You're not made to be a cog in the wheel.You're made to design the whole machine.

DATA SCIENCE,MACHINELEARNING & AI

FORBUSINESS

herovired.com/vired-for-business

CURRICULUM COVERAGE

MODULE ABOUT THE MODULE LEARNING OUTCOMES

Overview of Analytics, Machine Learning and Python Programming

This module will focus on understanding key analytics concepts, solutions, and modus operandi, through real-world business use-cases.

The module will additionally also introduce you to the core ideas of Machine Learning and programming on Python.

Understand why and how businesses use analytics through use-cases; the qualities of a good analyst; analytics methodologies and problem definitions; and the CRISP-DM architecture.

Understand the goal of machine learning; elements of supervised learning, and the difference between the training set and the test set; the difference of classification and regression - two representative kinds of supervised learning.

Introduced to python environments and ML packages, concept of Object-Oriented Programming, programming for a live environment, debugging, IDEs, and python basics such as class, objects, functions, conditions, loops/iterators, array, dictionary, lambda, mathematical and statistical operations, numpy for matrix algebra, exception handling, and file handling.

Getting Started with Data

In this module, you will be introduced to Business Intelligence tools and the key concepts behind working with data on Python.

Understand data sources – data availability, free/open-source/organizational data, policies and guidelines related to importing data, types of data sources.

Understand types of data – qualitative v/s quantitative; categorical v/s numerical.

Understand how to import data on Python – import/read data from different sources; import/read data of different file formats; metadata and data dictionaries; store data in required structures like dataframes; tools/packages/libraries to perform the above operations using python or other tool.

Introduction to BI tools – concepts of data warehouse and database; using Power BI to conduct basic data warehouse reporting; Power BI to conduct visualization of descriptive statistics

Data Gym with Python

This module will focus on techniques involved in converting raw data into readable format which can be perceived by a machine and be used further in ML applications.

Get a strong grasp of the following techniques:

Data formatting

Data description – high-level overview of data, dimension of data, data types of all features; summary statistics of data

Data manipulation

Data sanity checks

Exploratory Data Analysis – univariate and bivariate analysis

Data Pre-processing – missing value and outlier treatment

Feature Engineering – scaling and normalization, variable creation

MODULE ABOUT THE MODULE LEARNING OUTCOMES

Fundamentals of Statistics

This MITx course offers an introduction to the theoretical foundations of statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.

Formulate a statistical problem in mathematical terms, from a real-life situation, formulate a statistical problem.

Understand the role of mathematics in the design and analysis of statistical methods.

Select appropriate statistical methods.

Understand the implications and limitations of various methods.

Expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

Machine Learning with Python–From Linear Models to Deep Learning

In this MITx course, you will learn about principles and algorithms for turning training data into effective automated predictions.

Topics covered will include – representation, over-fitting, regularization, generalization, VC dimension; clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; on-line algorithms, support vector machines, and neural networks/deep learning.

Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning.

Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models.

Choose suitable models for different applications.

Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.

MODULE ABOUT THE MODULE LEARNING OUTCOMES

Advanced Concepts in Machine Learning

This module will delve deeper into more advanced concepts in machine learning with python in linear regression, linear classifiers, tree- and ensemble-based models, non-linear classification, recommender systems, unsupervised learning, reinforcement learning, neural networks, natural language processing, image and video analytics.

Understand more advanced concepts in Machine Learning, and learn how to implement and analyze more complex algorithms.

Delve deeper into the fundamental concepts of classic ML algorithms such as linear regression, logistic regression, and neural networks.

Explore and analyze additional clustering and reinforcement learning algorithms.

Implement natural language processing applications such as text similarity, sentiment analysis, text classification, and deep transfer learning with transformers.

Implement image and video analytics applications such as handwriting recognition, object classification, object detection, and semantic segmentation.

Concepts of MLOps

This module will uncover some of the concepts and tools behind operationalizing ML models, to ensure that these can generate business benefits by optimizing, automating and scaling the ML project pipeline.

Learn how to make a web service online; operationalize a ML pipeline.

Use Azure ML Studio, specifically AutoML and ML Designer, to demonstrate key components of MLOps.

Familiarized with prototyping using Flask.

Data Visualization & Storytelling

This module will use a blend of data visualization, analytics reporting, and summary statistics to craft a narrative which is anchored by compelling visuals.

Understand the principles of data storytelling.

Solid grasp on buidling visualizations on Tableau.

Understand the importance of ex-post storytelling by visualizing model outcomes, and post-modelling reporting of predications, and performance metrics.

Data Science in the Indian context

In this module, you will be introduced to the quirks and nuances of conducting data science and analytics in the Indian context.

Understand primary and secondary data research in India while dealing with data availability; working with open and semi-open datasets, and non-traditional supplementary sources of data.

MODULE ABOUT THE MODULE LEARNING OUTCOMES

Explainable AI This module will take you through a set of tools and frameworks to help understand and interpret predictions made by ML models, with a view to de-mystifying models’ behaviour so that businesses can understand, appropriately trust, and effectively manage ML projects.

Understanding of concepts such as the “black box” of AI, accuracy versus explainability, interpretability versus explainability, the tree approach, sensitivity analysis, layer-wise relevance propagation, algorithmic generalization for deeper control and understanding.

Understanding techniques for improving explainability of models such as feature importance, local interpretable model-agnostic explanations (LIME), Shapley values, partial dependence plots, and DeepLIFT.

Other Tools for Machine Learning

This module will introduce tools other than Python for data science and machine learning.

Implement a ML project workflow using CRISP-DM concepts on R and KNIME.

KEY FACULTY PROFILES

Dipyaman Sanyal

• Ph.D Economics• CFA Institute, US: Charter Holder• Master of Science (MS), Applied Economics, The University of Texas at Dallas

Experience

Co-Founder & CEOdōnō consulting

Adjunct FacultyNorthwestern University

Program DirectorJigsaw Academy

GunnvantSingh SainiMBA Management – UIAMS, Panjab University

Experience

Senior Consultant - Data Sciencedōnō consulting

Manager Content StrategyupGrad

SME: Data and InsightsJigsaw Academy

SubhashisMajumder• PhD in Computer Science & Engineering• M.Tech in Computer Science

Experience

Professor & HODHeritage Institute of Technology

Professor & Course DirectorIIIT

General Manager, TechnicaAstralsys Software

Dr. AnirbanChakraborti• Doctorate in PhD, Physics - Jadavpur University• Habilitation à diriger des recherches, Physics, Université Pierre et Marie Curie (Paris VI)

Experience

Dean of Research, Dean of School of Engineering and TechnologyBML Munjal University

ProfessorJawaharlal Nehru University

KEY FACULTY PROFILES

SnehamoyMukherjeeBachelors & Masters in Mathematics& Scientific Computing

Experience

Senior DirectorAxtria - Integenious Insights

Professor of Business Analytics, AIMLGreat Lakes Institute of Management

Analytics Practice HeadTechnopak Advisors

Rudra S.PGCHRM, MBA with specialization in HRM

Experience

Team Lead ManagereBay

ConsultantAccenture

Assistant ManagerHSBC Card Services

Assistant Manager & Team LeadGenpact

Gaurav SinghB.Tech Mechanical Engineering - IIT Bombay

Experience

Associate DirectorAxtria - Ingenious Insights

Assistant ManagerGenpact LLC

Manager AnalyticsICICI Bank

Business AnalystSynygy

Sanjay ThawakarBig Data Analytics, Big Data, Machine Learning & Data Science

Experience

Corporate Vice President & Head, AI WorksMax Life Insurance

Enterprise Analytics PracticeCognizant

Want more information on the program?Reach us at 1800 309 3939 | Visit us at www.herovired.com

FORBUSINESS

herovired.com/vired-for-business