Program Description

Embark on a transformative learning journey exploring the power of Artificial Intelligence across various fields such as electrical, mechanical, civil, and general applications. This course elevates the learner’s understanding of AI by bridging the gap between theory and practical applications, providing hands-on experience in applying AI algorithms to real-world scenarios.

Key Highlights:

  • AI in Healthcare: Gain an end-to-end perspective on AI solutions in healthcare.
  • Electrical Engineering: Learn key AI principles for load predictions and fault diagnosis in substations.
  • Mechanical Engineering: Explore AI applications in seismic data processing, geo-modelling, and reservoir engineering.
  • Civil Engineering: Discover AI's role in cloud data collection at construction sites and applications in transport engineering and road traffic prediction.

Immerse yourself in the future of AI with a focus on Machine Learning and Deep Learning operations. Gain insights that enable you to identify and apply AI-based solutions to real-world challenges. Engage in hands-on exercises with software support to gain a comprehensive understanding of AI metrics.

Enhance your skills and broaden your horizons with the power of AI.

Course Objectives

At the end of this course learners will be able to,

  • Understand the core principles and fundamentals of machine learning,
  • Understand the application machine learning techniques in real-time scenarios
  • Comprehend the ANN architecture in Electrical Engineering applications
  • Understand the significance of CNN in challenges aspects of Electrical Engineering
  • Understand the impact and advancements of ML algorithms in various applications of mechanical engineering
  • Understand the latest advances in AI technology in the context of mechanical engineering applications
  • Understand the various facets of ML applications in civil engineering
  • Understand CNN-based Civil Engineering solutions
  • Understand AI’s impacts across general and social domains

Key Highlights

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The AI algorithms are explained with a pedagogy such that, the novice learner can start designing AI algorithms for related problems

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The demos provided make the learners to get an experiential learning on AI programming

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The various use-cases enable the learner to innovate new solutions for real-world problems

Course enrollment data

Learning Format

Online

Duration

5 units

Certified by

IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras and
L&T EduTech

Program Fee

Rs. 1900/- Inclusive of Tax

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Program Description

Education Qualification

Students pursuing Diploma / UG / PG Programs in Electrical & Electronics Engineering / Electronics and Communication Engineering

Suggested Prerequisites

Basic knowledge on Electronics Engineering

Teaching Hours

14

Lead Faculty

Dr. B Venkatalakshmi

Subject Matter Expert L&T EduTech.

A highly qualified academician and researcher with a Ph.D in Multisource Network Coding for MANETs and an ME in Optical Communication from College of Engineering Guindy, Anna University. Her research interests include pervasive computing, network coding, RFID, digital signal processing, information theory, mobile ad-hoc networks, industrial IoT, AI and edge computing and 5G. Dr Venkatalakshmi has a wide range of research skills, including Matlab, GIoMoSim, Qualnet, ADS, RFID API integration, Python, Weka and Power BI. With over 29 years of teaching and research experience, having served as a lecturer, professor, head of research and development department and vice principal at various educational institutions in Chennai, she has made significant contributions to academic planning and development, research planning and development and industry interactions and development. She has organised and developed the ME Mobile and Pervasive Computing syllabus and gained expertise in the domain of RFID and wireless sensor networks, training human resources in these areas. She has also established new research labs and published many research works in national and international journals.

Learning Schedule

• ML algorithms such as SVM, KNN, K-means, BERT, Random Forest classifier, CNN and Mobile Net V2
• ML techniques in diverse real-time applications

• ML Algorithm in various aspects of electrical engineering, such as load prediction and feature extraction in substations
• CNN based tasks related to substation analysis, infrastructure management, and infrared fault image diagnosis

• Impact of ML in the oil and gas industry
• Seismic data processing techniques, with a focus on salt body delineation using CNN.
• Process of geomodelling based on the Gaussian process regression algorithm
• AI applications in the upstream sector of the oil and gas industry
• Service-Oriented Architecture (SOA) of big data for the oil and gas industry

• Generic ML modeling framework for civil engineering applications
• Deep learning techniques in construction sites, with a focus on recycled cement strength prediction
• Diverse ML application areas such as transport engineering, road traffic prediction, naval architecture, and wave height forecasting, using deep learning algorithms like ANN, CNN, and YOLO architecture

• Impact of AI in education
• Open-source AI software libraries such as H2O, ImageAI, OpenAI Gym, Keras, TensorFlow, PyTorch, and Scikit-learn
• Computer vision techniques for car object detection using YOLO
• Language reasoning in AI with an application of language identification in text
• AI-based speech recognition technology in the healthcare sector for heart disease prediction
• Architecture, data architecture, data ingestion, and stream processing in IIoT solutions.
• Policies and strategies related to AI adoption and implementation



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