The Business Application of Artificial Intelligence program from Innosential, in association with Dayananda Sagar University, equips learners with the knowledge and expertise to navigate the impending AI and ML revolution, including AI and ML Engineer certification from Amazon, Google, and Microsoft.

Taught by experts from academia, AI geniuses who have set world-leading AI systems, and practicing data scientists, the program uses Reverse Engineer Pedagogy to give students practical and employment-ready learning along with the opportunity to implement end-to-end MLOps Lifecycle. The program offers deployment-based learning across BFSI, Retail, Healthcare, Manufacturing, Supply Chain, and Automobile industries.

Who is this Program For?

• IT Professionals, Software Engineers, and Data and Business Analysts who want to unlock new opportunities for career growth and chart a cutting-edge career path.
• Recent Science, Technology, Engineering, and Mathematics (STEM) graduates and academics who want to enter the private sector and scale the positive impact of evolving technologies
• This program will equip you with the hands-on skills needed to launch and accelerate your career in AI and ML.
• Outcoming Job Roles: Data Scientist, Machine Learning Scientist, Machine Learning Engineer, Artificial Intelligence Engineer.

Program Prerequisites

• A bachelor’s degree or higher in STEM fields.
• Experience with Python, SQL, Statistics, and Calculus.
• Minimum of 2 years in Software Engineering/Data Science
*Eligibility-based exemption for the two months Foundational Program. .

Program Overview

Module	Duration	Content
Foundational Program	2 Months	Inferential Statistics, Python Programming, Exploratory Data Analytics, etc.
Supervised Machine Learning	4 Weeks	AI concepts, classical ML, and end-to-end ML workflows.
Deep Learning	4 Weeks	NLP, Computer Vision, and Transformer-based models.
Unsupervised Machine Learning	4 Weeks	Clustering, GANs, and deployment-focused projects.

Module 1: Supervised Machine Learning - 4 Weeks

Learners will be introduced to AI, classical machine learning, and key problems involving tabular data, as well as the underlying machine learning algorithms used to solve these problems. The learners will also get introduced to an end-to-end ML workflow and core algorithms like Regression, SVM, etc.

MLOPS 1: MLOPS Deployment on Amazon AWS - 4 Weeks

The deployment labs in the module focus on building ML Systems in the Amazon AWS SageMaker and cover key tools and APIs in the Amazon MLOps stack. Examples of ML systems: Weather forecasting, Network Intrusion Detection, Credit Risk Analysis, Named Entity Extraction, and Customer Lifetime Modelling.

Module 2: Deep Learning with applications in NLP & Computer Vision - 4 Weeks

Learners are provided with hands-on exposure to understand problems from texts, images, and video data using Deep Learning algorithms like RNN, CNN, Sequence Models, Transformers, Attention, etc. It also covers some key concepts of Language Technologies and Computer Vision fundamentals which are helpful to understand the applications.

MLOPS 2 & 3: MLOPS Deployment on Microsoft Azure & Google Cloud Platform - 4 Weeks

In the deployment sessions MLOps on the Azure ML Studio are used to build ML Systems such as Text Summarization, Image Segmentation, Object Detection, NER, Intent, and Information Retrieval using key libraries such as Hugging Face for implementation, Google's MLOP universe. It is discussed from the ground up and used to build ML Systems like recommender systems and search engines. Module 3: Unsupervised Machine Learning - 4 Weeks Learners will learn to solve problems with unlabeled data and understand key concepts that can be used to solve problems such as Search Engines, Text Summarization, Fraud Detection, and Image Recoloring. You’ll also learn about clustering, K-Means, GANs, and other generative models. In the deployment labs MLOPs universe of Google is used to build ML systems like recommender systems and search engines, etc. MLOPs 4: Designing a Machine Learning & MLOPS System with Kubeflow: 4 Weeks The learners will get LMS access for 12 months

Applied AI

The following table illustrates a few AI system problem statements along with the Data Formats, Algorithms, and tools that will be taught in the program.

Data Format	Problem Statement AI System	Algorithms	Library & Tools
Tabular	NYC-east-river-bicycle-crossings	Linear Regression (OLS, GLM)
Tabular	Credit Risk	Naive Bayes/Logistic Regression	SKlearn
Tabular	Trees	SKlearn, LightGBM, XGBoost
Tabular	SVM	SKlearn
Image	Image Classification	VGGNet, ResNET, U-Net, Yolo, MobileNet	OpenCV/TensorFlow/Torch
Image	Image Segmentation		OpenCV/TensorFlow/Torch
Image	Object Detection		OpenCV/TensorFlow/Torch
Text	Sentiment Classification	Logistic Regression	SKlearn
Text	Forecasting	ARIMA, Prophet, DeepAR	Statsmodel/Pymc3/PyTorch/TensorFlow
Text	NER	Sequential Modelling using Transformers (BERT, GPT)	Hugging Face
Text	Intent		Hugging Face
Text	Information Retrieval
Text	Language Model
Unsupervised Learning	Text retrieval/Search Engine	Clustering (K-means, DBScan, Isolation Forest), Topic Modelling (LDA); Generative Models (VAE, GAN)	Hugging Face
Unsupervised Learning	Text Summarization		Hugging Face
Unsupervised Learning	Fraud Detection
Unsupervised Learning	Generating Synthetic Data
Unsupervised Learning	Image Recoloring	DCGAN & WGAN
Unsupervised Learning	Image Enhancement/Compression	Superpixel
Reinforcement Learning	A/B Testing	MAB (Epsilon-Greedy, Thompson Sampling, UCB)	Python/Numpy
Reinforcement Learning	Travelling Salesman / Vehicle Routing	Q-Learning	Python/Numpy
Reinforcement Learning	Adaptive Recommendation	DDPG, REINFORCE	Python/Numpy

Faculty

Industry experts from Fortune 50 companies by Innosential.

Faculty Information

Faculty Name	Designation	Organization	Experience
Chirag Ahuja	Sr. Applied Scientist	OCI - Oracle Cloud Infrastructure	Oracle
Rishabh Malhotra	Sr. Data Scientist	JIO
Bhaskarjit Sarmah	VP Data Science	BlackRock
Sahibpreet Singh	Data Scientist	Tatras Data	Ex Data Scientist, ZS, Competition Expert
Subhodeep Dey	Data Scientist	JIO	Ex - United Health Group
Shivam Mittal	SWE Intern	Microsoft	Kaggle Competitions Expert (Ranked 313) NSUT'23
Shreyans Mehta	Chief Data Scientist	ApnaKlub	Ex - BlackRock
Vaibhav (Veer) Taneja	Data Scientist	Points (a Plusgrade company)
Aditi Gupta	Sr. Data Scientist	Apna	Ex - Data Scientist, Delhivery
Ranraj Singh	Senior Data Scientist	Convosight	Ex - Data Scientist, UnitedHealth Group
Professor K. N. Amarnath	Professor	Dayananda Sagar University	30 years of Data Science Experience
Professor H.N. Shankar	CEO DDE	ORG, Denmark	40 years of Data Science Experience
Professor SaiKumar K Chandrashekar	Professor	Alma Mater, IIM Bangalore	30 years Data Science Experience
Professor Srinivas Iyengar	Vice President	Happiest Minds
Professor Sriramu M.S.	Professor	IIT-B, Bombay
Professor Thiagarajan Rajagopalan	Founder and CEO	Tripeur, BITS Pilani
Professor Nikhil Gupta	Professor	IIT-Varanasi, MIT (US)

Academic Certification:

Upon completion of this program, you will be awarded an Executive Certificate by Dayananda Sagar University, Bengaluru (SCMS/Executive Education), as well as a certificate of completion from Innosential.