- Artificial Intelligence
- Python Programming
- Machine Learning
- Deep Learning
- Robotics
- Matlab
Certification Course on Artificial Intelligence
About the Course
Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance. This specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills. It is designed to give you a firm understanding of what is AI, its applications and use cases across various industries. You will become acquainted with terms like Machine Learning, Deep Learning and Neural Networks.
Furthermore, it will familiarize you with IBM Watson AI services that enable any business to quickly and easily employ pre-built AI smarts to their products and solutions. You will also learn about creating intelligent virtual assistants and how they can be leveraged in different scenarios. By the end of this specialization, learners will have had hands-on interactions with several AI environments and applications, and have built and deployed an AI enabled chatbot on a website – without any coding.
Course Objective:
- To understand what is AI, its Applications and use cases and how it is transforming our lives
- To describe several issues and ethical concerns surrounding AI
- To explain terms like machine learning, deep learning and neural networks.
- To provide a foundation in use of this AI for real time applications.
Course Outcome:
Upon successful completion of this course, students will be able to
- Expand knowledge about basic concepts of AI
- work with IBM Watson
- Create Chatbots without the need of writing any code
Course Duration:
3 Months
Course Content
S.No Content
1 What is AI? Applications and Examples of AI
2 AI Concepts, Terminology, and Application Areas
3 AI: Issues, Concerns and Ethical Considerations
4 The Future with AI, and AI in Action
5 Getting Started with AI using IBM Watson
6 Watson AI Overview
7 Watson AI Services
8 More Watson AI Services
9 Watson in Action
10 Building AI Powered Chatbots without programming - Introduction
11 Intents
12 Entities
13 Dialog
14 Deployment
15 Context Variables & Slots
16 Digressions
17 Final Exam
18 Online Certification
Certification Course on Python Programming
About the Course
Python programming certification course enables students to learn data science concepts. This Python Course will also help students to gain knowledge in Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, which are essential for Data Science.
Python Scripting is one of the easy languages to learn and is widely used from individuals to big organizations such as Google. This Python training starts with basic syntax of Python and continues to small GUI programs. Students will learn Python data types such as Tuples and Dictionaries, Looping, Functions and I/O handling. Python training will also give students an overview of Object Oriented Programming and Graphical application development. This course will explain some basics modules and their usage. At the end of the Python Scripting Training, individuals will have the skills to grow in Web-Development, GUI Application Programming, Game Development and writing powerful script for System Administration.
The Advanced Python Programming training course will give students a detailed overview of advance python programming topics like Leveraging OS services, Code graphical interfaces for applications, Create modules, Create and run unit tests, Define classes, Interact with network services, Query databases, Process XML data. This is an extensive hands-on training involving labs and exercises to students a practical and real-time exposure.
Course Objective:
- Expose students to application development and prototyping using Python
- To understand data Visualization and use of machine Learning in python.
- To enable the student to gain automation skills using python programming language to manage network devices.
- To prepare the students to use Python Programming in handling real world problems.
Course Outcome:
Upon successful completion of this course, students will be able to
- Students will be able to determine the methods to create and manipulate Python programs by utilizing the data structures like lists, dictionaries, tuples and sets.
- Students will be able to apply the best features of mathematics, engineering and natural sciences to program real life problems.
- Students will be able to design real life situational problems and think creatively about solutions of them.
Course Duration:
3 Months
Course Content
S.No Course Content
1 Python language basic constructs
2 Oops concepts in python
3 Introduction to Programming Using Python
4 Packages and functions in python
5 Introduction to databases in python
6 Importing data in python
7 Python Lists
8 Python Libraries(pandas)
9 Exception handling in python
10 Advanced python Programming
11 Introduction to MongoDB in Python
12 Python Programming for Network Engineering
13 Introduction to data visualization in python
14 Python for web Application
15 Introduction to Machine Learning with python
Certification Course on Machine Learning
About the Course
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
Topics include:
(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Course Objective:
- To introduce students to the basic concepts and techniques of Machine Learning.
- To develop skills of using recent machine learning software for solving practical problems.
- To gain experience of doing independent study and research.
Course Outcome:
Upon successful completion of this course, students will be able to
- Gain knowledge about basic concepts of Machine Learning
- Identify machine learning techniques suitable for a given problem
- Solve the problems using various machine learning techniques
- Design application using machine learning techniques.
Course Duration:
3 Months
Course Content
S.No Course Content
1 Introduction
2 Linear Regression with One Variable
3 Linear algebra Review
4 Linear Regression with Multiple Variables
5 Running the program in Octave / MATLAB
6 Logistic Regression
7 Regularization
8 Neural Networks
9 Advice for Applying Machine Learning
10 Machine Learning System Design
11 Support Vector machines
12 Unsupervised Learning
13 Dimensionality Reduction
14 Anomaly Detection
15 Recommender systems
16 Large Scale Machine Learning
17 Application Example: Photo OCR
18 Practice Exercises
19 Online Certification
Certification Course on Deep Learning
About the Course
Deep Learning course builds a solid foundation by covering the most popular and widely used Deep Learning technologies and its applications including Computer vision, Artificial neural networks, convolutional neural networks for the students who are interested in machine learning, Artificial Intelligence and who also have knowledge in Python programming. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like Mobiles, tablets, TVs, and hands-free speakers.
Deep learning differs from traditional machine learning techniques (like classification, clustering etc.) in a way that they can automatically learn representations from data such as images, video or text, without introducing hand-coded rules or human domain knowledge. Deep learning changes how you think about representing the problem that you’re solving with analytics. It moves from telling the computer how to solve a problem to training the computer to solve the problem itself.
Course Objective:
- To understand the concept of artificial neural networks, convolutional neural networks, and recurrent neural networks.
- To learn the foundations of Deep Learning, including how to build neural networks and machine learning projects.
- To learn deep learning methodologies to process not only image based datasets but also raw text, numbers etc.
Course Outcome:
Upon successful completion of this course, students will be able to
- Identify the deep learning algorithms which are more appropriate for various types of learning tasks in various domains.
- Develop ability to independently solve business problems using deep learning techniques.
- Apply such deep learning mechanisms to various learning and real world problems.
Course Duration:
3 Months
Course Content
S.No Course Content
1 Introduction to Deep learning
2 Neural Networks Basics
3 Shallow neural networks
4 Deep Neural Networks
5 Practical aspects of Deep Learning Optimization algorithms
6 Hyperparameter tuning
7 Batch Normalization and Programming Frameworks
8 Machine Learning Strategies
9 Foundations of Convolutional Neural Networks
10 Deep Convolutional models: case studies Object detection
11 Special applications: Face recognition & Neural style transfer
12 Recurrent Neural Networks
13 Natural Language Processing & Word Embeddings
14 Sequence models & Attention mechanism
Certification Course on Design of Robot using Embedded Systems
About the Course
This course is introduced to meet the growing demand for trained engineers in the field of Robotics. It provides sound, proportional knowledge in hardware as well as software development in their applications. Robotic is a course that involves design, development and operation of robots and it is an overlap of several engineering disciplines like electrical, mechanical, electronics, computer science and artificial intelligence. The students come from diverse backgrounds, but united by our common passion for robotics that will lead the future science and technology. The course contains Embedded C and Atmel Studio 6.0, I/O interfacing on AVR based microcontrollers and debugging, timers and delay generation, DC motor control and PWM generation for velocity control and Analog-to-Digital conversion and white line follower.
Course Objective:
- To train the students through hands-on projects are imperative in producing successful innovators in the field of Robotics.
- To provide the competitive advantage to colleges in attracting talented students.
- To facilitate the infrastructure creation by sharing its experience and expertise.
- To encourage to use robots to solve real life problems.
Course Outcome:
Upon successful completion of this course, students will be able to
- Create embedded systems, robotics technology and mechatronics based products.
- Provides platform to design, develop, program and test robots for various applications.
- Students can participate in national and international robotics competitions.
- Improve engineering projects with help of e-yantra open source community.
- Exposure to job opportunities in robotics.
Course Duration:
3 Months
Course Content
S.No Course Content
1 Introduction
2 Atmel Studio 6 IDE
3 Writing and debugging C code snippets
4 Programming and charging procedure for Firebird V
5 Function of I/O ports and the associated registers
6 Interface I/O peripherals like switch and Bar graph LEDs
7 Different LCD commands and ASCII encoding using Firebird V
8 Displaying text at different positions on the LCD and implementing a simple scrolling display
9 TIMERs and their registers in ATmega2560 for configuring TIMERs in Firebird
10 Manipulating TIMERs to generate delays as required without using "_delay_ms()" function.
11 Direction control of DC motors present on Firebird V
12 PWM or velocity control of the motors present on Firebird V
13 sharp sensors and white line sensors
14 ADC (analog to digital conversion) on Firebird
15 White lines following through writing a code to make Firebird V follow a white line
Certification Course on MATLAB Programming
About the Course
This course teaches computer programming to those with little to no previous experience. It uses the programming system and language called MATLAB to do so because it is easy to learn, versatile and very useful for engineers and other professionals. MATLAB is a special-purpose language that is an excellent choice for writing moderate-size programs that solve problems involving the manipulation of numbers. The design of the language makes it possible to write a powerful program in a few lines. The problems may be relatively complex, while the MATLAB programs that solve them are relatively simple: relative that is, to the equivalent program written in a general-purpose language, such as C++ or Java. As a result, MATLAB is being used in a wide variety of domains from the natural sciences, through all disciplines of engineering, to finance, and beyond, and it is heavily used in industry. Hence, a solid background in MATLAB is an indispensable skill in today’s job market.
Nevertheless, this course is not a MATLAB tutorial. It is an introductory programming course that uses MATLAB to illustrate general concepts in computer science and programming. Students who successfully complete this course will become familiar with general concepts in computer science, gain an understanding of the general concepts of programming, and obtain a solid foundation in the use of MATLAB. Students taking the course will get a MATLAB Online license free of charge for the duration of the course. The students are encouraged to consult the eBook that this course is based on. More information about these resources can be found on the Resources menu on the right.
Course Objective:
- To familiarize the student in introducing and exploring MATLAB.
- To enable the student on how to approach for solving Engineering problems using programming.
- To prepare the students to use MATLAB in their project works.
- To provide a foundation in use of this software’s for real time applications.
Course Outcome:
Upon successful completion of this course, students will be able to
- Express programming for engineering problems.
- Write basic mathematical, electrical, electronic problems in MATLAB.
- Do projects in image processing, control system using MATLAB.
Course Duration:
3 Months
Course Content
S.No Course Content
1 Introduction
2 Commands
3 MATLAB Editor & Running Scripts
4 Vectors and Matrices
5 Indexing into and Modifying Arrays
6 Array Calculations
7 Calling Functions
8 Obtaining Help
9 Plotting Data
10 Review Problems
11 Importing Data
12 Logical Arrays
13 Programming
14 Project I & II
15 Online Certification