One of the objectives for Artificial Intelligence is to find solutions for various tasks without direct instructions or human intervention. This objective is achieved with the help of the method known as machine learning. In lay terms, machine learning can be explained as a branch of AI that trains computers how to learn and make decisions on their own. There are 2 types of machine learning: inductive (putting the knowledge of the humankind in a computer in a form of a database) and, which is more promising, deductive (making computers recognize patterns through the data analysis). The best language for machine learning is Python, which is rather easy to learn. Therefore, if you are the beginner in machine learning development Python will perfectly suit you.
Development of machine learning started earlier than many think – more than half a century ago. The pioneer in machine learning was Arthur Samuel, American computer scientists. In 1952, he developed a program for the IBM computer that helped the system to play checks better with each game. This event marked the beginning of the machine learning era. In 1958, Frank Rosenblatt created the first model of the neural network that imitated a human nervous system, and a year later, the neural network named MADALINE was used to solve a real problem for the first time: using an adaptive filter, it removed echoes on telephone lines. Over the last decades, machine learning has come a long way, and nowadays computers are able to produce speech, recognize faces, defeat a human in games, diagnose cancer and reveal internet fraudsters.
Why machine learning is useful?
Indeed, potential opportunities for machine learning are unlimited: it can be used wherever fast data analysis is important. Nowadays it is a fuel for such things as the Internet of Things, Smart cities, driverless cars, and many others. Machine learning is already used in many spheres of our everyday life and its further development will change our world. There is an assumption that due to machine learning people can be left out. In fact, the partial automation of decision-making process will simply change working responsibilities of people and lead to the creation of new working places.
Machine learning algorithms
The most widely adopted machine learning methods are supervised learning and unsupervised learning. Each of them includes a number of algorithms. Here are some self-learning algorithm examples that a ML developer need to know:
- Decision tree – this algorithm caters for the use of a tree graph or decision-making model, as well as the possible consequences of their work, including the likelihood of an event, resource costs, and utility.
- Logistic regression – statistical method for predicting the probability of a certain event with one or several independent variables.
- Support vector machine – a set of algorithms used for classification and regression analysis problems.
- Ensemble method – this method is based on training algorithms that form multiple classifiers and then segment new data points, starting from voting or averaging.
- Clustering algorithms – algorithms of this type cater for the grouping of objects so that the most similar elements are included in one cluster.
- Principal component analysis – which uses an orthogonal transform to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables, called principal components.
- Singular-value decomposition – a factorization of a real or complex matrix.
- Independent component analysis – this is a method of identifying hidden factors that underlie a variety of random variables, signals, and other measurements.
Machine Learning Trends in 2019
The beginning of 2019 is the right year for the implementation of machine learning in various industries, and according to Andersen’s forecasts, its usage will expand. For example, the Food and Beverages industry puts this technology into use learning to forecast which items will run out of stock within a specified timeframe. Such a forecast helps to keep a balance between the demand and supply for products like milk, mineral water or salt. In the Healthcare industry, Artificial Intelligence with the help of machine learning is now able to predict the chances of a patient’s death with a 95% accuracy. The prediction is made on the base of a patient’s health records, demographics, health history, and other factors. Therefore, it allows medical practitioners to take appropriate action in time and save millions of lives.
2019 is also the year of development for the AI assistant. Large companies like Mercedes, BMW, Google, Microsoft, Apple, and Amazon are already exploiting AI to the utmost. Along with other experts, we believe this year Artificial Intelligence and machine learning will come to several new areas and have an impact into jobs like talent acquisition, banking, finance, accounting and even intellectual jobs such as teaching.