Robotics has opened a plethora of job opportunities in the field of science from eminent organizations with lucrative salary packages. It is one of the highly demanding professions in these recent years. Though there is a controversy that robots will take over human jobs but it is guaranteed that robotics is the future of the industries. RPA has transformed the work environment by boosting productivity and assisting human employees in factories or hazardous environments. RPA has created new professions such as robotics engineer, robotics technician, sales engineer, software developer, robotics operator and many more. Reputed companies require experienced professionals to work with RPA efficiently and effectively.

Developers with no data science experience are now able to integrate Machine Learning (ML) with IoT. As the number of IoT endpoints proliferate, the need for organizations to understand how to architect machine learning with IoT will grow rapidly. However, for this to occur, IoT architects and data scientists must overcome the challenge of having two very different disciplines collaborate closely to design an ML-powered IoT system. IoT architects often focus on IoT infrastructure (e.g., IoT endpoints, gateways and platforms) and defer consideration of how they will integrate ML inference into their design. They may not be familiar with ML well enough to know when it could help them solve their business problems.

If you aim to become deep learning researcher then you should lean things more deeply then just their implementation part. I would advice to start working on some project along with the reading stuff. A good knowledge of these concepts is required for reading research paper. Then learn basic ML stuff and deep learning concept. Doing this course will give you sufficient knowledge about the basic architectures of deep learning.

It is interesting to note that there is no agreed upon definition of artificial intelligence. Why is this interesting? Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about it, dreamers base their fantasies on it, and pragmatists criticize and denounce it. Such a state of affairs has persisted since Newell, Simon and Shaw wrote their first chess program and proclaimed that in a few years, a computer would be the world champion. Not knowing exactly what we are talking about or expecting is typical of a new field; for example, witness the chaos that centered around program verification of security related aspects of systems a few years ago. The details are too grim to recount in mixed company. However, artificial intelligence has been around for 30 years, so one might wonder why our wheels are still spinning. Below, an attempt is made to answer this question and show why, in a serious sense, artificial intelligence can never demonstrate an outright success within its own discipline. In addition, we will see why the old bromide that "as soon as we understand how to solve a problem, it's no longer artificial intelligence" is necessarily true.