About The Program

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 course 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.

Ready to transform your career?

    Program Highlights

    AI and ML Solutions

    Gain AI and ML knowledge to select optimal models for business needs and solve industry problems.

    ML Production Pipeline

    Build end-to-end machine learning systems, implement the ML lifecycle, and deploy them to production.

    AI Networking Forum

    Learn, interact & collaborate with AI experts working at Fortune 50 companies.

    Career Accelerator

    Join our career accelerator program for job preparation, including mock interviews, resume prep and certification training.

    Program Overview

    Building a strong foundation of knowledge and skills is essential before diving into the
    more intricate aspects of AI. To develop a comprehensive understanding of AI, you will be
    trained on the following prerequisites as a learner in the field of AI.

    Unsupervised Machine Learning

    – Clustering Methods: Expectation Maximization, K-means,
    k-medoids, Agglomerative and Divisive Hierarchical clustering,
    Birch, DBScan, Spectral Clustering, Self-organizing maps.
    – Community detection in graphs.
    – Association Rules and Sequence Pattern Discovery.
    – Deviation Detection.
    – Semi-supervised and Active Learning.
    – Sequential Data Models: Markov Models and Hidden Markov
    Models, Kalman Filters.
    – Model Selection: Model Comparisons, Analysis Considerations.

    Text Pre-processing

    – Tokenization
    – Lemmatization
    – Vectorization
    – Bag of Words representation
    – Tfidf
    – Visualizing complex and high
    dimensional data

    Part 2: Core Curriculum

    A. Statistical Language Models

    – Statistical Language Modeling
    – Computational Linguistics
    – Statistical Decision Making and the Source-Channel Paradigm
    – Sparseness; Smoothing
    – Measuring Success: Information Theory, Entropy and Perplexity
    Maximum Entropy Models, Whole-Sentence Models, Semantic
    – EM for sound separation
    – Probabilistic Context-Free Grammars (PCFG), the Inside-Outside
    – Syntactic Language Models
    – Decision Tree Language Models

    B. Syntactic Models for Text

    – Parts-of-Speech tagging
    – Dependency Parsing
    – Coreference Resolution

    C. Model for Information Extraction

    – Named entity recognition Using HMMs and Conditional
    Random Fields

    D. Discourse Modeling

    – Case Study: Spam Detector

    E. Semantic Representations of Text

    – Singular Value Decomposition
    – Topic Models


    A. Predicting the Next Word in a Sentence

    – Computational Graphs
    – Feed-forward Neural Network Language Models
    – Measuring Model Performance: Likelihood and Perplexity

    B. Distributional Semantics and Word Vectors

    – Describing a word by the company that it keeps
    – Skip-grams and CBOW
    – Counting and predicting
    – Evaluating/Visualizing Word Vectors
    – Advance Methods for Word Vectors

    C. Why is word2vec So Fast?: Speed Tricks for Neural Nets

    – Softmax Approximations: Negative Sampling, Hierarchical
    – Parallel Training

    D. Convolutional Networks for Text

    – Bag of Words, Bag of n-grams, and Convolution
    – Applications of Convolution: Context Windows and
    Sentence Modeling
    – Stacked and Dilated Convolutions
    – Structured Convolution
    – Convolutional Models of Sentence Pairs
    – Visualization for CNN’s

    E. Recurrent Networks for Sentence or Language Modeling

    – Recurrent Networks
    – Vanishing Gradient and LSTMs
    – Strengths and Weaknesses of Recurrence in Sentence Modeling
    – Pre-training for RNNs

    F. Using/Evaluating Sentence Representations

    – Sentence Similarity
    – Textual Entailment
    – Paraphrase Identification
    – Retrieval

    G. Conditioned Generation

    – Conditional Generation and Search
    – Ensembling
    – Evaluation
    – Types of Data to Condition On

    H. Using/Evaluating Sentence Representations

    – What do We Attend To?
    – Improvements to Attention
    – Specialized Attention Varieties


    MODULE 1

    Introduction to Al & Nature of Intelligence


    MODULE 5

    Introduction to Al & Nature of Intelligence
    ◦ Basic Concepts and algorithms
    ◦ Algorithmic Complexity
    ◦ Data Structures

    MODULE 2

    Mathematics of Machine Learning
    ◦ Linear Algebra
    ◦ Multivariate Calculus

    MODULE 6

    Exploratory Data Analysis & Feature Engineering

    ◦ Data Exploration and preprocessing
    ◦ Feature Engineering

    MODULE 3&4

    Statistics and Probability for Data Scientists & Python Programming Language

    MODULE 7

    Introduction to Machine Learning

    ◦ Learning from Data
    ◦ Supervised Machine Learning
    ◦ Neural networks and Introduction to Deep Learning

    Who is this program for?

    IT Professionals, Software Engineers, 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.

    Program Prerequisites

    Key Takeaways

    Develop a comprehensive understanding of ML/ AI concepts and identify the best ML model to fit various situations and implement them in the production environment of Microsoft, Amazon, Google, and Kubeflow

    Learn to build end-to -end ML systems and pipelines. Master the area of ML Ops by implementing the ML life cycle. Build enterprise solutions to leading industry problems

    Learn, interact and collaborate with AI experts working at Fortune 500 companies like Amazon, Facebook, Microsoft, Blackrock, Oracle, UnitedHealth Group, and other market leaders.

    Job assurance viaCareer Accelerator - mock interview, resume preparation, job interviews,mock test, and certificate preparation

    Job assurance viaCareer Accelerator - mock interview, resumepreparation, job interviews,mock test, and certificate preparation

    Build a strong foundation for the course with our complimentary two months pre-course work.

    Get Certified

    Upon completing this course, you will be awarded with a certificate of completion from

    This program, prepares you for the AI and ML certification exams from Amazon, Microsoft and Google through MLOPs sprints and mock test prep in the career accelerator. Upon clearing these three exams you receive the following certifications.

    Why Enrol Now

    This is the only course that offers unique ‘Reverse Engineer Pedagogy ’ that enables you to learn hands-on skills in AI, ML, Deep Learning, Language, Vision, MLOps, ML Pipelines. It offers an opportunity to work on Industry problems across various domains and industries.

    Our Unique Pedagogy

    Introduce a problem.

    Reverse engineer the learning process of AI and ML by focussing on the application of the problem.

    Introduce the nearest available dataset available for that problem.

    Explore the nature of data associated with the problem and explain methods of Exploratory Data Analysis.

    Introduce the high-level approach to the solution.

    Impart the intuition to solve a problem, design thinking, and various methods to solve an AI problem.

    Introduce the algorithm(s) that can solve the problem.

    Introduce the intuition of the algorithm. Showcase a tool or library that can solve the problem.

    Deep dive into the algorithm.

    The mathematical formulation and Hyperparameters of the algorithm.

    Design and deployment of the solution.

    Learn to Operationalize ML and deploy models in production environment on Amazon AWS SageMaker, Kubeflow, Azure ML Studio, and GCP.

    Learning outcomes of the certification program

    The program is focused on deployments, applications & understanding of various techniques of AI across various verticals and horizontals of an enterprise. The student gets to deep dive into building ML systems, pipelines & solutions using various forms of AI methods – Natural Language Processing, Computer Vision, with different kinds of data (labeled, unlabeled, IoT Device data), etc.

    Program Director

    Dr. Sid J Reddy

    Principal Scientist at Google,
    Ex-Principal Data Scientist at Amazon Alexa Seattle, Ex-Principal Applied Scientist at Microsoft

    He Designed, developed, and contributed to dozens of AI systems used in production in a wide array of use cases and industry verticals (Health, Business Intelligence, Life Sciences, Legal Enterprise, and E-commerce).

    He developed text mining infrastructures from scratch at two technology startups, at the Mayo Clinic, and at Northwestern University, where he led a team of scientists, engineers, and annotators. His research is featured in over 110+ publications and submitted patents in AI (machine learning, deep learning, information retrieval, reinforcement learning, dialog systems, information extraction, summarization, and question answering).

    Industry experts from Fortune 50 companies

    Chirag Ahuja

    Sr. Applied Scientist | OCI – Oracle Cloud Infrastructure

    Bhaskarjit Sarmah

    Data Scientist at BlackRock

    Subhodeep Dey

    Lead Data Scientist, JIO Ex-Data Scientist, UnitedHealth Group

    Shreyans Mehta

    Chief Data Scientist, ApnaKlub, Ex-Data Scientist, BlackRock

    Enquire Now