinforcement learning. The basic principle behind reinforcement learning forms the core of artificial intelligence. In this case, the machine learns from its own experience after performing a specific task. Based on feedback after performing the task, it then categorizes the action as good or bad. It then builds on this experience. The ultimate goal in this form of learning is maximizing actions to gain long-term rewards.
For instance, the best example of reinforcement learning that many people may know is the virtual chess game. In any match against the computer, the machine learns while playing. As a result, it develops a strategy based on the outcome of its previous action. The ultimate reward, in this case, is a victory over its opponent.
Coursera Machine Learning Offerings
Coursera an educational startup that took the world by storm in 2012. Stanford professors Andrew Ng and Daphne Koller founded the company. The company offers courses to promote remote learning. The company offers a wide variety of certifications in trending technologies. Moreover, Coursera offers master’s degrees in various fields. It offers as many as 1,479 on-demand courses in the field of machine learning itself.
Andrew Ng, the co-founder of Coursera, is the teacher for the basic course offered on the site. Developed in association with Stanford University, the 11-week long course covers a wide range of topics. Subjects include the basics of machine learning to advanced concepts, such as OCR algorithms and deep neural networks. Anyone can access the content of the course free of cost, but certification costs $59.
Apart from the basic course, the company offers various advanced specialization courses. These classes cover deep neural networks, data science with python, and deep learning. Also, Coursera offers courses on using machine learning with big data.
Python Machine Learning
Created by Guido van Rossum, Python is an interpreted language first used in 1991. The main motive behind Python’s creation was improving code readability. Moreover, the language supports a rich set of libraries that provide modules for complex tasks. The language supports multiple programming paradigms: namely object-oriented, procedural, functional, and imperative. For this reason, Python is one of the most favored languages when it comes to machine learning.
The distinct feature that makes python one of the most sought-after language in the field of machine learning is the syntax. Machine learning models work dominantly with mathematical models. Python’s syntax makes it easier to work with such complex mathematical models. This gives it an edge over other languages.
Python comes loaded with a rich set of predefined libraries. This aides programmers in performing complex tasks with ease. The two main libraries that aid in machine learning are:
The NumPy package in Python offers usage of multi-dimensional arrays and matrices for storing large complex values and equations. The package includes high-level, complex mathematical functions to implement on the large arrays. These two aspects combined help in aiding machine learning coding. Additionally, the package helps analyze huge amounts of data with different characteristics.
The Scikit-learn module in Python started as a Google summer code offering. The project later went on to become a huge success after 30 expert contributors decided to pitch into the library. The Scikit-learn module accesses the SciPy library. This library consists of all the functions required for the basic scientific calculations. Furthermore, the model offers functions to create models based on data sets provided.
At the programmer’s disposal, the package offers a comprehensive range of supervised and unsupervised learning algorithms. The different algorithms available for use in the package include:
- Ensemble methods
- Dimensionality Reduction
- Manifold Learning
- Feature Extraction
- Feature Selection
- Neural Networks
- Support Vector Machines
- Naive Bayes algorithm
The future of machine learning holds great promises. Furthermore, machine learning helps companies grow by personalizing customers feeds based on their purchases. The technology opens up new horizons for the advancement of virtual assistants. Moreover, the technology can make real-time speech translation and optical character recognition a reality. The revolutionary technology will help people commute in foreign lands with ease. The technology can help realize user experience with mobile operating systems. This allows them to adapt to the customers’ usage. The technology holds infinite possibilities and is on its way to change the world for the better.