Advanced Certification in AI and Machine Intelligence by CCE IIT Mandi

This program is the first and only of its kind in the entire online learning industry, where candidates learn the exact content delivered in the classes of B.Tech Data Science and Engineering course offered by IIT Mandi. In this Program, you will get to attend the live classroom conducted by IIT Mandi Professors through a live relay of sessions.

Duration: 9 Months - 550+ Hrs of Live Training

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Data Science & Machine Intelligence
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About Program

The demand for AI scientists is exponentially growing with focus on leveraging the technology of Machine Learning and Deep Learning to emulate better than human thinking in machines. Advanced certificate in AI and Machine Learning covers the 5 modules of certificate in Data science program, and in addition covers 4 advanced modules of Artificial Intelligence and Deep learning. AI Scientists leverage Deep Learning, Natural Language Processing (NLP), Computer Vision and Machine Learning; expected to work closely with data scientists, data engineers & product managers to incorporate AI-based capabilities into solutions

Key Features

  • 550+ Hrs of live sessions from B.Tech Data Science class of IIT Mandi
  • 250+ Hrs of Lab sessions
  • 100 Hrs of Mentoring session from Industry Experts
  • Multiple case studies and projects
  • One Week Immersion training at IIT Mandi Campus*
  • 3 Guaranteed Interviews from Rooman and NSDC
  • Recorded session of Live Class Room available on LMS
  • Course Completion Certificate from CCE IIT Mandi and NSDC
  • EMI Options available

*This is at an additional cost

Eligibility Criteria

STEM  graduates & Final Semester Students eager to start a career in Data Science/ Machine Learning.

Tools you learn at CCE IIT Mandi

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Career Growth

  • Business Analyst
  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • Artificial Intelligence Engineer
  • Business Intelligence Developer
  • Big Data Analyst
career growth infographic

About CCE IIT Mandi

IIT Mandi nurtures an interdisciplinary research environment that develops innovative technologies for widespread use. Driven by the need of the region and the nation, the thrust areas have been identified and organized. IIT Mandi has been ranked 20th among engineering colleges in India by the National Institutional Ranking Framework in 2022. It is ranked 12th among 23 IITs according to NIRF 2022.

Certification

Upon Completion of this course, you will:

  • Receive an Advanced Certification in Data Science and Machine Learning from CCE IIT Mandi and NSDC.
  • Job assistance through Rooman
advanced certification

Meet Our Faculties

Varun Dutt
Dr. Varun Dutt
Associate Professor
Dr. Varun Dutt is an Associate Professor at the IIT Mandi, in the School of Computing and Electrical Engineering. In 2011, he earned his Ph.D. degree from Carnegie Mellon University, USA. Dr. Dutt’s research focuses on the intersection of computer science, economics, and decision-making, with a specific emphasis on how to use computational and experimental models to take decisions on management, environmental, and socioeconomic issues. Dr. Dutt is a senior member of IEEE and he serves on the editorial board of several reputed journals like Frontiers in Psychology (cognitive science), Frontiers in Decision Neuroscience, and International Journal on Cyber Situational Awareness.
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Dr. Dileep A. D.
Associate Professor
Dr. Dileep A. D. has received his Masters (M.Tech) and Doctoral (Ph.D) degrees from Indian Institute of Technology Madras in 2006 and 2013 respectively. Presently, he is working as an Associate Professor at IIT Mandi in the School of Computing and Electrical Engineering (SCEE). His research area includes Spoken Language Recognition and Diarization and Applications of Machine Learning and Deep Learning.
mentors picture
Aditya Nigam
Associate Professor
Mr. Aditya Nigam has received his Masters (M.Tech) and Doctoral (Ph.D) degrees from Indian Institute of Technology Kanpur in 2009 and 2014 respectively. Presently, He is working as an Associate Professor at IIT Mandi in the School of Computing and Electrical Engineering (SCEE). His research area includes Biometrics, Computer Vision and Application of Deep Learning.

Course Curriculum

  • Calculus
  • Vector space
  • Eigenvalues and eigenvectors,
  • Introduction to optimization
  • Convex sets & functions & properties
  • Lagrange’s multiplier method
  • Basic Numerical Optimization.
  • Basics of Python
  • Functions
  • List , maps,Dictionary
  • Files
  • Strings
  • Numpy
  • Pandas
  • Scipy
  • Probability:
  • Random variables:
  • Standard deviation
  • Covariance
  • Correlation
  • Statistics
  • Data collection process
  • Data preprocessing 
  • Supervised learning with applications
    in classification problems
  • Supervised learning – regression
  • Unsupervised learning algorithms –
    Clustering
  • Data Analytics
  • SQL
  • MongoDB
  • Tabeleau
  • PowerBl
  • MS Excel
  • Data sources and collection
  • Data Pre-processing
  • Data representation
  • Basic charting and data visualization
  • PCA, SVD
  • clustering, gradient-based optimization methods
  • Geometry, Camera models
  • Probabilistic graphical model
  • Stereo disparity estimation
  • Features detection and tracking
  • Segmentation
  • High level vision
  • Synthesis:
  • Basics of artificial neural networks (ANN)
  • Feed forward neural networks
  • Deep neural networks (DNNs)
  • Convolution neural networks (CNNs)
  • Recurrent neural networks (RNNs)
    Generative models
  • Concepts of Artificial Development
  • Reinforcement Learning
  • Concept of Markov
  • Monte Carlo Methods

Projects

Projects will be a part of your Advanced Certification in Data Science & Machine Intelligence to consolidate your learning. It will ensure that you have real-world hands-on experience.

Project Preparing Power BI Dashboard

Power BI, a business intelligence and data visualisation application, allows you to use data from variety of sources to generate dynamic dashboards and analytical reports.

Read More

R - Shiny based app on Decision Tree

Shiny is a R package that enables the development of interactive web applications that may execute R code in the background.

Read More

EDA and Data Preprocessing Using Python

Exploratory data analysis (EDA) is a strategy or philosophy that uses a number of approaches (mainly graphical) to analyze data.

Read More

Project on Regression Using Cross Validation

In this Project you will be able to implement an end to end Pipeline including Data Cleaning, Preprocessing, Scaling, Transformation, feature engineering and from thereon you are going to feed the trasformed or engineered data in the machine learning models for regression.

Read More

Project on Classificaiton using Ensembling

When a category is the output variable, It is a classification algorith which can solve it. Thus a classification model tries to infer some meaning from the values that were seen. A categorization model will attempt to forecast the value of one or more outputs given one or more inputs.

Read More

Project on Ensembling with Stacking

The two most well-known ensemble modelling techniques are bagging and boosting. Bagging enables the averaging of several comparable models with significant volatility in order to reduce variance whereas Boosting creates several incremental models in order to reduce bias while minimizing variation.  

Read More

Time Series Project based on SARIMA

A time series is a collection of images taken over a period of time at equidistant intervals. Signal processing, pattern identification, econometrics, mathematical finance, weather forecasting, earthquake prediction, and other fields all employ time series.

Read More

Computer Vision based Project

Methods for capturing, processing, analysing, and comprehending digital pictures, as well as methods for extracting high-dimensional data from the actual world in order to create numerical or symbolic information in order to make right judgments, are all included in computer vision tasks.

Read More

ChatBot using Natural Langauge Processing

NLP, or natural language processing, enables computers to comprehend human language. Behind the scenes, NLP examines phrase structure and word meaning individually, then employs algorithms to extract meaning and provide results.

Read More

Placement Assistance

Data Science and Machine Intelligence

All eligible candidates will receive placement assistance after program completion

Data Science and Machine Intelligence

Access to Opportunities with Leading Companies

Data Science and Machine Intelligence

Workshops on Resume Review and Interview Preparation

Data Science and Machine Intelligence

Career Guidance and Mentorship by Industry Experts from Rooman and NSDC

Selection Process

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Program Fee

Duration: 9 Months

INR 90,000/- + GST

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Installment Options:

Full Payment (Inclusive of GST)

₹ 1,06,200/-

  • Within Five days of completing the admission process

2 Installments (Inclusive of GST)

₹ 53,600/-

  • 1st Installment- Within Five days of completing the admission process
  • 2nd Installment- Within Thirty days from the payment of first installment

3 Installments (Inclusive of GST)

₹ 36,400/-

  • 1st Installment- Within Five days of completing the admission process
  • 2nd Installment- Within Thirty days from the payment of first installment
  • 3rd Installment- Within Thirty days from the payment of Second installment

EMI Options:

₹ 8,900/Month

Frequently Asked Questions

Yes. You will get assured 3 interviews after the course completion. Our team will prepare you for the interview by conducting several mock interviews, assisting in resume building and more to crack the interviews with ease.

A computer system equipped with a web camera, microphone, and internet connection (minimum speed 1 Mbps) are the basic technical componential requirements for pursuing the course.

You may communicate with our mentors who are available during the session and the same doubts will be taken up by the IIT Mandi faculty during the session

The Advantage of 3

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  • Upskilling from IIT Professor’s
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  • Skill Loan
  • Assessment
  • Joint Certification
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  • Internships
  • Placements
  • Mentorship from Industry Experts
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1. Project Preparing Power BI Dashboard

Power BI, a business intelligence and data visualisation application, allows you to use data from variety of sources to generate dynamic dashboards and analytical reports. Users of Power BI may create their own reports and
dashboards using an easy to use interface and cloud based services for interactive visualisations. You will be able to create a dashboard utilising a range of visualisation techniques in the Power BI project, including location maps, scatter plots, and the associated trend line running through it. You can then use this line as the line of best fit to generate predictions.

2. R - Shiny based app on Decision Tree

Shiny is a R package that enables the development of interactive web applications that may execute R code in the background. Using Shiny, Dashboards are designed, interactive charts are included in R based documents, and standalone apps can be hosted online.

Shiny generates your code into the HTML, CSS, and JavaScript required to display your application on the web after you use R to develop a user interface and server. Any R calculation you can do on your desktop can be performed by the application because it is running R code on the backend.

In this Project you will be preparing a Decision Tree based Regression Tree and Random Forest and then we’ll be able to show results on the  web application. 

3. EDA and Data Preprocessing Using Python

Exploratory data analysis is the crucial process of doing preliminary analyses on data in order to find patterns, identify anomalies, test hypotheses, and double-check assumptions with the use of summary statistics and graphical representations. To put it another way, exploratory data analysis (EDA) is a strategy or philosophy that uses a number of approaches (mainly graphical) to analyse data.

Maximize understanding of a data collection, reveal underlying structure, extract crucial variables, find outliers and anomalies, test underlying hypotheses, build sparse models, and decide on the best factor settings. 

In this project we are going to take help of few datasets, there will be procedures to be followed in order to modify or encode data so that a computer can readily parse it, visualize it, understand the pattern of it so that our analysis is not biased to one particular sort of data and can extract the deeper insights out of it.

1. Project on Regression Using Cross Validation

In this Project you will be able to implement an end to end Pipeline including Data Cleaning, Preprocessing, Scaling, Transformation, feature engineering and from thereon you are going to feed the transformed or engineered data in the machine learning models for regression.

After trying with the different machine learning algorithms using cross validation techniques you can then cross examining your models based on variety of evaluation metrics including coefficient of determination, adjusted r2 score, AIC, BIC.

Regression Analysis is going to be understood with a full Statistical approach like where T-test is going to help us and what is meant by F-test to what are p values.

2. Project on Classification using Ensembling

When a category is the output variable, It is a classification algorithm which can solve it. Thus a classification model tries to infer some meaning from the values that were seen. A categorization model will attempt to forecast the value of one or more outputs given one or more inputs. 

Variety of Classification models exist including Logistic Regression, Decision Trees, SVM, and Naive Bayes. After trying with the different classification based machine learning algorithms using cross validation techniques you can then cross examine your models based on a variety of evaluation metrics including accuracy score, Threshold values, Confusion Matrix, TPR vs FPR, Precision vs Recall,  receiver operating characteristic and Area Under Curve.

In the end you are going to save the model architecture in the form of a pickle file so that it can be utilized elsewhere.

3. Project on Ensembling with Stacking

The two most well-known ensemble modelling techniques are bagging and boosting. Bagging enables the averaging of several comparable models with significant volatility in order to reduce variance whereas Boosting creates several incremental models in order to reduce bias while minimising variation. 

In this project you will be able to create a variety of models and use them to create intermediate predictions, one for every taught model. Then you include a new model that picks up on the same goal from the intermediate Prediction. This final model is said to be stacked on the top of the others, hence the name. Thus, you might improve your overall performance, and will be able to end up with a model which is better than any individual intermediate model.

1. Time Series Project based on SARIMA

A time series is a collection of images taken over a period of time at equidistant intervals. Signal processing, pattern identification, econometrics, mathematical finance, weather forecasting, earthquake prediction, and other fields all employ time series. Time series forecasting is the process of using a model to anticipate future values based on values that have already been observed.

In this Project you will be able extract the data and then out of it several lags will be created. Then on that new data you are going to check stationarity and if stationarity is not appropriate then data would have to undergo some other transformations like Differencing. Then with the help of correlogram appropriate autoregressive lags and autocorrelation lags are found out. With the help of these numbers Arima model or Sarima model can be utilized. Thus you will be able to make predictions or forecasting and will be able to see it with the help of visualization.

2. Computer Vision based Project

Methods for capturing, processing, analysing, and comprehending digital pictures, as well as methods for extracting high-dimensional data from the actual world in order to create numerical or symbolic information in order to make right judgments, are all included in computer vision tasks.

In this application, understanding refers to the conversion of visual pictures into descriptions of the outside world that make sense to cognitive processes and can prompt appropriate behavior. Using models created with the assistance of geometry, physics, statistics, and learning theory, this image comprehension may be thought of as the decoupling of symbolic information from picture data. 

In this project you will be able to train the images on the basis of their labels such that if a new similar image is brought in the computer can name it out. Python based Libraries like Keras, TensorFlow and concepts like CNN will be taken help of.

3. Chatbot using Natural Language Processing

NLP, or natural language processing, enables computers to comprehend human language. Behind the scenes, NLP examines phrase structure and word meaning individually, then employs algorithms to extract meaning and provide results. In other words, it interprets human language so that it may carry out various activities automatically. Chatbots are a well-known example of NLP in use. By automatically interpreting queries in everyday language and reacting, they assist support personnel in resolving problems. 

In this Project you will be able to create a chat bot from scratch using Feature Extraction and feature Engineering and then vectorizing and implementing word embedding on your corpus. In the end you will be able to receive replies from the chatbot on the basis of your questions. You will be able to take help of a few Python based libraries for Stemming and Lemmatization and Keras can be used in order to build LSTM/GRU based layers and train them.