Till a couple of decades ago it was unfathomable that machines can have intelligence of their own. Machines of any form were simply considered to be mere instruments that were meant to ease human labour. Things like decision-making were not under its realm. However, it was in the very last years of the previous millennium that things like artificial intelligence came into the picture. Broadly speaking, the role of artificial intelligence is to make machines capable of taking decisions on their own without any form of human interference.
With things like home automation becoming more popular, there is a growing demand for people who have a degree in artificial intelligence. An interesting aspect of this field is that this particular course is open to students from a wide variety of backgrounds. Electronics, computers and electrical engineers have their own contribution to the field. For each of them, attending specialised courses in the area of artificial intelligence puts their career on the right track for success. This article explores the different artificial intelligence courses that will enhance career growth.
• Learn With Google AI
The best feature about this crash course is the fact that its machine learning program is inbuilt with Google's machine learning library, TensorFlow. Thus, unlike most courses whose curriculum is stagnant, here things are completely up to date. This is all especially important in the context of something that is as dynamic as artificial intelligence.
The icing on the cake is the fact that this course starts teaching things from the grass-roots level. Thus, you will be able to reap the benefits of this course even if you have no prior experience in this field.
• Machine Learning - Udacity
This is another course whose contents are prepared by the market leaders in the field, Google, and the platform through which this is made available to us is Udacity. Unlike the Google AI course, this one is not for novices. If you want to enrol yourself in this course, some prior knowledge of supervised learning methods is an absolute must.
This course aims at enhancing the career growth of data analysts, data scientists and machine learning engineers by making the most of the latest in machine learning and neural network technologies. Enterprising individuals, with a background in science and a keen interest in technology waiting to utilise the plethora of open-source libraries and materials to their benefit and set their career on a path of growth, are also encouraged to take up this course.
• Machine Learning - Stanford University
This course is available both as a full-time course and as a distance learning program in association with Coursera. In fact, even in Coursera, the course can be taken either as a free course or as a paid certification. On paying for the certification you can use your understanding of artificial intelligence to increase your career prospects.
Things like speech recognition and web search enhancement are dealt with. Backward propagation courses, statistical topics, like linear regression, and artificial intelligence tools are all within the realms of this course. The course is 6 week long for those attending the live course while in the case of students attending the same through web-based medium, the duration stands at 12 weeks.
The course is conducted by Andrew Ng, who is a pioneer in this field and currently heads Google's deep learning unit, Google Brain.
• Deep Learning For Computer Vision - Nvidia
This course deals with visual information processing and takes into account the various aspects of image processing in order to make the most of artificial intelligence. As most of us are well aware, Nvidia is a market leader in the manufacturing of graphics processing units (GPUs).
The expertise of Nvidia in this field is apparent from the fact that the course curriculum here is systematically designed to cover the crucial part of the design and implementation of the high-powered graphical engines. This comes into play in the designing of a number of computer vision appliances.
This course teaches one the technical concepts and then encourages the students to unleash their creativity. That is why building and deploying a complete neural net application forms a part of the final assessment.
• Machine Learning - Columbia University
This is a multidimensional course that emphasises both on supervised as well as unsupervised learning. It teaches models, applications and methods for solving real-world problems using both probabilistic as well as non probabilistic methods. Like the previous course, this one is also available for free (but one needs to pay if they want to get a certification in this regard).
This is one of the most popular courses in this field. The course is offered through edX, which is a non-profit organisation. This forms a part of the artificial intelligence nanodegree and is a highly coveted course wherein one needs to prove their worth (in terms of prior certifications, degree or experience in this field) in order to get admission to the course.
• Deep Learning For Self-drive Cars - MIT
This is a scientifically designed course that deals with one major real-world aspect of artificial intelligence and uses that to explore a number of technologies involved in the field. Here students are taught how to configure machines to interpret data from a variety of sensors that are connected to them.
This is analogous to our brains interpreting signals that are coming from the different sensory organs like the eyes, ears and the nose. In the near future the self-drive cars will rely on artificial intelligence to interpret and utilise all the data hitting the vehicle's array of sensors and make decisions based on that to safely navigate through congested roads.
In such a scenario, people with a formal degree or certification in this field will find themselves landing with coveted and highly paid jobs.