Artificial Intelligence (AI) & Robotics Course

Course Description

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition,Learning,Planning, Problem solving. Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Top Skills You Will Learn

  • Introduction to Artificial Intelligence
  • Introduction to Robotics
  • Introduction to Computer Vision
  • Introduction to Neural Networks
  • Working with CNNs
  • Introduction to EdgeAI & Robotics
  • Introduction to ROS
  • Application Development for EdgeAI with ROS

Who Is This Program For?

Engineers, Software and IT Professionals, Data Professionals

Job Opportunities

Machine Learning Engineer, Data Scientist, AI Architect, Business Analyst, Product Analyst

Minimum Eligibility

Bachelor's Degree with minimum 1 year of work experience or a degree in Mathematics or Statistics

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>>Introduction to Artificial Intelligence
  • What is Artificial Intelligence?
  • Hype vs. Reality
  • Current Industrial Applications using AI at its core
  • The way forward with Artificial Intelligence
  • Industry Verticals and future AI
  • What to learn for Artificial Intelligence?
  • Example Case Studies
>>Introduction to Robotics
  • What is Robotics?
  • Current Industrial Applications using AI
  • Challenges for developing AI for robotics applications
  • Way forward in Robotics & AI
  • Example Case Study
>>Introduction to Computer Vision
  • What is Image?
  • What is Image Processing & its techniques?
  • Current trends in Image Processing
  • Introduction to OpenCV
  • Hands on Experience with OpenCV & Image Processing
  • - Filtering Techniques
    - Linearity and Convolution
    - Edge Detection and Gradients
    - Hough Transform
    - Convolution
    - Perspective Imaging
    - Extrinsic & Intrinsic Parameters
    - Camera Calibration
    - Introduction to features
    - Motion & Object Tracking
    - The Kalman Filter
    - Bayes Filter
    - Particle Filter
    - Recognition & Classification
    - Support Vector Machine
  • Mini Project - OpenCV + Image Processing
>>Introduction to Neural Networks
  • Neural Network based techniques
  • Working with Neural Networks
  • - Data as a core component
    - Model Training
    - Validation
    - NN Performance Parameters
  • Introduction to popular CNN architectures
  • Introduction to CNN Frameworks
  • - Need of a CNN framework
    - Most Popular CNN frameworks
  • Tensorflow & Keras
  • PyTorch
  • Introduction to PyTorch
  • Hands on Experience with Training CNN with PyTorch
  • >>Working with CNNs
    • Image Recognition and Classification Techniques
    • Data Annotations & Labeling
    • Mini - Project - Working on CNN model training with readily available dataset for classification problem with PyTorch based CNN Model
    >>Introduction to EdgeAI & Robotics
    • Introduction to EdgeAI
    • Difference between Heavy Computing AI & need of lightweight computing in AI
    • Introduction to Intel OpenVINO platform
    • Mini-Project - Hands on experience working with RaspberryPI & Intel Movidius Neural Stick & running Object Detection Model with OpenVINO
    >>Introduction to ROS
    • Introduction to Robotic Operating System
    • ROS Architecture
    • The Pub/Sub way of data transfers in ROS
    • ROS APIs
    >>Application Development for EdgeAI with ROS
    • Extension of Mini-Project to the major with application development on RPI, Intel Neural Stick with Intel OpenVINO

    What is Artificial Intelligence (AI)?

    Artificial Intelligence is the ability of machines to seemingly think for themselves. AI is demonstrated when a task, formerly performed by a human and thought of as requiring the ability to learn, reason and solve problems, can now be done by a machine. A prime example is an autonomous vehicle. The vehicle is able to perceive its surroundings and make decisions in order to safely reach its destination with no human intervention. Converging technologies along with Big Data and the Internet of Things (IoT) are driving the growth of AI. Machines communicate with one another and are now capable of advanced perception, capturing millions of data points in seconds, processing the information and making decisions, all in a matter of seconds. As AI evolves, machines will have more capability to physically act based on their intelligence, eventually leading to machines that can build better versions of themselves.

    What to learn in Artificial Intelligence

    The field of Artificial Intelligence (ai systems) and machine learning algorithms encompasses computer science, natural language processing, python code, math, psychology, neuroscience, data science, machine learning and many other disciplines. An introductory course in AI is a good place to start as it will give you an overview of the components bring you up to speed on the AI research and developments to date. You can also get hands-on experience with the AI programming of intelligent agents such as search algorithms, games and logic problems. Learn about examples of AI in use today such as self-driving cars, facial recognition systems, military drones and natural language processors.

    Go further with courses in Data Science, Robotics and Machine Intelligence. Learn the fundamentals of how robots operate, including how to represent 2D and 3D spatial relationships, how to manipulate robotic arms and plan end to end AI robot systems. In Machine learning, explore unsupervised learning techniques for data modeling and analysis including data clustering, computer vision, reinforcement learning, problem solving, machine learning algorithms, image recognition, data mining, speech recognition matrix factorization and sequential models for order-dependent data.

    Start with Artificial Technology and get an overview of this exciting field. If you are unfamiliar with basic computer science and programming, it will be helpful to take and introductory class to learn Python, R or another programming language commonly used in data analysis.

    Jobs in AI

    Over 3,000 full-time machine learning engineer positions were listed on at the time of this article, with many offering salaries above $125K per year. Data scientist AI jobs typically require a bachelor’s degree or higher in computer science, engineering, or IT and experience with multiple programming languages including Java, C, Python, R, JavaScript and SQL and experience in data science is also a big plus. Top job positions include Artificial Intelligence Engineer, AI Project Manager, Researcher and Artificial Intelligence Consultant and some of the top companies hiring include Amazon, Google, Apple and IBM.

    Explore a Career in Artificial Intelligence

    Help build the future with an exciting career in the fast-growing field of artificial intelligence. Many industries like digital marketing and social media experts are relying on deep learning methods to make business decisions and their business applications better. If you love computer science, mathematics and data analysis, python programming, linear regression, and more then enroll and start learning about the applications of artificial neural networks and how you can help them move forward.

    To meet with today's demand and need for data analysts and AI experts, edX offers the best artificial intelligence programs and computer systems online courses in the market. If machine learning, deep learning, virtual assistants, tensorflows, and neural networks excite you, we have proper courses to help advance your career at your own pace. Become an industry expert in machine learning techniques today!

    A Brief History of Artificial Intelligence

    AI research was founded in the summer of 1956 at Dartmouth College during a workshop event. The excitement of machines becoming as intelligent as humans quickly got funding for millions of dollars to make this dream a reality. As time went by, the early pioneers rapidly realized how complex and complicated this task would be.

    In 1973, the U.S and British Governments stopped funding the research project around data structuring and learning algorithms. This period when the funding ceased was known as "AI Winter" as progress slowed down and frustration grew. There were a few on an off funded projects during AI Winter, but the momentum of AI development would pick back up by the 21st century.

    Excitement, investment, and interest in AI development boomed in the first decades of the 21st century. The excitement and enthusiasm ignited around successful AI projects in academia and industry with the assistance of more powerful computer hardware. The time of new AI projects, data structuring, and artificial intelligence programming language improvement led to the phrase "AI Summer".

    In the present day, we see AI integrated into our everyday lives with personal assistants. AI applications and intelligent machines like Siri, Alexa, Watson, Cortana, LinkedIn, and Google AI Assistant are all popular applications we use to conduct everyday tasks. These assistants can be used to pull information from the web, turn on home appliances, set reminders, talk to each other, and so much more. These types of machine learning and intelligent systems assistants are ever evolving, so the demand for engineers and computer scientists is at an all-time high for this market. Whether you are working on Microsoft Windows, iOS, an open source platform, Google, or Android, you can expect there to be a lot of demand for your skills.

    Our New Batches are Starting in September, 2020. Please call us on 9595605544 || 020 40059500 || 020 40059600 to know more

    We totally understand that each student is different and each student has different problems, which we need to tackle. Keeping this in view, we have designed different packages to meet your needs and timings

    New Batches will start from 26th MARCH 2020

    • Weekend batches: We have a special weekend batches for the working professionals, who are tied up on weekdays.
    • Fast-track batches: To complete the course in a brief time with detailed training.
    • Specialized corporate batches: In this special batch, we only cater the training needs of the corporate employees.
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