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.
Gain AI and ML knowledge to select optimal models for business needs and solve industry problems.
Build end-to-end machine learning systems, implement the ML lifecycle, and deploy them to production.
Learn, interact & collaborate with AI experts working at Fortune 50 companies.
Join our career accelerator program for job preparation, including mock interviews, resume prep and certification training.
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.
– 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.
– Bag of Words representation
– Visualizing complex and high
Part 2: Core Curriculum
– 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
– Parts-of-Speech tagging
– Dependency Parsing
– Coreference Resolution
– Named entity recognition Using HMMs and Conditional
– Case Study: Spam Detector
– Singular Value Decomposition
– Topic Models
PART 2: MODERN NEURAL NATURAL LANGUAGE PROCESSING
– Computational Graphs
– Feed-forward Neural Network Language Models
– Measuring Model Performance: Likelihood and Perplexity
– 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
– Softmax Approximations: Negative Sampling, Hierarchical
– Parallel Training
– Bag of Words, Bag of n-grams, and Convolution
– Applications of Convolution: Context Windows and
– Stacked and Dilated Convolutions
– Structured Convolution
– Convolutional Models of Sentence Pairs
– Visualization for CNN’s
– Recurrent Networks
– Vanishing Gradient and LSTMs
– Strengths and Weaknesses of Recurrence in Sentence Modeling
– Pre-training for RNNs
– Sentence Similarity
– Textual Entailment
– Paraphrase Identification
– Conditional Generation and Search
– Types of Data to Condition On
– What do We Attend To?
– Improvements to Attention
– Specialized Attention Varieties
Introduction to Al & Nature of Intelligence
Introduction to Al & Nature of Intelligence
◦ Basic Concepts and algorithms
◦ Algorithmic Complexity
◦ Data Structures
Mathematics of Machine Learning
◦ Linear Algebra
◦ Multivariate Calculus
Exploratory Data Analysis & Feature Engineering
◦ Data Exploration and preprocessing
◦ Feature Engineering
Statistics and Probability for Data Scientists & Python Programming Language
Introduction to Machine Learning
◦ Learning from Data
◦ Supervised Machine Learning
◦ Neural networks and Introduction to Deep Learning
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.
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.
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
Our Unique Pedagogy
Reverse engineer the learning process of AI and ML by focussing on the application of the problem.
Explore the nature of data associated with the problem and explain methods of Exploratory Data Analysis.
Impart the intuition to solve a problem, design thinking, and various methods to solve an AI problem.
Introduce the intuition of the algorithm. Showcase a tool or library that can solve the problem.
The mathematical formulation and Hyperparameters of the algorithm.
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
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