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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Virtual Classroom	Video Based	Weekends

Course Overview

As the world progresses ahead in all the fields, Artificial Intelligence also advances ahead even in the Healthcare department. More and more companies are shifting to it and adapting this way of saving lives and even statistics show better results with it. A person interested in the course has to have a certificate in this sector to grow.

That is where this course comes in. Advanced Certification Programme in Digital Health and Imaging teaches the candidates all about biotech devices and how to use data analytics to their best. Moreover, it teaches how the landscape of the healthcare system is changing with the introduction of the Deployment of Artificial Intelligence, the Internet of things along with other new coming technologies and delivery systems.

The contents of the course have been brought by the expert faculty of the Indian Institute of Sciences in association with TalentSprint. The course offers one-on-one mentoring with the absolute best faculty of India and with this course the candidates will learn so much about Medical Devices, Digital Therapeutics, and Personalized Healthcare that it will open up a great number of opportunities for them to grow and shine.

The Highlights

1:1 mentoring
IISc certificate
Unique design
Expert faculty
Online sessions

Programme Offerings

Live Classes

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IISc Bangalore

The programme fee for Indian Students is Rs. 2,40,000 and that for International students in US Dollar 4,000

Total Fees for Indian Students	Rs. 2,40,000
Total Fees for International Students	USD 4000

Eligibility Criteria

Work Experience

Candidates should have a minimum of one year of relevant experience in the industry to apply for the course.

Education

Candidates have to have a Bachelor’s degree or a Master’s degree in Science, Pharmacy, Engineering, Management, or Medicine.

What you will learn

Data science knowledge

Once the programme studying is completed, they will have a firm grasp on some of the most important concepts of the industry.

They will learn all about Digital Health and how the candidates can use it to their best
They will learn the fundamentals of machine learning and how to function it
They will deep learn imaging and vision
They will learn the know-how behind Physiological Signal Processing
Finally, they will also learn about Wearable devices

Who it is for

The course is for professionals working in healthcare and technology, biotech, telemedicine, personalized healthcare, Digital therapeutics, and more among these lines.

Application Details

To get admission to the course, candidates have to follow the below-mentioned instructions for admission.

Step 1: Candidates have to visit the official course website.

Step 2: On the website, they will find a form which the candidates have to fill correctly and press on Apply Now.

Step 3: After the form has filled, they have to submit their documents.

Step 4: Once all the previous steps have been made, candidates have to wait to see if they get selected for the programme.

Step 5: If the candidates get selected, they will then be able to join the programme.

The Syllabus

Pre-requisites: Understanding of Digital Technology

Need, case studies, basics - mHealth and eHealth, Impact
Informatics: Health Level Seven (HL7), Integrating the Healthcare Enterprise (IHE), Vendor Neutral Archives (VNAs)
Open source/data/innovation - opportunities
IT infrastructure (IoT/Cloud computing)

Pre-requisites: Basics of Signals & Systems, Basics of Fourier Transforms and Z-Transforms, Basics of Physiology

Signal Processing: Sampling, Basic Filters, Decimation, Interpolation, STFT, Wavelets
Physiology: ECG Signal Acquisition (Electrical activity of heart, chest leads/montage, action potential in pacemaker and other regions; action potential relation to ECG Waveform; Reading ECG); EEG Signal Acquisition (Neural activity in the brain, Action potential, post-synaptic potential, Signal Propagation in the brain, EEG montage, EEG Signal Acquisition); EEG and ECG data processing
Wearable Sensors for health monitoring: Accelerometers (data acquisition and interpretation), glucose sensing (acquisition methods and comparison), Wearable ECG & EEG based on dry electrodes
Speech and audio signal processing: From signal capture to data pre-processing and feature modelling

Pre-requisites: Basic of Probability and Linear Algebra: Bayes Theorem, Random Variables, Expectation, Variance, Matrices, Inverse, Eigenvalues and Eigenvectors

Basic Mathematics for ML, What is Data and Model? Machine Learning Workflow and Applications
Introduction to real-world signals - text, speech, image, video; Feature extraction and front-end signal processing - information-rich representations, robustness to noise and artifacts
Learning as optimization, Linear Regression, Regularization and Logistic Regression
Basics of pattern recognition, Generative modelling - Gaussian and mixture Gaussian models
Machine Learning for physiological signal processing. Time series modelling

Pre-requisites: Basic Machine Learning that is part of Module 3

Deep Learning: Basics, MLPs, Back propagation, CNNs
Deep Learning for physiological signal processing. Recurrent neural models
Discussion on Depth Versus Width. Practical considerations in Deep Learning. Avoiding Overfitting- Regularization, Dropout. Convolutional Neural Networks. Recurrent Neural Networks. Forward and Backward propagation. Various Architectures for sequence to sequence and sequence to vector mapping.
Applications of Deep, Convolutional and Recurrent models in healthcare. (Instructor: SG)
Nature Language Processing: LSTMs, Language Models, Knowledge Graphs, Q&A (Demo)

Pre-requisites: Modules-1,2,3, and 4

Medical Imaging Modalities: Introduction, Protocols, Work Flows, Applications
Medical Image Analysis: Basics, Imaging Physics-Based Methods, and Need for Deep Learning & Neuroimaging: Introduction, Challenges
Vision - Deep learning: Loss function, Optimization, CNNs, Training Convolutional Neural Networks, Object Detection, Segmentation
Deep Learning models: AlexNet, VGG, GoogleNet, ResNet, RNN/LSTM

Instructors

IISc Bangalore Frequently Asked Questions (FAQ's)

1: Who are the faculty of this course?

Prof. Phaneendra Yalavarthy, Prof. Jaya Prakash, Prof. Ambedkar Dukkipati, Prof. Venkatesh Babu R, Prof. Sriram Ganapathy, and Prof. Yogesh Simmhan are the faculty of this course.

2: What does Career Accelerator in this course offer?

Career Accelerator offered by TalentSprint in this course offers a compelling Profile, membership into an elite community, Priority Career Access, and startup mentorship.

3: What jobs could the candidate pursuing this course apply for?

Candidates after having finished this course can apply for the job of a research scientist, healthcare data analyst, senior medical engineering software engineer, and many more other fruitful career aspects.

4: What are the eligibility criteria for the course?

Candidates have to have a bachelor’s degree of four years or equivalent or a Master’s degree in Science, Pharmacy, Engineering or Medicine. They also need one year of relevant work experience to be eligible for the course.