Aegis School of Business, Data Science, Cyber Security & Telecommunication

Aegis School of Business, Data Science, Cyber Security & Telecommunication

Start date: Sep 17, 2018
Application fee: 500.00 INR

Post Graduate Program in Applied AI, Machine Learning & Deep Learning

Start date : Sep 17, 2018
Application fee : 500.00 INR

About the Program

Certification Body: IBM and Aegis School of Business, Data Science and Telecommunication and Cyber Security
Location: Mumbai(29-Oct-2018)
Type: Post Graduate Program (PGP)
Coordinator: Mr. Ritin Joshi
Language: English
Duration: 11 Months
Registration amount: 30000.00 INR
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Course Details

Post Graduate Program (PGP/MS) in Applied AI, Machine Learning and Deep Learning in association with IBM

A True Applied AI Program, Build AI applications.  

“80 percent of all applications will have an AI component by 2020” IDC

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

This program is India’s first applied Post Graduate Program (PGP/MS) in Applied AI, Machine Learning and Deep Learning designed and delivered by Aegis School of Data Science in association with IBM and to train the new generation of applied AI professionals. This 11 months program provides you intensive hands-on training to develop the necessary and unique set of skills required for successful career in the fastest growing and intellectually stimulating fields of AI, NLP, ML, Deep Learning and Cognitive Computing. 

“There is a huge need for developers who not only understand AI, but know how to apply it.”  Sundar R Nagalingam, ‎Head - Deep Learning Practice, NVIDIA India, 

The demand for ML and Deep learning is growing, however there is little understanding on how to apply these complex technologies this program aims to bridge this gap and to accomplish this mission Aegis has joined hands with leaders like IBM, Nvidia and Amazon Web services. This program emphasizes more on application without compromising on fundamental theories. Develop AI applications and products using ML and DL using open source technologies, AWS AI, IBM Watson APIs, ML Azure and Google AI cloud platforms.

 Analyst firm IDC estimates that 80 percent of all applications will have an AI component by 2020. In another words by 2020 every piece of tech application or software will have embedded intelligence which will be enabled by AI. This tell us that not only those who wants launch their career into AI have to acquire these skills but also the existing software developers need to equip themselves with ML and DL skills so they can incorporate growing demand of making existing software intelligent and to meet the future demand from clients.

Extensive training during this program will cover the fundamental tenets of deep learning such as using AI for object detection, robotics, Self-driving cars, image classification, chatbots, applying this to determine the best approach to cancer treatment; Natural language processing, speech recognition etc.

Why is artificial intelligence important?

"AI and machine learning will increasingly augment and extend virtually every technology enabled service, thing or application." Gartner 

  • AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
  • AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
  • AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predicator. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
  • AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
  • AI achieves incredible accuracy though deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
  • AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

How Artificial Intelligence Is Being Used

Every industry has a high demand for AI capabilities – especially question answering systems that can be used for legal assistance, patent searches, risk notification and medical research. Other uses of AI include:

  • Health Care: AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.
  • Retail: AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.
  • Manufacturing: AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.   
  • Sports: AI is used to capture images of game play and provide coaches with reports on how to better organize the game, including optimizing field positions and strategy.

How Artificial Intelligence Works

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:

  • Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.

  • A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.

  • Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

  • Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.  

  • Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.

  • Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

Additionally, several technologies enable and support AI:

  • Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power.

  • The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.

  • Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.

  • APIs, or application processing interfacesare portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.

In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be anytime soon.