Starting a Career in Machine Learning

Starting a Career in Machine Learning

The scientific study of algorithms and statistical models is called Machine learning. It is used by computers to effectively perform a specific task using only models and inference and not explicit instructions and is considered as a subset of artificial intelligence. In the current technological field, Machine Learning engineering is growing immensely popular.

The role of a Machine Learning engineer is not purely academic and doesn’t require aresearch or academic background. However, it requires experience in both software engineering and data science. Your basic job as a Machine Learning engineer is to develop working software for other software components to make them run autonomously, with minimal human supervision.

Prerequisites for Machine Learning engineer

You, as a Machine Learning engineer, need to understand the entire ecosystem that you are designing for. For example, if you are designing a grocery chain, you not only need to write an automated coupon generation system, but also make it work. This requires an understanding of the whole ecosystem, such as inventory, catalog, pricing, purchase order and the like. Thus, Machine Learning boils down to developing a working software.

Skills required by developers to turn Machine Learning engineers

1. Computer science fundamentals and programming

  • Data structures: Includes stacks, trees, graphs, queues, multi-dimensional arrays, and the like.
  • Algorithm: Includes searching, sorting, optimization, dynamic programming, etc.
  • Computability and complexity: Includes approximate algorithms, NP-complete problems, P vs.    NP, big-O notation, and so on.
  • Computer architecture: Includes memory, distributed processing, cache, deadlocks, bandwidth, etc.

If you aspire to be a Machine Learning engineer, you need to learn the skills to apply, implement, adapt and address the above, when programming. Hone your skills by practicing problems and entering coding competitions and hackathons.

2. Probability and statistics

At the center of many Machine Learning algorithms, lies a formal characterization of probability, which includes likelihood, independence, Bayes rule, etc. Also lying at its heart are the techniques derived from formal characterization of probability, that is, Markov Decision Processes, Bayes Nets, Hidden Markov Models, and the like. Armed with these, you will manage to deal with uncertainty in the real world.

Statistics is another field that is closely related to it that is essential for building and validating models from observed data. It provides various measures, distributions and analysis methods. Measures may include mean, variance, median, etc.; distributions may include normal, uniform, binomial, Poisson, etc.; and analysis methods, ANOVA, hypothesis testing, etc.

3. Data modeling and evaluation

With data modeling, you get to estimate the underlying structure of a given dataset. The objective being to find useful patterns that include correlations, clusters, eigenvectors, etc. It also entails predicting properties of unseen instances, such as classification, regression, anomaly detection, etc. The estimation process entails continual evaluation of the given model to find out how good it is. You need to be skilled in choosing a suitable accuracy/error measure and an evaluation strategy.

4. Applying Machine Learning algorithms and libraries

You can easily procure standard implementations of Machine Learning algorithms through libraries, packages and APIs, such as H2O, scikit-learn, Spark MLib, Theano, TensorFlow, etc. However, applying them effectively requires selecting a suitable model, such as neural net, decision tree, support vector machine, nearest neighbor, ensemble of multiple models, etc.

You will also need to acquire a learning procedure to fit the data, such as gradient descent, genetic algorithms, linear regression, bagging, boosting and other model-specific methods. Finally, you need to have an understanding of how hyper parameters affect learning.

5. Software engineering and system design

As a Machine Learning engineer, you are expected to deliver software, which may be a small component of a large ecosystem of practices and products. So, gaining an understanding of how these different pieces work together is important, as also communicating with them via library calls, REST APIs, database queries, etc. Finally, you will need to build appropriate interfaces for components to enable others to use them.

With emerging technologies, the market for Machine Learning jobs is booming. This is because this job has seemingly limitless applicability. And there are many programming language courses to acquire Machine Learning skills. You can opt for Machine Learning with Python, a well-known programming language course that teaches the purpose and applications of Machine Learning.