- No comments
Machine Learning usually goes synonymous with Artificial Intelligence. To generally put it, it’s a pathway which enables the computers to perform certain tasks, for example, prediction, planning, recognition, diagnosis, robot control, all without specific programming. Through machine learning algorithms are created, by the development of these algorithms a model or a program can teach itself to learn and grow, and eventually, change whenever exposed to new data.
One can draw a line of similarity between Machine Learning and Data Mining, as both search the data for patterns, while data mining extracts data for business understanding, Machine Learning notices patterns in the data and alters program actions, thus extracting data for improving the programs own understanding.
One parallel is, that you need to have an inherent quality of being inquisitive, as curiosity is a prerequisite to a profession in Machine learning. The toolbox of a Machine Learning enthusiast is quite wide-ranging, yet there are a few key non-negotiable skillsets that are essential.
Statistics and Probability theories become imperative, as these theories help in learning about the algorithms. Theories like Naïve Bayes, Hidden Markov Models are some examples. And to understand these models it becomes necessary for you to know statistics and probability.
Math and Algorithms work as a pillar to a machine learning enthusiast, as to discriminate models such as SVM’s, you need to have a firm understanding of the significance of how an algorithm works and understanding the algorithm theory.
Programming Languages become a must if you need to build a career in machine learning, more importantly, it is better if you have the working knowledge of all the languages, Python is a favourite general purpose language, R for statistics, Hadoop is Java-based so Java to implement mappers and reduces, C++ can help in speeding code up.
Data Modelling, as machine learning often requires analysing unstructured data which eventually is relied on the science of data modelling, which is basically a prose of reckoning the structure of underlying data, finding patterns, filling gaps where data is non-existent. So if your conceptual understanding of data modelling is strong, it helps create effective and efficient algorithms that can be further trained and enhanced over time.
Managing and working with large datasets
is expected out of a person planning to build their career in machine learning, as these days, data is generally not processed using a single machine. A distributed computing approach is used, and data is distributed across an entire cluster.
Advanced Signal Processing Techniques is an important part of machine learning. It is not a one cap fits all type of solution that one is expected to give in machine learning, different types of problems can have various solutions. And using advanced signal processing algorithms will help you set yourself apart from the race.
Besides these skills it also becomes important for someone entering the machine learning field to keep themselves updated about the recent and future trends, so be well read, and connected with the online community, and learn about the new theories and algorithms.
The trends of 2018, like the last year, show massive promise in the field of machine learning. As a machine learning professional, one area where you will play a big role is in shaping the future of online services. Besides diverse industries are ready for innovation in this field and are on the lookout for experts and engineers. So by mastering the skills of machine learning, you will be building yourself a solid ground for the future.