Machine learning is the subfield of Artificial intelligence and it enables computers to learn on their own without being explicitly programmed. Let's get to know how machine learning works. Machine learning refers to an algorithm that is being ingested with a massive amount of data, extracts patterns out of data, and finally converts those patterns into actions.
Machine learning started as a very small concept in the beginning, and eventually, it has become part of every industry to embrace new opportunities. Let’s consider a scenario here on how machine learning helps organizations. Have you ever come across the products, appearing in front of your screen or as an advertisement while you are browsing through the internet that is being liked by you or visited by you earlier? Probably you may have!
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You will see or receive product or service suggestions just because of the machine learning algorithms that work behind the scenes. The ultimate goal of these algorithms is to fulfill the final goal of any organization. That's how machine learning induces people to buy the products and services of an organization and helps the organizations in making profits. Your Facebook will show you the news feed that suits your requirement, isn’t it? It is just because of the machine learning algorithms, not only it happens with Facebook but also with all other social media platforms and e-commerce sites.
We have come to know how machine learning application works, and along the way, it is also creating a ton of employment opportunities in the field of IT for data scientists and software engineers. To kick start your career in machine learning or to be a part of this futuristic technology, you need to learn some specific skills Required for Machine Learning, and the pay scale for the machine learning developer is also very high compared to other jobs.
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Here below mentioned are the skills to begin with and advance your career in machine learning.
Below mentioned are the basic skills one should learn to excel in one career in machine learning technology.
Tools and tables are very essential in machine learning to create models from data. Statistics and its branches such as analysis of variance and hypothesis testing are crucial for building algorithms. As machine learning algorithms are built upon statistical models, we can understand how important statistics is for machine learning is. That's how statistics play an essential role in developing the algorithms. So it is very vital to gain knowledge on statistical tools to speed up your career in machine learning.
Probability helps in predicting future consequences, and the majority of the algorithms in machine learning work based on uncertain conditions and have to make reliable decisions. Mathematical equations in probability such as derivative techniques, Bayes Nets, and Markov decisions would help machine learning in predicting the future.
The critical task of machine learning is to analyze the unstructured data models, which requires the science of data modeling. Data modeling enables identifying the underlying data structures, finding out the patterns, and filling the gaps between the places where data is nonexistent.
Having explicit knowledge of data modeling concepts would help in creating efficient algorithms.
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We are all aware that machine learning mostly depends on algorithms, which means one should possess sound knowledge of different programming languages. Dealing with machine concepts is entirely different from handling small kinds of things such as creating simple applications, scripting web pages, etc. It demands some extra knowledge and skill of programming. Below mentioned are the skills which you require to become a professional in machine learning.
Machine learning involves computation on large data sets; hence one should possess strong basic fundamental skills such as computer architecture, algorithms, data structures, complexity, etc. Getting in-depth into the programming books and exploring new things will be a good advantage. Sign Up for some courses to sharpen your programming skills and to advance your knowledge.
As a machine learning engineer, you need not reinvent the wheel, all you need to do is using the algorithms and libraries that are developed by other developers and organizations. There are a wide variety of API Packages and libraries such as Microsoft’s CNTK, Apache Spark’s MLlib, and Google TensorFlow. Applying these technologies will require a clear understanding of different concepts, how they can be integrated with different systems, and the pitfalls that arise along the road.
Machine learning should be integrated with various other technologies to leverage its full potential. As a machine learning engineer, you need to develop algorithms and systems that can easily integrate and communicate with the other existing technologies. Hence you are required to have strong skills in Application User Interface (APIs) of various flavors (web APIs, dynamic and static libraries, etc.), and also designing interfaces that would sustain future changes.
One should certainly require programing knowledge skills to deal with machine learning projects, but the problem is which language one should learn? To clarify this doubt let’s get into the details. Machine learning is not limited by any specific programing language, and it's more like object-oriented language. It would be developed in any language which can meet the required components.
Let’s discuss how each programming language listed below helps in developing machine learning.
Python is one of the widely used programming languages in many fields; in fact, it is mostly preferred by data scientists and machine learning engineers. It is equipped with a wide range of useful libraries which help in processing data efficiently and in scientific computing.
Python also supports a number of machine learning libraries such as Theano, TensorFlow, and sci-kit- learn, and these libraries make training algorithms very easier.
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R is a programming language developed for statistical computing and data mining, and these features enable it to be a perfect fit for machine learning tasks. There are many statistical models and algorithms available in R for different computing tasks. When it comes to the syntax of R, it’s a bit different from other languages but is simple to learn.
C/C++ is generally used to program the interface and to develop the mechanisms of machine learning. The libraries available in all other languages can be firstly developed in C/C++ and can be made available in all other languages with the help of API calls. C/C++ languages are mostly used to develop embedded systems, and it will help you to a great extent if you are planning to get into the programing of Smart homes, smart cars, and sensor devices.
It becomes difficult for novice programmers when compared to all other programming languages, but it has some great ML libraries in C/C++ such as LibSVM, Shark, and mlpack.
One should develop strong research skills on topics that are relevant to machine learning. Attending conferences, following news on machine learning, being aware of the changes to different components of machine learning would help you in gaining constant knowledge. Being aware of the changes that are happening in the machine learning field would help you in staying at the top of the game.
Reading will provide you with immense knowledge on the topic, to gain more knowledge on machine learning concepts. There are various free books available on the internet which are loaded with the right amount of information. Read research papers of Google file system, Google Map-Reduce, Google Big Table, etc.
The technology is evolving to new heights each day, and machine learning has been multiplying in terms of its growth. Organizations across the world are heading towards the implementation of Artificial Intelligence and machine learning in their organizations to scale up. This trend shows how much role machine learning will play in the future of online services and mastering the required skills will place you on a path where opportunities are limitless.
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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