- About
- Program Highlights
- Topics
- Certification
- Pedagogy
- Curriculum
- Faculty
- Fee Structure
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
Modeling
– EM for sound separation
– Probabilistic Context-Free Grammars (PCFG), the Inside-Outside
Algorithm
– 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
PART 2: MODERN NEURAL NATURAL LANGUAGE PROCESSING
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
SoftMax
– 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
Curriculum
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
- A bachelor’s degree or higher in STEM fields
- Some experience with Python, SQL, Statistics, and Calculus
- Minimum of 2 Years of Software Engineering/Data Science Experience
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
Innosential.
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
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
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
