Certification in Artificial Intelligence and Machine Learning by E&ICT Academy, IIT Guwahati
The program professionals need to get into the field of AI from Eckovation in collaboration with E&ICT Academy, IIT Guwahati
Next Batch Starting on
15th August 2020
₹199,999 ₹250,000
Limited Seats • Filling Fast
350+ Hrs of Content 8 Months (Only 60 seats. Filling fast)   Study 12hrs/Week

In Collaboration with:

E & ICT Academy
IIT-Guwahati

In Collaboration with:

E & ICT Academy
IIT-Guwahati

Estimated Time

8 Months (Only 60 seats. Filling fast)  

Commitment Level

12 Hours/Week

Prerequisites

Programming & Networking Basics

Why learn Machine Learning?
Number of Jobs
With increasing data processing capabilities of the world, the amount of data being captured is increasing at an exponential rate. This has created an exponential jump in the number of jobs for ML and AI developers cutting across industries. Job Openings for Machine Learning and AI developers are increasing by 400% each year in India.
Salary Potential
Machine Learning has become a critical aspect of decision making and business strategy. As the rate of new Ml and AI developers entering the job market is much slower as compared to new jobs being created, salaries for this role are not only high, they are growing faster than for any other job role. In general, starting pay packages for Ml and AI developers range between 15 to 40 lakhs per year.
Top Companies
Machine Learning is a role which is not limited to any industry. Any business you can think of, Machine Learning and Artificial Intelligence has a critical role to play in it today. Even companies like SpaceX, Google, Amazon, Walmart, Facebook, etc. are actively hiring for it.
Why this Course?
Certification by E&ICT Academy, IIT Guwahati
The curriculum offered by Eckovation in collaboration with E&ICT Academy, IIT Guwahati is the most comprehensive curriculum offered on the internet as of 2020.
Industry Oriented Learning
Top Experts from IITs and Industry Experts from Zomato, Microsoft, Adobe and Cisco will be providing you personalised guidance.
Personalised Mentorship
Eckovation guarantees one-to-one mentorship with Industry Experts from top companies throughout the duration of the course.
Live Classes
Learn theory and practical skills 8 hours per week with the course faculty.
Placement Assistance
With their technical skills enhanced and their soft skills honed, Eckovation students will have a much better chance of getting placed and hence achieve a better future for themselves.
Professional Networking
You will become a member of the ever-growing professional network of Eckovation Learners, from every sector of the industry, be it medical, IT, manufacturing, logistics, etc. You will build a relationship, trust and reputation with your skills in this program.
Professional Certificate
  • Get a professional certification from E&ICT Academy, IIT Guwahati
  • Add an extremely valuable asset to your Resume
  • Kickstart your career
Course Details
Video Lectures with Assessment Tests
Video lectures accompanied with assessment tests. Learn concepts through videos and assess your understanding through tests. Course Curriculum designed by online pedagogy experts.
Assignments and Case Studies
Apply the concepts you have learned by working on carefully designed assignments to exercise your problem-solving muscle. Get a flavour of real-world problems through case studies sourced from industry.
Projects
Complex real-industry problems demanding a thorough understanding of concepts, mastery over skill and understanding of the business context. Having worked on them would be a big highlight of your resume.
Personalised Mentorship
Top industry experts and IIT graduates would provide you personalised assistance in resolving doubts, working on assignments and a detailed review of your projects.
Learning Community
An online community of peers learning together, learning from each other, ensuring that learning remains the social experience that it ought to be.
Placement Assistance
Assistance to create a world-class resume,ensuring job interviews with top recruiters and preparing you for the recruitment process.
Curriculum
350+
Hours of Content
25
Industry Projects
100+
Assignments
8
Tools and Softwares

Fill Details

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Python Revision
4 Week duration, Multiple Assignments & Projects
15 Assignments
Mathematical Preliminaries
4 Week duration, Multiple Assignments & Projects
15 Assignments
Dimensionality Reduction
4 Week duration, Multiple Assignments & Projects
15 Assignments
Unsupervised Learning, Generative models and Pattern discovery
4 Week duration, Multiple Assignments & Projects
15 Assignments & 3 Projects
Supervised Learning and Discriminative Models
4 Week duration, Multiple Assignments & Projects
15 Assignments & 3 Projects
Module on Domains - Speech, Vision & NLP
4 Week duration, Multiple Assignments & Projects
15 Assignments & 12 Projects
Module on Data Analytics & Visualization
4 Week duration, Multiple Assignments & Projects
15 Assignments & 2 Projects
Deep Learning
4 Week duration, Multiple Assignments & Projects
15 Assignments & 10 Projects
Reinforcement learning
4 Week duration, Multiple Assignments & Projects
15 Assignments & 3 Projects
Machine Learning & Cloud (AWS, AZURE, GCP)
4 Week duration, Multiple Assignments & Projects
15 Assignments & 2 Projects
Capstone Project - Self-driving Car
4 Week duration, Multiple Assignments & Projects
The name suggests itself. The car that drives itself. Using computer vision the program detects various objects on and around the road and decides whether to keep moving or stop or turn. Google, Volvo, Tesla are trying to perfect their algorithm with the real world scenarios. This project is the most complex project that you will come across as a learner and if you succeed you are definitely to come in the radar of say Google or Tesla.
Completion Certificate
You will get certificates after completing all courses and assignments.
Estimated Time
8 Months (Only 60 seats. Filling fast)  
Commitment Level
12 Hours/Week
Tools Covered
Completion Certificate
You will get certificates after completing all courses and assignments.
Industry Projects
Learn through real-life industry projects inspired by top companies across industries
Face Feature Extraction
The face recognition system consists of a feature extraction step and a classification step. Principal component analysis (PCA) is widely used in such scenarios to construct the feature space and extract features, substantially reducing the dimensionality of the input feature vector/image. The reduced feature vector can then be used for the purpose of face analysis.
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Speech Classification
Linear Discriminant Analysis or LDA is a dimensionality reduction technique used to reduce the number of dimensions (i.e. variables) in a dataset while retaining as much information as possible. Using LDA based classification, we can find discriminative features for a given audio segment to achieve the task of Automatic Speech Classification such that speech belonging to the same class are close together, but samples from different classes are far apart from each other.
Know More
Exploratory Factor Analysis
Exploratory factor analysis is a statistical technique used to reduce data to a smaller set of summary variables and to explore the underlying structure of a relatively large set of variables. It is used to identify the underlying relationships between measured variables. Each observed variable is considered as a potential measure of every factor, and the goal is to determine the strongest relationships.
Know More
Face Datasets
A real-world recognition system copes with several unseen individuals and determine whether a given face image is registered or not and hence to achieve the same in this project you will learn & apply several hashing functions and classification methods.
Know More
Speech Classification
Linear Discriminant Analysis or LDA is a dimensionality reduction technique used to reduce the number of dimensions (i.e. variables) in a dataset while retaining as much information as possible. Using LDA based classification, we can find discriminative features for a given audio segment to achieve the task of Automatic Speech Classification such that speech belonging to the same class are close together, but samples from different classes are far apart from each other.
Know More
Exploratory Factor Analysis
Exploratory factor analysis is a statistical technique used to reduce data to a smaller set of summary variables and to explore the underlying structure of a relatively large set of variables. It is used to identify the underlying relationships between measured variables. Each observed variable is considered as a potential measure of every factor, and the goal is to determine the strongest relationships.
Know More
Face Datasets
A real-world recognition system copes with several unseen individuals and determine whether a given face image is registered or not and hence to achieve the same in this project you will learn & apply several hashing functions and classification methods.
Know More
Edge Detection in Images
Edges define the boundaries between different regions in an image, which helps in matching the pattern, segment, and recognize an object. In simple thresholding, the threshold value is global, which is prone to fail in many cases. Adaptive thresholding is a modified method where the threshold value is calculated for each pixel based on a smaller region around it. Therefore, there will be different threshold values for different regions which gives better results for images with varying illumination.
Know More
Exploratory Factor Analysis
Exploratory factor analysis is a statistical technique used to reduce data to a smaller set of summary variables and to explore the underlying structure of a relatively large set of variables. It is used to identify the underlying relationships between measured variables. Each observed variable is considered as a potential measure of every factor, and the goal is to determine the strongest relationships.
Know More
Face Datasets
A real-world recognition system copes with several unseen individuals and determine whether a given face image is registered or not and hence to achieve the same in this project you will learn & apply several hashing functions and classification methods.
Know More
Edge Detection in Images
Edges define the boundaries between different regions in an image, which helps in matching the pattern, segment, and recognize an object. In simple thresholding, the threshold value is global, which is prone to fail in many cases. Adaptive thresholding is a modified method where the threshold value is calculated for each pixel based on a smaller region around it. Therefore, there will be different threshold values for different regions which gives better results for images with varying illumination.
Know More
Image Segmentation
Image segmentation is the process of partitioning a digital image into multiple distinct regions containing each pixel with similar attributes i.e. classification of an image into different groups. There are different methods, and one of the most popular methods is the k-means clustering algorithm. K-Means clustering algorithm is an unsupervised algorithm, and it is used to segment the interest area from the background.
Know More
Face Datasets
A real-world recognition system copes with several unseen individuals and determine whether a given face image is registered or not and hence to achieve the same in this project you will learn & apply several hashing functions and classification methods.
Know More
Edge Detection in Images
Edges define the boundaries between different regions in an image, which helps in matching the pattern, segment, and recognize an object. In simple thresholding, the threshold value is global, which is prone to fail in many cases. Adaptive thresholding is a modified method where the threshold value is calculated for each pixel based on a smaller region around it. Therefore, there will be different threshold values for different regions which gives better results for images with varying illumination.
Know More
Image Segmentation
Image segmentation is the process of partitioning a digital image into multiple distinct regions containing each pixel with similar attributes i.e. classification of an image into different groups. There are different methods, and one of the most popular methods is the k-means clustering algorithm. K-Means clustering algorithm is an unsupervised algorithm, and it is used to segment the interest area from the background.
Know More
Pattern Discovery in Textures
The classical k-means method of clustering minimizes the sum of squared distances between cluster centres and cluster members. The intuition is that the radial distance from the cluster-centre should be similar for all elements of that cluster. The spherical k-means algorithm, however, is equivalent to the k-means algorithm with cosine similarity, a popular method for clustering high-dimensional data. The idea is to set the centre of each cluster such that it makes the angle between components both uniform and minimal.
Know More
Edge Detection in Images
Edges define the boundaries between different regions in an image, which helps in matching the pattern, segment, and recognize an object. In simple thresholding, the threshold value is global, which is prone to fail in many cases. Adaptive thresholding is a modified method where the threshold value is calculated for each pixel based on a smaller region around it. Therefore, there will be different threshold values for different regions which gives better results for images with varying illumination.
Know More
Image Segmentation
Image segmentation is the process of partitioning a digital image into multiple distinct regions containing each pixel with similar attributes i.e. classification of an image into different groups. There are different methods, and one of the most popular methods is the k-means clustering algorithm. K-Means clustering algorithm is an unsupervised algorithm, and it is used to segment the interest area from the background.
Know More
Pattern Discovery in Textures
The classical k-means method of clustering minimizes the sum of squared distances between cluster centres and cluster members. The intuition is that the radial distance from the cluster-centre should be similar for all elements of that cluster. The spherical k-means algorithm, however, is equivalent to the k-means algorithm with cosine similarity, a popular method for clustering high-dimensional data. The idea is to set the centre of each cluster such that it makes the angle between components both uniform and minimal.
Know More
Intrusion Detection by Visual Surveillance
Detection of abnormalities in live videos requires optimized scene representation which involves real-time detection of objects while efficiently representing the state of objects temporally across frames. For such purposes, Incremental Clustering can be used. Incremental clustering allows clustering of pixels with motion which is further used for mapping the trajectories in subsequent frames and can be used for Surveillance and for real-time traffic analysis.
Know More
Image Segmentation
Image segmentation is the process of partitioning a digital image into multiple distinct regions containing each pixel with similar attributes i.e. classification of an image into different groups. There are different methods, and one of the most popular methods is the k-means clustering algorithm. K-Means clustering algorithm is an unsupervised algorithm, and it is used to segment the interest area from the background.
Know More
Pattern Discovery in Textures
The classical k-means method of clustering minimizes the sum of squared distances between cluster centres and cluster members. The intuition is that the radial distance from the cluster-centre should be similar for all elements of that cluster. The spherical k-means algorithm, however, is equivalent to the k-means algorithm with cosine similarity, a popular method for clustering high-dimensional data. The idea is to set the centre of each cluster such that it makes the angle between components both uniform and minimal.
Know More
Intrusion Detection by Visual Surveillance
Detection of abnormalities in live videos requires optimized scene representation which involves real-time detection of objects while efficiently representing the state of objects temporally across frames. For such purposes, Incremental Clustering can be used. Incremental clustering allows clustering of pixels with motion which is further used for mapping the trajectories in subsequent frames and can be used for Surveillance and for real-time traffic analysis.
Know More
Construction of Hashing Tree
Hierarchical clustering is the hierarchical decomposition of the data based on group similarities. It allows us to build tree structures from data similarities and see how different sub-clusters relate to each other, and how far apart data points are. It gives us a tree-type structure based on the hierarchical series of nested clusters. A diagram called Dendrogram graphically represents this hierarchy and is an inverted tree that describes the order in which factors are merged, or clusters are broken apart.
Know More
Pattern Discovery in Textures
The classical k-means method of clustering minimizes the sum of squared distances between cluster centres and cluster members. The intuition is that the radial distance from the cluster-centre should be similar for all elements of that cluster. The spherical k-means algorithm, however, is equivalent to the k-means algorithm with cosine similarity, a popular method for clustering high-dimensional data. The idea is to set the centre of each cluster such that it makes the angle between components both uniform and minimal.
Know More
Intrusion Detection by Visual Surveillance
Detection of abnormalities in live videos requires optimized scene representation which involves real-time detection of objects while efficiently representing the state of objects temporally across frames. For such purposes, Incremental Clustering can be used. Incremental clustering allows clustering of pixels with motion which is further used for mapping the trajectories in subsequent frames and can be used for Surveillance and for real-time traffic analysis.
Know More
Construction of Hashing Tree
Hierarchical clustering is the hierarchical decomposition of the data based on group similarities. It allows us to build tree structures from data similarities and see how different sub-clusters relate to each other, and how far apart data points are. It gives us a tree-type structure based on the hierarchical series of nested clusters. A diagram called Dendrogram graphically represents this hierarchy and is an inverted tree that describes the order in which factors are merged, or clusters are broken apart.
Know More
Clustering Customer Data
The Agglomerative Clustering algorithms perform hierarchical clustering using a bottom-up approach. Each data point is treated as a single cluster, and then pairs of clusters are successively merged until all clusters have been merged into a single cluster that contains all data points. This approach is called hierarchical agglomerative clustering or HAC. DBSCAN, on the other hand, is a density-based clustered algorithm that does not require a pre-set number of clusters and is capable of identifying outliers as noises. It views clusters as areas of high density separated by areas of low density and thus, clusters found by DBSCAN can be any shape.
Know More
Intrusion Detection by Visual Surveillance
Detection of abnormalities in live videos requires optimized scene representation which involves real-time detection of objects while efficiently representing the state of objects temporally across frames. For such purposes, Incremental Clustering can be used. Incremental clustering allows clustering of pixels with motion which is further used for mapping the trajectories in subsequent frames and can be used for Surveillance and for real-time traffic analysis.
Know More
Construction of Hashing Tree
Hierarchical clustering is the hierarchical decomposition of the data based on group similarities. It allows us to build tree structures from data similarities and see how different sub-clusters relate to each other, and how far apart data points are. It gives us a tree-type structure based on the hierarchical series of nested clusters. A diagram called Dendrogram graphically represents this hierarchy and is an inverted tree that describes the order in which factors are merged, or clusters are broken apart.
Know More
Clustering Customer Data
The Agglomerative Clustering algorithms perform hierarchical clustering using a bottom-up approach. Each data point is treated as a single cluster, and then pairs of clusters are successively merged until all clusters have been merged into a single cluster that contains all data points. This approach is called hierarchical agglomerative clustering or HAC. DBSCAN, on the other hand, is a density-based clustered algorithm that does not require a pre-set number of clusters and is capable of identifying outliers as noises. It views clusters as areas of high density separated by areas of low density and thus, clusters found by DBSCAN can be any shape.
Know More
Gaussian mixture model
A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. It attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset allowing the model to learn automatically, i.e. in an unsupervised manner. The bag-of-words model is a way of representing text data when modelling text with machine learning algorithms which can be combined with GMM to get a useful model representation.
Know More
Construction of Hashing Tree
Hierarchical clustering is the hierarchical decomposition of the data based on group similarities. It allows us to build tree structures from data similarities and see how different sub-clusters relate to each other, and how far apart data points are. It gives us a tree-type structure based on the hierarchical series of nested clusters. A diagram called Dendrogram graphically represents this hierarchy and is an inverted tree that describes the order in which factors are merged, or clusters are broken apart.
Know More
Clustering Customer Data
The Agglomerative Clustering algorithms perform hierarchical clustering using a bottom-up approach. Each data point is treated as a single cluster, and then pairs of clusters are successively merged until all clusters have been merged into a single cluster that contains all data points. This approach is called hierarchical agglomerative clustering or HAC. DBSCAN, on the other hand, is a density-based clustered algorithm that does not require a pre-set number of clusters and is capable of identifying outliers as noises. It views clusters as areas of high density separated by areas of low density and thus, clusters found by DBSCAN can be any shape.
Know More
Gaussian mixture model
A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. It attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset allowing the model to learn automatically, i.e. in an unsupervised manner. The bag-of-words model is a way of representing text data when modelling text with machine learning algorithms which can be combined with GMM to get a useful model representation.
Know More
Object Tracking in Images
Object tracking is one of the most popular areas of video processing. The main purpose of object tracking is to estimate the position of the object in images continuously and reliably against dynamic scenes. This can be achieved by using the mean shift object tracking algorithm. The mean shift algorithm is an efficient approach to tracking objects whose appearance is defined by histograms. Thus, it can be used to track non-rigid objects by discovering clusters in a smooth density of samples.
Know More
Clustering Customer Data
The Agglomerative Clustering algorithms perform hierarchical clustering using a bottom-up approach. Each data point is treated as a single cluster, and then pairs of clusters are successively merged until all clusters have been merged into a single cluster that contains all data points. This approach is called hierarchical agglomerative clustering or HAC. DBSCAN, on the other hand, is a density-based clustered algorithm that does not require a pre-set number of clusters and is capable of identifying outliers as noises. It views clusters as areas of high density separated by areas of low density and thus, clusters found by DBSCAN can be any shape.
Know More
Gaussian mixture model
A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. It attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset allowing the model to learn automatically, i.e. in an unsupervised manner. The bag-of-words model is a way of representing text data when modelling text with machine learning algorithms which can be combined with GMM to get a useful model representation.
Know More
Object Tracking in Images
Object tracking is one of the most popular areas of video processing. The main purpose of object tracking is to estimate the position of the object in images continuously and reliably against dynamic scenes. This can be achieved by using the mean shift object tracking algorithm. The mean shift algorithm is an efficient approach to tracking objects whose appearance is defined by histograms. Thus, it can be used to track non-rigid objects by discovering clusters in a smooth density of samples.
Know More
Text Document Clustering
PLSA or Probabilistic Latent Semantic Analysis is a technique used to model information under a probabilistic framework. It is a statistical technique for the analysis of two-mode and co-occurrence data. PLSA characterizes each word in a document as a sample from a mixture model, where mixture components are conditionally independent multinomial distributions. Its main goal is to model cooccurrence information under a probabilistic framework in order to discover the underlying semantic structure of the data.
Know More
Gaussian mixture model
A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. It attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset allowing the model to learn automatically, i.e. in an unsupervised manner. The bag-of-words model is a way of representing text data when modelling text with machine learning algorithms which can be combined with GMM to get a useful model representation.
Know More
Object Tracking in Images
Object tracking is one of the most popular areas of video processing. The main purpose of object tracking is to estimate the position of the object in images continuously and reliably against dynamic scenes. This can be achieved by using the mean shift object tracking algorithm. The mean shift algorithm is an efficient approach to tracking objects whose appearance is defined by histograms. Thus, it can be used to track non-rigid objects by discovering clusters in a smooth density of samples.
Know More
Text Document Clustering
PLSA or Probabilistic Latent Semantic Analysis is a technique used to model information under a probabilistic framework. It is a statistical technique for the analysis of two-mode and co-occurrence data. PLSA characterizes each word in a document as a sample from a mixture model, where mixture components are conditionally independent multinomial distributions. Its main goal is to model cooccurrence information under a probabilistic framework in order to discover the underlying semantic structure of the data.
Know More
Market Basket Analysis
Association Rule Mining is used when we want to find an association between different objects in a set or find frequent patterns in a transaction database or relational databases. The applications of Association Rule Mining are found in Marketing, Basket Data Analysis (or Market Basket Analysis) in retailing, clustering and classification. It can be used to find what items do customers frequently buy together by generating a set of rules called Association Rules.
Know More
Object Tracking in Images
Object tracking is one of the most popular areas of video processing. The main purpose of object tracking is to estimate the position of the object in images continuously and reliably against dynamic scenes. This can be achieved by using the mean shift object tracking algorithm. The mean shift algorithm is an efficient approach to tracking objects whose appearance is defined by histograms. Thus, it can be used to track non-rigid objects by discovering clusters in a smooth density of samples.
Know More
Text Document Clustering
PLSA or Probabilistic Latent Semantic Analysis is a technique used to model information under a probabilistic framework. It is a statistical technique for the analysis of two-mode and co-occurrence data. PLSA characterizes each word in a document as a sample from a mixture model, where mixture components are conditionally independent multinomial distributions. Its main goal is to model cooccurrence information under a probabilistic framework in order to discover the underlying semantic structure of the data.
Know More
Market Basket Analysis
Association Rule Mining is used when we want to find an association between different objects in a set or find frequent patterns in a transaction database or relational databases. The applications of Association Rule Mining are found in Marketing, Basket Data Analysis (or Market Basket Analysis) in retailing, clustering and classification. It can be used to find what items do customers frequently buy together by generating a set of rules called Association Rules.
Know More
Anomalous Event Detection
Sequential pattern mining is a topic of data mining concerned with finding statistically consistent patterns between data examples where the values are delivered in a sequence. It helps discover patterns across time or positions in a given dataset. For any event, the sequence is typically ordered using timestamps. The goal of sequence mining is to discover interesting patterns in data with respect to some subjective and to find anomaly using the obtained pattern in data.
Know More
Text Document Clustering
PLSA or Probabilistic Latent Semantic Analysis is a technique used to model information under a probabilistic framework. It is a statistical technique for the analysis of two-mode and co-occurrence data. PLSA characterizes each word in a document as a sample from a mixture model, where mixture components are conditionally independent multinomial distributions. Its main goal is to model cooccurrence information under a probabilistic framework in order to discover the underlying semantic structure of the data.
Know More
Market Basket Analysis
Association Rule Mining is used when we want to find an association between different objects in a set or find frequent patterns in a transaction database or relational databases. The applications of Association Rule Mining are found in Marketing, Basket Data Analysis (or Market Basket Analysis) in retailing, clustering and classification. It can be used to find what items do customers frequently buy together by generating a set of rules called Association Rules.
Know More
Anomalous Event Detection
Sequential pattern mining is a topic of data mining concerned with finding statistically consistent patterns between data examples where the values are delivered in a sequence. It helps discover patterns across time or positions in a given dataset. For any event, the sequence is typically ordered using timestamps. The goal of sequence mining is to discover interesting patterns in data with respect to some subjective and to find anomaly using the obtained pattern in data.
Know More
Human Activity Recognition from Smart Phone Data
Recognizing human activities from temporal streams of sensory data observations is a very important task on a wide variety of applications in context recognition. Human activities are hierarchical in nature, i.e. the complex activities can be decomposed to several simpler ones. Human activity recognition is the problem of classifying sequences of accelerometer data recorded by pre-installed sensors in smart phones into known well-defined movements to make it ready for predictive modelling.
Know More
Market Basket Analysis
Association Rule Mining is used when we want to find an association between different objects in a set or find frequent patterns in a transaction database or relational databases. The applications of Association Rule Mining are found in Marketing, Basket Data Analysis (or Market Basket Analysis) in retailing, clustering and classification. It can be used to find what items do customers frequently buy together by generating a set of rules called Association Rules.
Know More
Anomalous Event Detection
Sequential pattern mining is a topic of data mining concerned with finding statistically consistent patterns between data examples where the values are delivered in a sequence. It helps discover patterns across time or positions in a given dataset. For any event, the sequence is typically ordered using timestamps. The goal of sequence mining is to discover interesting patterns in data with respect to some subjective and to find anomaly using the obtained pattern in data.
Know More
Human Activity Recognition from Smart Phone Data
Recognizing human activities from temporal streams of sensory data observations is a very important task on a wide variety of applications in context recognition. Human activities are hierarchical in nature, i.e. the complex activities can be decomposed to several simpler ones. Human activity recognition is the problem of classifying sequences of accelerometer data recorded by pre-installed sensors in smart phones into known well-defined movements to make it ready for predictive modelling.
Know More
Handwritten Digit Recognition
Digit recognition system is the working of a machine to train itself for recognizing the digits from different sources like emails, bank cheque, papers, images, etc. and in different real-world scenarios for online handwriting recognition on computer tablets or system. Developing such a system includes a machine to understand and classify the images of handwritten digits as 10 digits (0–9). Handwritten digits from the MNIST database has been one of the most famous databases among the machine learning community for many recent decades.
Know More
Anomalous Event Detection
Sequential pattern mining is a topic of data mining concerned with finding statistically consistent patterns between data examples where the values are delivered in a sequence. It helps discover patterns across time or positions in a given dataset. For any event, the sequence is typically ordered using timestamps. The goal of sequence mining is to discover interesting patterns in data with respect to some subjective and to find anomaly using the obtained pattern in data.
Know More
Human Activity Recognition from Smart Phone Data
Recognizing human activities from temporal streams of sensory data observations is a very important task on a wide variety of applications in context recognition. Human activities are hierarchical in nature, i.e. the complex activities can be decomposed to several simpler ones. Human activity recognition is the problem of classifying sequences of accelerometer data recorded by pre-installed sensors in smart phones into known well-defined movements to make it ready for predictive modelling.
Know More
Handwritten Digit Recognition
Digit recognition system is the working of a machine to train itself for recognizing the digits from different sources like emails, bank cheque, papers, images, etc. and in different real-world scenarios for online handwriting recognition on computer tablets or system. Developing such a system includes a machine to understand and classify the images of handwritten digits as 10 digits (0–9). Handwritten digits from the MNIST database has been one of the most famous databases among the machine learning community for many recent decades.
Know More
Television Commercial Recognition
TV Advertising plays an important role in our lives. Automatic real-time recognition of TV commercials is an essential step for TV broadcast monitoring and ad monitoring services. It comprises of two primary tasks: rapid detection of known commercials that are stored in a database, and accurate recognition of unknown ones that appear for the first time in TV streaming.
Know More
Human Activity Recognition from Smart Phone Data
Recognizing human activities from temporal streams of sensory data observations is a very important task on a wide variety of applications in context recognition. Human activities are hierarchical in nature, i.e. the complex activities can be decomposed to several simpler ones. Human activity recognition is the problem of classifying sequences of accelerometer data recorded by pre-installed sensors in smart phones into known well-defined movements to make it ready for predictive modelling.
Know More
Handwritten Digit Recognition
Digit recognition system is the working of a machine to train itself for recognizing the digits from different sources like emails, bank cheque, papers, images, etc. and in different real-world scenarios for online handwriting recognition on computer tablets or system. Developing such a system includes a machine to understand and classify the images of handwritten digits as 10 digits (0–9). Handwritten digits from the MNIST database has been one of the most famous databases among the machine learning community for many recent decades.
Know More
Television Commercial Recognition
TV Advertising plays an important role in our lives. Automatic real-time recognition of TV commercials is an essential step for TV broadcast monitoring and ad monitoring services. It comprises of two primary tasks: rapid detection of known commercials that are stored in a database, and accurate recognition of unknown ones that appear for the first time in TV streaming.
Know More
Object Detection by Dl
Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition.
Know More
Handwritten Digit Recognition
Digit recognition system is the working of a machine to train itself for recognizing the digits from different sources like emails, bank cheque, papers, images, etc. and in different real-world scenarios for online handwriting recognition on computer tablets or system. Developing such a system includes a machine to understand and classify the images of handwritten digits as 10 digits (0–9). Handwritten digits from the MNIST database has been one of the most famous databases among the machine learning community for many recent decades.
Know More
Television Commercial Recognition
TV Advertising plays an important role in our lives. Automatic real-time recognition of TV commercials is an essential step for TV broadcast monitoring and ad monitoring services. It comprises of two primary tasks: rapid detection of known commercials that are stored in a database, and accurate recognition of unknown ones that appear for the first time in TV streaming.
Know More
Object Detection by Dl
Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition.
Know More
Face Detection
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Because faces are so complicated, there isn’t a straightforward test that will tell if we found a face or not. Instead, thousands of small patterns and features must be matched for accurate facial detection.
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Television Commercial Recognition
TV Advertising plays an important role in our lives. Automatic real-time recognition of TV commercials is an essential step for TV broadcast monitoring and ad monitoring services. It comprises of two primary tasks: rapid detection of known commercials that are stored in a database, and accurate recognition of unknown ones that appear for the first time in TV streaming.
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Object Detection by Dl
Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition.
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Face Detection
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Because faces are so complicated, there isn’t a straightforward test that will tell if we found a face or not. Instead, thousands of small patterns and features must be matched for accurate facial detection.
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Text Detection
Text detection is the process of detecting the text present in the image, followed by surrounding it with a rectangular bounding box. Text detection can be carried out using image-based techniques or machine learning-based techniques. In image-based techniques, an image is segmented into multiple segments, and statistical features of connected components are utilised to form the text. Whereas, Machine learning approaches use support vector machine and convolutional neural networks to classify the components into text and non-text.
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Object Detection by Dl
Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition.
Know More
Face Detection
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Because faces are so complicated, there isn’t a straightforward test that will tell if we found a face or not. Instead, thousands of small patterns and features must be matched for accurate facial detection.
Know More
Text Detection
Text detection is the process of detecting the text present in the image, followed by surrounding it with a rectangular bounding box. Text detection can be carried out using image-based techniques or machine learning-based techniques. In image-based techniques, an image is segmented into multiple segments, and statistical features of connected components are utilised to form the text. Whereas, Machine learning approaches use support vector machine and convolutional neural networks to classify the components into text and non-text.
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Object Detection by DL
Object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification model and seeks to localize precisely where in the image each object appears.When performing object detection, given an input image, we wish to obtain the object bounding box, the class or the label the object belongs to and the probability of the object belonging to the predicted class.
Know More
Face Detection
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Because faces are so complicated, there isn’t a straightforward test that will tell if we found a face or not. Instead, thousands of small patterns and features must be matched for accurate facial detection.
Know More
Text Detection
Text detection is the process of detecting the text present in the image, followed by surrounding it with a rectangular bounding box. Text detection can be carried out using image-based techniques or machine learning-based techniques. In image-based techniques, an image is segmented into multiple segments, and statistical features of connected components are utilised to form the text. Whereas, Machine learning approaches use support vector machine and convolutional neural networks to classify the components into text and non-text.
Know More
Object Detection by DL
Object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification model and seeks to localize precisely where in the image each object appears.When performing object detection, given an input image, we wish to obtain the object bounding box, the class or the label the object belongs to and the probability of the object belonging to the predicted class.
Know More
Face Detection, Recognition & Search by DL
Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Once the facial region is obtained, we can use deep learning methods such as CNNs to extract a wide range of features from images. Deep neural networks can be used to produce a bunch of numbers each of which describes a face (known as face encodings) and can be used for both facial recognition and search.
Know More
Text Detection
Text detection is the process of detecting the text present in the image, followed by surrounding it with a rectangular bounding box. Text detection can be carried out using image-based techniques or machine learning-based techniques. In image-based techniques, an image is segmented into multiple segments, and statistical features of connected components are utilised to form the text. Whereas, Machine learning approaches use support vector machine and convolutional neural networks to classify the components into text and non-text.
Know More
Object Detection by DL
Object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification model and seeks to localize precisely where in the image each object appears.When performing object detection, given an input image, we wish to obtain the object bounding box, the class or the label the object belongs to and the probability of the object belonging to the predicted class.
Know More
Face Detection, Recognition & Search by DL
Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Once the facial region is obtained, we can use deep learning methods such as CNNs to extract a wide range of features from images. Deep neural networks can be used to produce a bunch of numbers each of which describes a face (known as face encodings) and can be used for both facial recognition and search.
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Speech – Music Recognition
Deep learning techniques such as RNN or CNNs can be used for classification of an audio segment. Given input audio, the trained model can predict the genre or class the audio segment belongs to which can be used for music/speech analysis. For example, using this approach, we can detect the language being spoken or the instrument being played.
Know More
Object Detection by DL
Object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification model and seeks to localize precisely where in the image each object appears.When performing object detection, given an input image, we wish to obtain the object bounding box, the class or the label the object belongs to and the probability of the object belonging to the predicted class.
Know More
Face Detection, Recognition & Search by DL
Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Once the facial region is obtained, we can use deep learning methods such as CNNs to extract a wide range of features from images. Deep neural networks can be used to produce a bunch of numbers each of which describes a face (known as face encodings) and can be used for both facial recognition and search.
Know More
Speech – Music Recognition
Deep learning techniques such as RNN or CNNs can be used for classification of an audio segment. Given input audio, the trained model can predict the genre or class the audio segment belongs to which can be used for music/speech analysis. For example, using this approach, we can detect the language being spoken or the instrument being played.
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Applications in Natural Language Processing
Natural Language Processing (NLP) is concerned with machine-human collaboration. It helps computers read, interpret, and understand the human language so that machines can carry our repetitive and high-volume tasks. It is the field of Artificial Intelligence (AI) that focuses on quantifying human language to make it intelligible to machines by combining the power of linguistics and computer science to study the rules and structure of language and create intelligent systems.
Know More
Face Detection, Recognition & Search by DL
Face detection can be regarded as a specific case of object-class detection, which focuses on the detection of frontal human faces. Once the facial region is obtained, we can use deep learning methods such as CNNs to extract a wide range of features from images. Deep neural networks can be used to produce a bunch of numbers each of which describes a face (known as face encodings) and can be used for both facial recognition and search.
Know More
Speech – Music Recognition
Deep learning techniques such as RNN or CNNs can be used for classification of an audio segment. Given input audio, the trained model can predict the genre or class the audio segment belongs to which can be used for music/speech analysis. For example, using this approach, we can detect the language being spoken or the instrument being played.
Know More
Applications in Natural Language Processing
Natural Language Processing (NLP) is concerned with machine-human collaboration. It helps computers read, interpret, and understand the human language so that machines can carry our repetitive and high-volume tasks. It is the field of Artificial Intelligence (AI) that focuses on quantifying human language to make it intelligible to machines by combining the power of linguistics and computer science to study the rules and structure of language and create intelligent systems.
Know More
Visual Question Answering
Visual Question Answering (VQA) is a computer vision task where a system is given a text-based question about an image, and it must infer the answer. Questions can be arbitrary, and they can belong to many sub-problems in computer vision, e.g., Object recognition - What is in the image? In general, we can define a VQA system as an algorithm that takes as input an image and a natural language question about the image and generates a natural language answer as the output.
Know More
Speech – Music Recognition
Deep learning techniques such as RNN or CNNs can be used for classification of an audio segment. Given input audio, the trained model can predict the genre or class the audio segment belongs to which can be used for music/speech analysis. For example, using this approach, we can detect the language being spoken or the instrument being played.
Know More
Applications in Natural Language Processing
Natural Language Processing (NLP) is concerned with machine-human collaboration. It helps computers read, interpret, and understand the human language so that machines can carry our repetitive and high-volume tasks. It is the field of Artificial Intelligence (AI) that focuses on quantifying human language to make it intelligible to machines by combining the power of linguistics and computer science to study the rules and structure of language and create intelligent systems.
Know More
Visual Question Answering
Visual Question Answering (VQA) is a computer vision task where a system is given a text-based question about an image, and it must infer the answer. Questions can be arbitrary, and they can belong to many sub-problems in computer vision, e.g., Object recognition - What is in the image? In general, we can define a VQA system as an algorithm that takes as input an image and a natural language question about the image and generates a natural language answer as the output.
Know More
Text Classification in DL
Text classification is the process of assigning tags or categories to text according to its content. It is one of the fundamental tasks in Natural Language Processing (NLP) with broad applications such as sentiment analysis, topic labelling, spam detection, and intent detection. Text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.
Know More
Applications in Natural Language Processing
Natural Language Processing (NLP) is concerned with machine-human collaboration. It helps computers read, interpret, and understand the human language so that machines can carry our repetitive and high-volume tasks. It is the field of Artificial Intelligence (AI) that focuses on quantifying human language to make it intelligible to machines by combining the power of linguistics and computer science to study the rules and structure of language and create intelligent systems.
Know More
Visual Question Answering
Visual Question Answering (VQA) is a computer vision task where a system is given a text-based question about an image, and it must infer the answer. Questions can be arbitrary, and they can belong to many sub-problems in computer vision, e.g., Object recognition - What is in the image? In general, we can define a VQA system as an algorithm that takes as input an image and a natural language question about the image and generates a natural language answer as the output.
Know More
Text Classification in DL
Text classification is the process of assigning tags or categories to text according to its content. It is one of the fundamental tasks in Natural Language Processing (NLP) with broad applications such as sentiment analysis, topic labelling, spam detection, and intent detection. Text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.
Know More
Machine Translation by DL
Machine translation or MT refers to fully automated software that can translate source content into target languages. Humans may use MT to help them render text and speech into another language .nMT tools are often used to translate vast amounts of information involving millions of words that could not possibly be translated the traditional way. It is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
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Instructors
Dr. Prithwajit Guha
Dr. Guha is a renowned lecturer and researcher in the fields of Computer Vision, Pattern Recognition, Signal Processing, Robotics. He has numerous publications in his name and has been teaching in IIT Guwahati for past 7 yrs.
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Dr. Ashish Anand
Ashish, an IIT Kanpur alumni, is a faculty member at the Dept of CSE, Indian Institute of Technology Guwahati. He did his Masters in Mathematics and Scientific Computing from IITK. Thereafter, as a research student he joined Androgen Receptor Laboratory (University of Helsinki, Finland.  Later he joined a collaborative project of Prof. Pradip Sinha and Prof. K Deb (at IIT Kanpur) to understand neoplastic cancer in the model organism D. Melanagastor. He did his Ph.D. from Nanyang Technological University on Computational Intelligence Methods for Problems in Computational Biology. In particular, he worked on multi-class classification, template clustering for short time series data and imbalanced binary classification problems. And prior to joining IIT G, he was part of the European Consortium, BaSySBio at Systems Biology Lab (Group Leader: Dr Benno Schwikowski) Institut Pasteur, Paris. His post-doc work was mainly concentrated on regulatory network reconstruction and pathway analysis. 
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Srijan Gupta
Besides the B.Tech degree, Srijan has also gained a Minor in Computer Science and has worked extensivey in the field in technology. Now as the Director of Technology he will be joining the course as one of the Industry Expert.
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Bishal Shaw
Bishal Shaw is a Research Scholar in Machine Learning, Pattern Recognition and Computer Vision at IIT Guwahati. He has delivered numerous lectures & projects in Machine Learning and related fields. His expertise is being well utilized as a Teaching Assistant in this PG Certification program.
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Over 500 Careers Transformed
Thousands of learners have graduated successfully from Eckovation Courses. Who better than our alumni to tell you what learning on Eckovation is like.
See all Reviews
Akshay Mittal
2.3 Years of Experience

Engineer @ Calance Software Pvt. Ltd.
"Eckovation is the new mainstream now for learning, I have been pursuing the course of AI and ML for the past one month and the experience till now has been very satisfactory and fulfilling. The course is also planned in such a way that doesnot hinder people like me who are also working professionals. The classes are scheduled in such a way that the student has to go through a topic multiple times by using assignments and doubt clearence classes. The faculty also has good spoken communication as well as student focus mindset which no doubt leads to deep understanding."
Kshitij Agarwal
19.9 Years of Experience

Advisor @ DXC Technology India Limited(Working with Ford UK)
"IIT Faculty is excellent. They explain the topics in detail and also encourages to ask questions during lectures/guided assignments. Eckovation platform is very good and easy to use and at the same time it is user friendly. Course Progress is tracked automatically and reminders of all events are served in a very proactive manner.All the lectures recordings and material to go through is available online for 24x7 access. Eckovation team is very supportive and always ready to provide help in a best possible manner. I hope by end of this program, I should have fair knowledge of ML and AI subject which I can use in my professional career to move forward."
Kshitij Agarwal
19.9 Years of Experience

Advisor @ DXC Technology India Limited(Working with Ford UK)
"IIT Faculty is excellent. They explain the topics in detail and also encourages to ask questions during lectures/guided assignments. Eckovation platform is very good and easy to use and at the same time it is user friendly. Course Progress is tracked automatically and reminders of all events are served in a very proactive manner.All the lectures recordings and material to go through is available online for 24x7 access. Eckovation team is very supportive and always ready to provide help in a best possible manner. I hope by end of this program, I should have fair knowledge of ML and AI subject which I can use in my professional career to move forward."
Manish Joshi
2.3 Years of Experience

Former Engineer @ Capgemini
"Eckovation helped me a lot to strengthen my skills in Machine learning and AI in python Programming. This is best place to explore, The mentors are very good and helped in very difficult situation during our projects. The best part are the assignments in which you can practice after each lecture. That help us to gain our skills and the Eckovation team helps to make work on it. Thanks to Eckovation team to access all the utilities provided by them across the Globe."
Manish Joshi
2.3 Years of Experience

Former Engineer @ Capgemini
"Eckovation helped me a lot to strengthen my skills in Machine learning and AI in python Programming. This is best place to explore, The mentors are very good and helped in very difficult situation during our projects. The best part are the assignments in which you can practice after each lecture. That help us to gain our skills and the Eckovation team helps to make work on it. Thanks to Eckovation team to access all the utilities provided by them across the Globe."
Partha Goswami
4.7 Years of Experience

Senior Project Associate @ IIT Bombay
"IIT G Faculty is really good and the course content is excellent. The interactive doubt sessions, assignments, and guided projects in the curriculum apart from the lectures have helped me learn these concepts in great depth."
Partha Goswami
4.7 Years of Experience

Senior Project Associate @ IIT Bombay
"IIT G Faculty is really good and the course content is excellent. The interactive doubt sessions, assignments, and guided projects in the curriculum apart from the lectures have helped me learn these concepts in great depth."
Akshay Mittal
2.3 Years of Experience

Engineer @ Calance Software Pvt. Ltd.
"Eckovation is the new mainstream now for learning, I have been pursuing the course of AI and ML for the past one month and the experience till now has been very satisfactory and fulfilling. The course is also planned in such a way that doesnot hinder people like me who are also working professionals. The classes are scheduled in such a way that the student has to go through a topic multiple times by using assignments and doubt clearence classes. The faculty also has good spoken communication as well as student focus mindset which no doubt leads to deep understanding."
Thousands of learners have graduated successfully from Eckovation Courses. Who better than our alumni to tell you what learning on Eckovation is like.
Application Process
Submit Application
Tell us about your experience, academic qualifications, and a bit more in a detailed form. Don’t worry, we’ve kept it simple.
Interview
A telephonic interview with an industry expert who will gauge your passion and eligibility for the program.
Scholarship & Offer Letter
Apply for the scholarship (not mandatory) and take the test. At last, you will receive an offer letter to join the program if you are selected for the program.
Admission
Complete the admission & other formalities within 7 days of registration.
Payment Methods
No Cost EMI
We have partnered with the many finance companies to provide easy and flexible EMI options with 0% interest rate
Starting @
₹ 18,625 / month
Other Options
We provide the following options for one-time payment

Internet Banking

Credit/Debit Card

Wallets/UPIs
Total Course Fees
₹ 1,99,000
* Inclusive of GST
Commonly Asked Questions
Where do I attend these live classes?
You can attend these classes from anywhere on your laptop with an internet connection. You can join the class through a link shared to all the students before the beginning of the class.
How do I enroll in the course?
You can directly click on the Enroll Now button given above to start the enrolment process.
What is the payment structure?
The payment for the course can be made through Credit Card, Debit Card, Net Banking. One can either complete the process with direct on-time payment or with equal monthly instalments.
What does seat booking or token amount mean?
Seat booking or token amount is a non-refundable amount to be paid to book your slot in the batch so that you can pay the total fee on later due date and not worry about missing the batch seat, which are on first come first serve basis.
What should I expect once the enrollment is complete?
Once the enrolment is done, you will receive an email from Eckovation stating all the directions of how to proceed further and what to expect before and during the course.
What if I miss a class?
All the lectures will be available for the students to view in Eckovation platform after the class is over. However, you will not be able to ask direct questions to the Professor or TAs for that class.
Is there any attendance or minimum score policy for the course?
Yes, the minimum attendance policy for the course is 60% and minimum marks for successful course completion is 40%.
What if I do not want to continue the course after I have made my payment?
If you do not wish to continue the course you can ask for a refund within 7days of payment or commencement of the course, whichever is later. For Instalments, once the refund deadline is over, there won’t be any refunds, including future pending EMIs.
How much time do I have for completing my final project?
The course lectures will be for 8 months. You will have 2 months duration after that to submit your final project for evaluation.
How will the placement assistance work?
The placement assistance will be provided to the students who successfully complete the course. We will help you build your resume as per the course's job profile, take mock interview sessions and get you interviews in various companies.
What is the deferral or refund policy for this program?
Refund Policy: (Programs with Live-session component)

Student must pay an amount of ₹20,000 as seat blocking amount for the enrollment of the course. This will be adjustable against the total course fee payable by the student.You can claim a refund for the this amount at any time before the cohort start date by sending an email request to Admissions Counselor with reasons listed.

The prep login will be activated post completion of the 'payment of the seat block amount or on the 'Specified date' as communicated by Eckovation support team. Processing fee of ₹10,000 will be levied in case refund is claimed.

Once the student pays seat block amount, refund shall be subject to deduction of ₹10,000 processing charges. Also any taxes paid by the student shall not be refundable.

There shall be no refund applicable once the program has started. This is applicable even for those students who could not complete their payment and could not be enrolled in the batch opted for. However, the student can avail pre-deferral as per the policy defined below for the same.Refund shall be processed to an eligible student within 30 working days from the date of receipt of refund form from him/her in this regard.

Deferral Policy: (Post Program Commencement)

If a student is facing severe issues in dedicating time to the course, we provide the opportunity for the student to defer to another batch.A student can request for deferral only once and from the batch start date of initial batch the student enrolled for.

The student will be required to pay deferral fees of Rs 5000 +Taxes if any along with the differential program fees between the two cohorts.The deferral request will be approved once the deferral fee is paid.

Till this is completed, the student will be assumed to be continuing in the same cohort.
The student has 1 Week (including holidays and weekends) from the date of deferral request to make the payment of the deferral fee post which the deferral request will expire, and the student will continue as part of the current cohort.

If the student completes the deferral payment, the student will leave the deferred cohort and will start learning on the new cohort.

Deferral Policy: (Pre Program Commencement)

If a student, due to unavoidable circumstances is unable to commence with the cohort and requests for a deferral before the cohort starts, we provide the opportunity for the student to defer to another batch.

However, the student will be required to pay 50% of the total course fee amount (inclusive of taxes) before the deferral can be approved. Till this is completed, the student will be assumed to be continuing in the same cohort.

A student can request for deferral only once and from the batch start date of initial batch the student enrolled for.

The student has time till the current cohort launch date to make the payment of the 50% program fee, post which the deferral request will expire. Once the deferral window expires and the student now asks for a refund, the above-mentioned applicable refund policy will apply.

The fee applicable to the deferred student will be as per prevailing fee for the batch student as opted to defer to.
What will the be duration of Campus Immersion Module?
One week campus Immersion modules at IIT-Guwahati during which participants will visit the Campus to interact with their peers and learn from faculty. However this might be delayed due to the current Covid-19 situation.
What will the be timings of the live-sessions?
Live Sessions (Lecture or Doubt Sessions) will be scheduled post 8PM IST on any of the weekdays (Monday to Saturday). The Sunday sessions can be in morning, afternoon or evening. If any learner has any specific timing preferences then they can request the learner support team. The support team tries to accomodate all the incoming requests.

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