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Machine learning (ML), the subset of Artificial Intelligence (AI) that enables computers to “learn” to perform tasks they haven’t been explicitly programmed to do, took huge leaps in 2016.
Basically, machine learning refers to algorithms that ingest huge amounts of data, extract patterns from that data and turn those patterns into actions. It is now being employed in a vast number of industries to improve efficiency and open up new possibilities. When you see an advertisement on a website that seems aligned to your needs and tastes, it’s machine learning doing its magic. When Amazon makes suggestions about what other products you might be interested in buying, a machine learning algorithm is at work behind the scenes. The same goes for your Facebook newsfeed, and countless more every-day examples. ML is also slated to do much more in the future, such as fighting cybercrime and even running beauty contests.
And naturally, as is the case with every technology that starts to gain traction and become widely adopted, machine learning is creating a ton of IT job opportunities, especially for software engineers and data scientists. The average salary for AI and machine learning talent is north of $100K, and in some cases on par with NFL quarterbacks.
Here are the skills needed to begin and advance your machine learning career path:
As a conglomeration of math, data science and software engineering, machine learning will require you to be proficient in all three fields. Whether you’re a self-taught programmer or an engineer with a degree from MIT, you’ll do well to test your mettle in these domains:
Most machine learning algorithms are about dealing with uncertainty and making reliable predictions. The mathematical tools to deal with such settings are found in principles of probability and its derivative techniques such as Markov Decision Processes and Bayes Nets.
Also of importance are tools and techniques that enable the creation of models from data. Relevant to this task is the field of statistics and its various branches such as analysis of variance and hypothesis testing. Machine learning algorithms are often built upon statistical models.
Machine learning often involves analyzing unstructured data, which relies on the science of data modeling, the process of estimating the underlying structure of a dataset, finding patterns and filling gaps where data is nonexistent.
Understanding data modeling and evaluation concepts is key to creating sound algorithms that can be trained and enhanced over time.
Machine learning is about creating dynamic algorithms, which means your programming and software development skills will be put to test. This is much different from scripting web pages and creating simple windows applications. You’ll have lots to do with the fundamentals of analysis and design. Here’s what you need to know.
Machine learning is a field that involves performing computation on huge sets of data, and therefore it requires proficiency in fundamental concepts such as data structures, algorithms, complexity and computer architecture.
This is a good opportunity to take out those semester books and review your stacks, b-trees, sort algos, or skim through your programming book and solve a few parallel programming problems.
Alternatively, you can sign up with a service like HackerRank, and work your way through its select challenges for machine learning programming.
As a machine learning engineer, you’ll have to create algorithms and systems that integrate and communicate with other software components and ecosystems that are already in place. That is why you’ll need a strong background in Application Programming Interfaces (API) of different flavors (web APIs, static and dynamic libraries, etc.) as well as designing interfaces that will sustain future changes that will overcome your overall system.
Creating reliable and flexible software requires mastering skills such as requirements analysis, developing use cases and test cases, documentation and testing.
Fortunately, you don’t need to reinvent the wheel. Part of your job as a machine learning engineer will be to use algorithms and libraries created by other developers and organizations. There are already a lot of packages, APIs and libraries you can use such as Google’s TensorFlow, Microsoft’s CNTK and Apache Spark’s MLib.
But applying them effectively will require understanding models and learning procedures, how they apply to each technology, and the potential pitfalls.
A good starting place to get the hang of programming machine learning algorithms is Kaggle, an online platform to learn and hone your ML skills.
As a theory and concept, machine learning is not bound to any specific language, and like object oriented programming, it can be implemented in virtually any language that has the required components and features. In fact, there are ML libraries available in different programming languages.
However, there are slight nuances in what each programming language is more suitable for and the complexity involved in putting it to use in ML projects.
C/C++ are almost as low level as you can get in programming. They’re especially suitable for developing software that is memory and speed critical such as operating systems and networking protocols. They’re also hardware interfacing programs. These are usually the languages used to program the infrastructure and mechanics of machine learning engines. A number of ML libraries available in other languages are actually developed in C/C++ and wrapped around with API calls to make them available in those languages.
C/C++ are also the language of choice in most embedded systems, so if you’re planning to get into smart cars, smart home and sensor programming, C/C++ skills are indispensable.
But given the lack of automated garbage collection and memory management found in other languages, C/C++ can make creating complete machine learning systems difficult, especially for novice programmers.
Nonetheless, there are some good ML libraries available for C/C++, including LibSVM, Shark and mlpack.
R is a language that has been specifically tailored for statistical computing and data mining, making it an excellent choice for machine learning tasks. There’s a huge repository of algorithms and statistical models developed in R for various tasks.
The syntax is a bit different from other traditional languages, but it’s not difficult to learn.
Python is one of the favorite languages of data scientists and machine learning engineers. Although a general purpose language, Python has a number of useful libraries (NumPy, SciPy and Pandas) for efficient data processing and scientific computing.
Python also has a number of specialized machine learning libraries (e.g. scikit-learn, Theano and TensorFlow) that make it easy to train algorithms using different computing platforms.
As the trends show, 2017 will be an even bigger year for machine learning. Diverse industries are ripe for innovation in the field, and the need for experts and engineers is increasing.
Machine learning will have a serious role in shaping the future of online services. By mastering the skills required to enter the ML space, you’ll have a chance to be part of that future.