In the era of intense computerization, and the paradigm shift in prioritization of business to move ‘virtually everything’ on technology; mere concepts of automation & operational efficiencies do not suffice.

Artificial intelligence (or automation) is a trending topic of interest amongst veteran technology experts and astute business minds. The focus is on creating a holistic and prudent system which do not depend on human intervention for either data or intelligence. The systems look for certain trends in data, responses, experiences, and outcomes; and then leverages the knowledge gained to self-learn and improve – thereby progressively reducing the delta between ‘actual’ and ‘desired’. The ‘how?’ factor of artificial intelligence is catered to by the concept of Machine Learning.

Artificial Intelligence, Machine Learning & Deep Learning

Artificial intelligence is analogous to a ‘science fiction’ which is conceptually intriguing; and is instrumental in providing a vision of the future. Machine learning is ‘an approach to implement artificial intelligence’ wherein it has embedded algorithms to parse the underlying data and derive valuable insights in terms of the predictive nature of the system.

Deep learning is an offshoot of machine learning which advocates the relevance of neural networks; and the importance of understanding data representations rather than simply being task oriented.

Different machine learning methods

Machine learning algorithms may be supervised, semi-supervised or unsupervised.

‘Supervised machine learning algorithms’ is generally employed when a past learning needs to be superimposed on a new dataset. In this, certain known input-output combinations are used to infer predictions; as well as the residual is used to train the make the model more relevant to the current business problem.

‘Unsupervised machine learning algorithms’ doesn’t come with any particular classifications or labels. Therefore, its primary objective is to explore hidden truths from the data and derive inferences – rather than simply charting out an output.

Mid-way between the two lies the ‘Semi-supervised machine learning algorithms’ which is generally a combination of small proportions of labelled data; and comparatively large quantum of unlabeled data. These algorithms are employed when the labelled data requires to be trained on up-scaled resources.

Introduction to Machine Learning with Python

There are multiple scripting languages which can be leveraged to implement machine learning algorithms – Python, R, Scala, JavaScript, C/C++, Ruby and so on. Amongst all option; firstly, judging it from a popularity perspective, Python leads the pack with over 50% of the data scientists and experts using it for their analysis. In addition, close to 33% of the developers prioritize it.

Another aspect of juxtaposition is the ‘prioritization to utilization’ ratio which indicates whether a particular technology is a first choice or used as a complementary option. Python scores a whooping 58% in this KPI – signifying that Python is the first choice of most developers and machine learning experts.

Finally, from a usability perspective as well; Python is quite easy to muster and has an intense & exhaustive set of libraries to perform complicated tasks in a simple & convenient way.

It can probably be deemed as the best language for machine learning!