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About the Program

Hero Vired’s integrated program in Data Science, Machine Learning and Artificial Intelligence is designed to give you the right tools to become indispensable in the future that is driven by smart technologies and predictive analytics. With this course, you get to be at the forefront of innovation and become the thought leader and game changer the world needs.

Live Online Classes

Program Delivery

11 Months

Program Duration

December, 2021

Program Start Date

UG Degree

Program Eligibility

INR 4,25,000+ Applicable Taxes,

Program Fees (0% EMIs Available*)

The MIT community is driven by a shared purpose: to make a better world through education, research, and innovation. MIT graduates have invented fundamental technologies, launched new industries, and created millions of American jobs. At the same time, and without the slightest sense of contradiction, MIT is profoundly global. Through teaching, research, and innovation, MIT’s exceptional community pursues its mission of service to the nation and the world. MIT faculty members play a vital role in shaping the Institute’s vibrant campus community — as advisors, coaches, heads of houses, mentors, committee members, and much more.

Why Data Science, ML & AI
from Hero Vired?

70-90% of all learning will be live instructor-led with live classes and guided learning with faculty on live sessions, doubt-clearing sessions and project work

With a strong academic and industry focus, the program provides hands-on project based learning on real data

You will learn end to end programming, including data engineering and MLOPs

The program is designed to deliver global academic quality with complete Indian contextualisation for a holistic experience

Once you complete this program, you will be able to create your own ML algorithms and libraries from scratch. You won’t be dependent on existing libraries

Certification

On the successful completion of this program, you will receive a certificate from Hero Vired in collaboration with MIT, MicroMasters® credentials, and postgraduate certification from BML Munjal University*.



*Terms & Conditions Apply.

Program Curriculum

This is what you will learn from the program.

Overview of Analytics:

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.

Learning Outcomes:

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

About the Module:

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

Learning Outcomes:

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 Bl tools – concepts of data warehouse and database; using Power Bl to conduct basic data warehouse reporting; Power Bl to conduct visualization of descriptive statist

About the Module:

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.

Learning Outcomes:

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

About the Module:

This mathematics module 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.

Learning Outcomes:

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.

About the Module:

This MITx course will cover all the basic probability concepts, including multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, the main tools of Bayesian inference methods, and an introduction to random processes (Poisson processes and Markov chains).

Learning Outcomes:

Learn the basic structure and elements of probabilistic models, random variables, their distributions, means, and variances, probabilistic calculations, inference methods, laws of large numbers and their applications, random processes.

About the Module:

In this module, 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 modelling, reinforcement learning; online algorithms, support vector machines, and neural networks / deep learning.

Learning Outcomes:

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.

About the Module:

This MITx course start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study common models and methods to analyze specific types of data in four different domain areas, viz. epigenetic codes and data visualization, criminal networks and network analysis, prices, economics and time series, and environmental data and spatial statistics.

Learning Outcomes:

Model, form hypotheses, perform statistical analysis on real data Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data.
Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks.
Model time series using moving average, autoregressive and other stationary models for forecasting with financial data.
Use Gaussian processes to model environmental data and make predictions.
Communicate analysis results effectively.

About the Module:

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.

Learning Outcomes:

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.

About the Module:

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.

Learning Outcomes:

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.

About the Module:

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

Learning Outcomes:

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.

About the Module:

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

Learning Outcomes:

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.

About the Module:

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.

Learning Outcomes:

Understanding of concepts such as the “black box· of Al, 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.

About the Module:

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

Learning Outcomes:

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