Today, cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are bringing in the age of Technological Revolution. These technologies are helping shape not just our present but also laying the foundations for a better tomorrow. Companies across the world are now investing in these technologies to foster innovation, thus creating the demand for skilled and talented data professionals.
Machine Learning is a branch of Artificial Intelligence that enables machines to perform advanced tasks like prediction, image/voice recognition, robotics control, and so on without being exclusively programmed for those tasks. ML technology is mainly algorithm-oriented, that is, it aims to create such algorithms as can ’learn’ from the behavior of data and adapt accordingly to produce the best possible outcomes. In this respect, ML is somewhat similar to data mining – both of these fields are interested in sifting through data to uncover valuable insights and patterns hidden within data. However, ML has the edge over data mining.
While aspiring to begin a career in Machine Learning is undoubtedly commendable, you must first develop the requisite skills that are demanded of machine learning specialists. Here are the five skills that every machine learning engineer should develop.
- Fundamental Concepts of Computer Science and Programming
- Probability and Statistics
Machine Learning is closely associated with Probability and Statistics. In fact, probability concepts such as conditional probability, Naive Bayes rule, Markov Decision Processes, Hidden Markov Models, etc., make up the core of various ML algorithms. Statistics, on the other hand, provides the foundation for distributions, measures, and analysis techniques that are required for building and analyzing data models.
- Data Modeling and Evaluation
Data Modelling is the technique of estimating the implicit structure of a particular dataset to extract meaningful information from the data and to predict hidden trends from within. In this process, you have to evaluate the relevance and efficacy of the data model continually and to do this you must choose such an accuracy measure and evaluation strategy that best fits the data model in question.
- ML Libraries
To be able to implement ML algorithms appropriately, you need to be familiar with ML libraries or APIs such as Theano, Spark, TensorFlow, and Scikit-learn, to name a few. However, for these APIs to function optimally, you need to have extensive knowledge about classification algorithms like decision trees, neural nets, support vector machines, etc. The more you practice with these algorithms and APIs, the better will be your grasp on machine learning. You can try ML challenges on platforms such as Kaggle that will introduce you to a vast range of complex algorithmic problems.
- Software Engineering and System Design
An ML engineer is a software engineer and machine learning expert rolled into one. Essentially, an ML engineer’s core task is to build innovative software. To develop a software, you need to learn how to integrate smaller fragments to create the bigger picture. Thus, the software you create should be compatible with your system design. It should be scalable, flexible, and efficient so that it can adapt according to the ever-mounting volumes of data.
While mastering these major skills is a must for building a career in Machine Learning, you must also never forget that it is still a developing field. Thus, it is important that you constantly update yourself with the latest trends and innovations in ML and upskill accordingly.
If you are interested in building a career in Machine Learning, our Machine Learning course – Cognizance is what you need. At Coding Ninjas, our expert and qualified panel of instructors will ensure that by the end of the course, you are fully aware of the nitty-gritty of machine learning.