What One Should Know About Machine Learning?
If you focus on the trends of content marketing of large IT companies, you can get the impression that the concept of artificial intelligence and machine learning in some unknown, almost miraculous way were born only two or three years ago. This, of course, is not true.
What One Should Know About Machine Learning?
Programmers and researchers used the term «artificial intelligence», as you know, in the 1950s to describe machines capable of adequately solving abstract problems without the mediation of man. In addition, machine learning is one of the most well-known methods of realizing the possibilities of artificial intelligence.
Machine learning is based on the creation of algorithms that have the built-in ability to recognize patterns when analyzing large data sets and use them for self-learning.
Why a career in the area of machine learning is a good choice?
The Netflix and Amazon examples clearly illustrate how smart, due to the usage of machine learning, can be just as clever as humanly intelligent. When Netflix, as if reading your thoughts, offers you exactly those movies and serials that will certainly interest you, – ML is behind this.
Thanks to machine training, Amazon so successfully convinces you to buy something else in the load to the product you are purchasing. To offer exactly those products that you will like, you use sophisticated algorithms that process terabytes of data. Try to imagine how many business cases exist in which ML can change the status quo.
It is obvious that machine learning is an attractive sphere both for those who are just preparing to choose a profession and for those who are dreaming about a new round of IT career.
Flexible approach to programming languages
Most machine-learning enthusiasts worry about learning Python or R? To develop programs based on machine learning algorithms, these two programming languages are actually used most often. However, developers focused on long-term career success, this question should not be asked.
— The unique advantages of some programming languages make it easier to solve AI-related problems, but you do not want to torpedo a project?
— With access to libraries, it is not so difficult to develop ML-based programs in any programming language
— Some of the ML-based technologies cannot cope with certain tasks, and in this case, the programmer must be able to find an alternative
— In addition, new approaches to the implementation of ML algorithms are emerging. To «stay in the game», you need to be able to adapt to the situation and master technology as it changes
Probability and statistics
The foundations of probability theory are the core of machine learning. Bayes’ theorem, conditional probability, likelihood function, independent and mutually conditioned events – anyone who dreams of interesting work in the field of machine learning, it is necessary to understand these elements of probability theory.
After all, these concepts are the scientific basis for solving the problem of uncertainty in the algorithms of machine learning. A mathematical expectation, median, mode, variance, binomial distribution, etc. – any programmer or data analysis specialist should own these concepts because otherwise, he will not be able to check the operation of ML algorithms and improve them.
Many algorithms of machine learning represent a logical continuation of the procedures of statistical modelling.
Data modelling is the definition of the basic structure of complex arrays. The effectiveness of the algorithm of machine learning depends on whether there are useful patterns in this array: for example, correlation, eigenvector, and categorization. ML is based on continuous improvement of data models.
Depending on what degree of tolerance to the error lies in the application for which you are developing the model, a decision is made about the degree of accuracy and the degree of error. Iterative learning algorithms should be able to modify the model depending on the measure of error. Without basic knowledge in the field of data modelling, it will not be possible to develop even the simplest algorithm.
Why you need to master libraries machine learning
Good news: For most of the basic and standard implementations of ML-solutions, there are libraries, APIs, software packages. However, to succeed in working on ML-projects, it is necessary:
— be able to choose the appropriate model (nearest neighbour, decision tree, neural network, a compilation of several models)
— be able to choose the appropriate training procedure for a particular data type
— understanding of how hyper-parameters affect the learning algorithm
— be able to assess the pros and cons of different approaches
Those enthusiasts, who want to understand all the nuances of machine learning, should visit the Kaggle site, which contains a lot of interesting material on ML and data analysis, including practical tasks.
Skilful data handling
It is important to understand that data is always more important than algorithms. Based on the data, you can implement an outstanding program – even if the algorithm is basic.
So all those who want to build an IT career, working with ML, will first have to study data management, data organization, data analysis and only then dive into the development of advanced algorithms. Because programmers specializing in machine learning spend a lot of time transforming data, this is a key aspect of their work.
Distributed data processing
The technology of machine learning is associated with the processing of big data processing. Programmers could hardly perform such a huge amount of work, using the resources of only one computer.
The solution to the problem is distributed processing of data. Any experience in this area can be invaluable. Apache Hadoop and Amazon EC2 offer successful solutions for distributed data processing, so you probably would be useful to get acquainted with at least their basics.
Machine learning is the only technology capable of running applications with enough powerful potential to revolutionize the functioning of entire industries. Companies around the world are already investing huge amounts of money in the development of ML-based services for internal use.
Thus, there are more and more new vacancies; a wide range of job opportunities opens up. Follow the tips outlined in this article to learn machine learning and plan a new career cycle.