What Skills to Develop for a Career in Machine Learning

The new iPhone X announcement this month confirms the consumerization of machine learning is upon us.This only accelerates the hotly in-demand area of technological development. Nonetheless, carrying the around a machine learning (ML) device in your pocket signals that the space is on the cusp of a renaissance that has been brewing since around 2012.

To begin work in any facet of ML or artificial intelligence (AI) development, a well-rounded core set of skills helps candidates break into the field. Much of the ML work available involves a data science background, which, by no coincidence is the most in-demand professional in the job market. A machine learning engineer with 3-5 years of experience can expect to command a $150,000 annual salary.

Data science and OOP drives the field

Data science is the foundation for machine learning. The ‘brain’ of machines operate with programs informed by immense and dynamic data structures. The algorithmic centerpiece drives ML programs determines how well the machine transforms queries (input) into answers (output). Ideal candidates demonstrate fluency in programs used for searching, sorting, crawl optimization, and dynamic programming, which involves adding previous query-answer outcomes into the dataset. Naturally, nearly all  jobs in ML and AI require SQL aptitude.

Programming languages for controlling computer hardware are important too. The insides of machine learning devices operate with the same binary language computers use to function. Controlling system memory, cache in CPU and silicon chips, bandwidth for networked devices, virtualized distributed processing skills remain central in the machine learning context. Server side languages like Python commonly are among the skills listed on ML jobs. Programming embedded controllers and microprocessors requires aptitude in object oriented programming, thus opportunities abound for those with a grasp of OOP languages like Java and C+/C++.

Pure mathematics skills in demand

Some of the highest paying jobs in machine learning are geared for candidates of with strong backgrounds in traditional mathematics, namely statistics and probability. The integral role that statistical mathematics plays in machine learning logic creates demand for specialists in this area. Those with expertise in the Markov mathematics (ie. Decision Process and Hidden Models) play an important role in driving development for ML and AI projects. If you have backend server experience (Python or proprietary equivalent  to match) opportunities abound.

Additionally, a background in understanding the variations and distributions Analyze with ANOVA, hypothesis testing. Data Modeling and Evaluation belie the critical thinking written into ML. The learning that takes places inside a machine is performed by various types of modeling and evaluation functions. The ability to apply the math principles within ML libraries and APIs used in machine learning gives candidates a leg up in the field. Specifically useful is expertise in Bayesian networking and graphical modeling with neural nets. In this area, stats and probability skills combined with OOP languages Spark/Java/Scala rank high on the list of demands for AI/ML professionals.

Deep-learning specific frameworks

Even though consumerized ML is new to the market, it has been around long enough to have its own coding framework. As a matter of fact, there are a handful. Most senior level ML jobs want to see proficiency in at least one:

• TensorFlow - an open source software library for numerical computation using data flow graphs.
• Caffe - a deep learning framework made with expression, speed, and modularity in mind developed at UC-Berkeley.
• Theano - a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.
• Torch - a scientific computing framework built to utilize graphics card hardware (CUDA), with wide support for machine learning algorithms.
• CNTK - Microsoft’s open-source framework for ML.
• MxNet - Apache-integrated neural networks framework for ML