Category: Analytics

Machine Learning (ML) is a subset of Artificial Intelligence, which enables the computers to perform certain tasks such as Recognition, Diagnosis, Planning, Robotics Control, Prediction etc., without specific programming. Machine Learning focuses on developing algorithms with the capability of teaching itself to grow and adapt when exposed to new sets of data. As a result, there is a massive interest in the field of machine learning, in individuals who wish to pursue their career in this field, as well as organizations who wish to reap the benefits by its application.
As a Machine Learning engineer, it is very important that you understand not only the specific skill set, but also that you have a fair understanding of the environment, for which you are designing.

Let us understand this with the help of an example, assume that you are working for a retail store. And let us say the company wants to design a reward system, through which coupons are issued based on facts like, purchase history, with the intent that the issued coupons will actually be used. Now traditional data analysis approach would be to study the historical data, and figure out trends, and subsequently propose a strategy. But in the Machine Learning approach an engineer would need to create an automated coupon generation system, however, you will only be successful, if you understand the peripheral functions of the environment like the inventory, Catalogue, Pricing, Purchase Orders, Invoice Generation, CRM Software etc.,

So the skill requirement is not only restricted to the application of machine learning algorithms and the understanding of what to apply when, it is also equally important to understand the Interconnected Relationships of these Functions so that you can then successfully create a software which integrates interface, for an effective output.
Now for the real deal, the actual technical skills you need to kick-start your career as a machine learning engineer. You need to have a good and detailed understanding of the ML Algorithms, Mathematics, Skills in Problem Solving and Analytical thinking, and above all an innate sense of Curiosity. In addition to this, the below mentioned Skill….
Programming Languages like C++ can help in speeding code up, R, Python & Java works wonders for statistics.
Theories like Naïve Bayes, Hidden Markov Model, would require you to have a good understanding of Probability and Statistics so that you can comprehend these models.
A firm understanding of Applied Math and Algorithm theory, along with the knowledge of how the algorithms works, will help you discriminate models.
You will also need to skill yourself on Distributed Computing, as a machine learning role would require you to work on large datasets, which cannot be processed using a single machine, but you will be required to distribute it across an entire cluster

Data Modelling and Evaluation

Data Modelling is the process of estimating the underlying structure of any given dataset, with the intent of finding a pattern that is useful or picks up predictions of previously unseen trends. This process will be futile if the appropriate evaluation is not done to access the effectiveness of the model. So that you can choose an appropriate error measure, and apply an evaluation strategy, it is important that you understand these measures, even while applying standard algorithms.

Software Engineering and System Design

These are considered as the typical output of any ML engineer’s deliverables. It is that small component that becomes a part of the larger ecosystem. Like said earlier you need to make the puzzle, keeping in mind the various components, ensure they work with the help of proper communication of the system with the interface, and finally carefully design the system such, that any bottlenecks are avoided and the algorithms successfully scale along with the volume of data.
It is hence without a doubt that the demand for machine learning Engineers will rise exponentially, as the challenges of the world are complex and only complex systems will be able to solve them. Machine Learning Engineers are building these complex systems, therefore you become the future!
Also Read : Skills Required to Learn Machine Learning

Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL), can be imagined as the three bears from Goldilocks staying together in a house, where each member has a specific use but yet conceptually they are interconnected. Artificial Intelligence is the big umbrella under which resides the Machine Learning concepts, and Deep Learning can be referred to as a sub-set of Machine Learning. So while Deep Learning and subsequently Machine learning comprises of Artificial Intelligence, the other way around is not necessarily true.
The field of data science is buzzing with these terminologies, and in all the noise it is very easily misinterpreted, and often Machine Learning and Deep Learning are used interchangeably for one another. One thing is certain that Deep Learning is a technique for implementing Machine Learning.
Also Read: Future of Machine Learning in India
Let us understand this by breaking down the puzzle….

Artificial Intelligence orchestrates the capacity of a machine to comprehend complex human tasks like, decision making, understanding spoken language, detecting fraud, in short, it enables a machine to imitate intelligent human behaviour.
Machine Learning is surely a part of AI, where it enables a computer to act in a specific way without the need for explicit programming. This is imperative as the volume of data is ever increasing and to keep up, the machine should have the capability of implementing effective algorithms which can effectively and efficiently make predictions by recognising patterns. To perform the same, data scientists have a number of existing ML methods or Algorithms which can be easily applied to any data problem, at the same time it can be applied to a number of real-life use cases, for example, recommendation engines, or applying Natural Language Processing (NLP) in chat logs.

Deep Learning is a subset of ML and when data scientist refers to the term deep learning they most often mean Deep Artificial Neural Networks or alternatively Deep Reinforcement Learning.
Deep Artificial Neural Networks are essentially a set of Algorithms which are popular in recent times, for setting new records in accuracy while dealing with complex problems like, Image Recognition, Sound Recognition, Recommendations Systems, etc…, To add on, one can easily define DL in a similar manner to ML, so it can be safely said that DL also enables a computer to act in a specific manner without the need of explicit programming, with the addition that DL ensures it produces results with higher accuracy.
DL is often more complex as compared to ML, the prerequisites to DL would be a high-performance computer and huge volumes of Labelled data to give reliable results.
ML can be used with a small volume data set and has a shorter training time, while DL will be effective with large volume data set and often requires a longer training time. In ML you can use your own features, so for example, if you need a computer to be trained on recognising an image of a cat, you will first need to key in all the relevant features of a cat into it. In DL you will need to feed the computer with large volumes of data with cat images, and the system will choose the features on itself which characterises a cat. Hence the training time is longer, while the chances of accurate information are higher due to the complexity of reaching the conclusion, in some cases, it is more accurate than humans.
Driverless Cars, Movie Recommendations and preventive health care are all possibilities enabled by Deep Learning. DL in the future could be responsible for machine assistance in possibly all aspects of life. Deep Learning has the promise of taking the application of AI from fantasy to reality.
Related Article: Skills Required to Learn Machine Learning