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    Aman Vishnoi

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    Sai Kumar Yava

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Comprehensive Curriculum

12+

Industry Projects

9+

Languages & Tools

The curriculum of the PGP in Artificial Intelligence & Machine Learning is created in consultation with industry experts, academicians & program alums to ensure you learn the most cutting-edge topics.

    Foundations

    The Foundations module comprises of two courses where we get our hands dirty with Statistics and Code, head-on. These two courses set our foundations so that we sail through the rest of the journey with minimal hindrance.

    Python for AI & ML

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    3 Quizzes

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    1 Project

    This course will let us get comfortable with the Python programming language used for Artificial Intelligence and Machine Learning. We start with a high-level idea of Object-Oriented Programming and later learn the essential vocabulary(/keywords), grammar(/syntax) and sentence formation(/usable code) of this language. This course will drive you from introducing AI and ML to the core concepts using one of the most popular and advanced programming languages, Python.

    • Python Basics
    • Python is a widely used high-level programming language and has a simple, easy-to-learn syntax that highlights readability. This module will help you drive through all the fundamentals of programming in Python, and at the end, you will execute your first Python program.

    • Jupyter notebook – Installation & function
    • You will learn to implement Python for AI and ML using Jupyter Notebook. This open-source web application allows us to create and share documents containing live code, equations, visualisations, and narrative text.

    • Python functions, packages and routines
    • Functions and Packages are used for code reusability and program modularity, respectively. This module will help you understand and implement Functions and Packages in Python for AI.

    • Pandas, NumPy, Matplotlib, Seaborn
    • This module will give you a deep understanding of exploring data sets using Pandas, NumPy, Matplotlib, and Seaborn. These are the most widely used Python libraries.

    • Working with data structures,arrays, vectors & data frames
    • Data Structures are one of the most significant concepts in any programming language. They help in the arrangement of leader-board games by ranking each player. They also help in speech and image processing for AI and ML. In this module, you will learn Data Structures like Arrays, Lists, Tuples, etc. and learn to implement Vectors and Data Frames in Python.

    Applied Statistics

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    3 Quizzes

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    1 Project

      Here we learn the terms and concepts vital to Exploratory Data Analysis and Machine Learning in general. From the very basics of taking a simple average to the advanced process of finding statistical evidence to confirm or deny conjectures and speculations, we will learn a specific set of tools required to analyze and draw actionable insights from data.

    • Descriptive Statistics
    • The study of data analysis by describing and summarising several data sets is known as Descriptive Analysis. It can either be a sample of a region’s population or the marks achieved by 50 students. This module will help you understand Descriptive Statistics in Python for Machine Learning.

    • Inferential Statistics
    • This module will let you explore fundamental concepts of using data for estimation and assessing theories using Python.

    • Probability & Conditional Probability
    • Probability is a mathematical tool used to study randomness like the possibility of an event occurrence in a random experiment. Conditional Probability is the possibility of an event occurring given that several other events have also occurred. In this module, you will learn about Probability and Conditional Probability in Python for Machine Learning.

    • Probability Distributions - Types of distribution – Binomial, Poisson & Normal distribution
    • A statistical function reporting all the probable values that a random variable takes within a specific range is known as a Probability Distribution. This module will teach you about Probability Distributions and various types like Binomial, Poisson, and Normal Distribution in Python.

    • Hypothesis Testing
    • This module will teach you about Hypothesis Testing in Machine Learning using Python. Hypothesis Testing is a necessary procedure in Applied Statistics for doing experiments based on the observed/surveyed data.

    Machine Learning

    The next module is the Machine Learning online course that will teach us all the Machine Learning techniques from scratch, and the popularly used Classical ML algorithms that fall in each of the categories.

    Supervised Learning

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    4 Quizzes

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    1 Project

      In this course we learn about Supervised ML algorithms, working of the algorithms and their scope of application - Regression and Classification.

    • Multiple Variable Linear regression
    • Linear Regression is one of the most popular ML algorithms used for predictive analysis in Machine Learning, resulting in producing the best outcomes. It is a technique assuming a linear relationship between the independent variable and dependent variable.

    • Multiple regression
    • Multivariate Regression is a supervised machine learning algorithm involving multiple data variables for analysis. It is used for predicting one dependent variable using various independent variables. This module will drive you through all the concepts of Multiple Regression used in Machine Learning.

    • Logistic regression
    • Logistic Regression is one of the most popular ML algorithms, like Linear Regression. It is a simple classification algorithm to predict the categorical dependent variables with the assistance of independent variables. This module will drive you through all the concepts of Logistic Regression used in Machine Learning.

    • K-NN classification
    • k-NN Classification or k-Nearest Neighbours Classification is one of the most straightforward machine learning algorithms for solving regression and classification problems. You will learn about the usage of this algorithm through this module.

    • Naive Bayes classifiers
    • Naive Bayes Algorithm is used to solve classification problems using Baye’s Theorem. This module will teach you about the theorem and solving the problems using it.

    • Support vector machines
    • Support Vector Machine or SVM is also a popular ML algorithm used for regression and classification problems/challenges. You will learn how to implement this algorithm through this module.

    Unsupervised Learning

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    2 Quizzes

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    1 Project

      We learn what Unsupervised Learning algorithms are, working of the algorithms and their scope of application - Clustering and Dimensionality Reduction.

    • K-means clustering
    • K-means clustering is a popular unsupervised learning algorithm to resolve the clustering problems in Machine Learning or Data Science. In this module, you will learn how the algorithm works and later implement it.

    • Hierarchical clustering
    • Hierarchical Clustering is an ML technique or algorithm to build a hierarchy or tree-like structure of clusters. For example, it is used to combine a list of unlabeled datasets into a cluster in the hierarchical structure. This module will teach you the working and implementation of this algorithm.

    • High-dimensional clustering
    • High-dimensional Clustering is the clustering of datasets by gathering thousands of dimensions.

    • Dimension Reduction-PCA
    • Principal Component Analysis for Dimensional Reduction is a technique to reduce the complexity of a model like eliminating the number of input variables for a predictive model to avoid overfitting. Dimension Reduction-PCA is a well-known technique in Python for ML, and you will learn everything about this method in this module.

    Ensemble Techniques

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    2 Quizzes

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    1 Project

      In this Machine Learning online course, we discuss supervised standalone models’ shortcomings and learn a few techniques, such as Ensemble techniques to overcome these shortcomings.

    • Decision Trees
    • Decision Tree is a Supervised Machine Learning algorithm used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.

    • Random Forests
    • Random Forest is a popular supervised learning algorithm in machine learning. As the name indicates, it comprises several decision trees on the provided dataset’s several subsets. Then, it calculates the average for enhancing the dataset’s predictive accuracy.

    • Bagging
    • Bagging, also known as Bootstrap Aggregation, is a meta-algorithm in machine learning used for enhancing the stability and accuracy of machine learning algorithms, which are used in statistical classification and regression.

    • Boosting
    • As the name suggests, Boosting is a meta-algorithm in machine learning that converts robust classifiers from several weak classifiers. Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting.

    Featurization, Model Selection & Tuning

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    2 Quizzes

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    1 Project

      Learn various concepts that will be useful in creating functional machine learning models like model selection and tuning, model performance measures, ways of regularisation, etc.

    • Feature engineering
    • Feature engineering is transforming data from the raw state to a state where it becomes suitable for modelling. It converts the data columns into features that are better at representing a given situation in terms of clarity. Quality of the component in distinctly representing an entity impacts the model’s quality in predicting its behaviour. In this module, you will learn several steps involved in Feature Engineering.

    • Model selection and tuning
    • This module will teach you which model best suits architecture by evaluating every individual model based on the requirements.

    • Model performance measures
    • In this module, you will learn how to optimise your machine learning model’s performance using model evaluation metrics.

    • Regularising Linear models
    • In this module, you will learn the technique to avoid overfitting and increase model interpretability.

    • ML pipeline
    • This module will teach you how to automate machine learning workflows using the ML Pipeline. You can operate the ML Pipeline by enabling a series of data to be altered and linked together in a model, which can be tested and evaluated to achieve either a positive or negative result.

    • Bootstrap sampling
    • Bootstrap Sampling is a machine learning technique to estimate statistics on population by examining a dataset with replacement.

    • Grid search CV
    • Grid search CV is the process of performing hyperparameter tuning to determine the optimal values for any machine learning model. The performance of a model significantly depends on the importance of hyperparameters. Doing this process manually is a tedious task. Hence, we use GridSearchCV to automate the tuning of hyperparameters.

    • Randomized search CV
    • Randomized search CV is used to automate the tuning of hyperparameters similar to Grid search CV. Randomized search CV is provided for a random search, and Grid search CV is provided for a grid search.

    • K fold cross-validation
    • K-fold cross-validation is a way in ML to improve the holdout method. This method guarantees that our model’s score does not depend on how we picked the train and test set. The data set is divided into k number of subsets, and the holdout method is repeated k number of times.

    Self-paced Module: Demystifying ChatGPT and Applications

      Gain an understanding of what ChatGPT is and how it works, as well as delve into the implications of ChatGPT for work, business, and education. Additionally, learn about prompt engineering and how it can be used to fine-tune outputs for specific use cases.

    • Overview of ChatGPT and OpenAI
    • Timeline of NLP and Generative AI
    • Frameworks for understanding ChatGPT and Generative AI
    • Implications for work, business, and education
    • Output modalities and limitations
    • Business roles to leverage ChatGPT
    • Prompt engineering for fine-tuning outputs
    • Practical demonstration and bonus section on RLHF

    Introduction to SQL

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    3 Quizzes

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    1 Project

      Here, we will cover everything you need to know about SQL programming, such as DBMS, Normalization, Joins, etc.

    • Introduction to DBMS
    • Database Management Systems (DBMS) is a software tool where you can store, edit, and organise data in your database. This module will teach you everything you need to know about DBMS.

    • ER diagram
    • An Entity-Relationship (ER) diagram is a blueprint that portrays the relationship among entities and their attributes. This module will teach you how to make an ER diagram using several entities and their attributes.

    • Schema design
    • Schema design is a schema diagram that specifies the name of record type, data type, and other constraints like primary key, foreign key, etc. It is a logical view of the entire database.

    • Key constraints and basics of normalization
    • Key Constraints are used for uniquely identifying an entity within its entity set, in which you have a primary key, foreign key, etc. Normalization is one of the essential concepts in DBMS, which is used for organising data to avoid data redundancy. In this module, you will learn how and where to use all key constraints and normalization basics.

    • Joins
    • As the name implies, a join is an operation that combines or joins data or rows from other tables based on the common fields amongst them. In this module, you will go through the types of joins and learn how to combine data.

    • Subqueries involving joins and aggregations
    • This module will teach you how to work with subqueries/commands that involve joins and aggregations.

    • Sorting
    • As the name suggests, Sorting is a technique to arrange the records in a specific order for a clear understanding of reported data. This module will teach you how to sort data in any hierarchy like ascending or descending, etc.

    • Independent subqueries
    • The inner query that is independent of the outer query is known as an independent subquery. This module will teach you how to work with independent subqueries.

    • Correlated subqueries
    • The inner query that is dependent on the outer query is known as a correlated subquery. This module will teach you how to work with correlated subqueries.

    • Analytic functions
    • A function that determines values in a group of rows and generates a single result for every row is known as an Analytic Function.

    • Set operations
    • The operation that combines two or more queries into a single result is called a Set Operation. In this module, you will implement various set operators like UNION, INTERSECT, etc.

    • Grouping and filtering
    • Grouping is a feature in SQL that arranges the same values into groups using some functions like SUM, AVG, etc. Filtering is a powerful SQL technique, which is used for filtering or specifying a subset of data that matches specific criteria.

    Artificial Intelligence

    The next module is the Artificial Intelligence online course that will teach us from the introduction to Artificial Intelligence to taking us beyond the traditional ML into Neural Nets’ realm. We move on to training our models with Unstructured Data like Text and Images from the regular tabular data.

    Introduction to Neural Networks and Deep Learning

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    3 Quizzes

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    1 Project

      In this Artificial Intelligence online course, we start with the motive behind using the terms Neural network and look at the individual constituents of a neural network. Installation of and building familiarity with TensorFlow library, appreciate the simplicity of Keras and build a deep neural network model for a classification problem using Keras. We also learn how to tune a Deep Neural Network.

    • Gradient Descent
    • Gradient Descent is an iterative process that finds the minima of a function. It is an optimisation algorithm that finds the parameters or coefficients of a function’s minimum value. However, this function does not always guarantee to find a global minimum and can get stuck at a local minimum. In this module, you will learn everything you need to know about Gradient Descent.

    • Introduction to Perceptron & Neural Networks
    • Perceptron is an artificial neuron, or merely a mathematical model of a biological neuron. A Neural Network is a computing system based on the biological neural network that makes up the human brain. In this module, you will learn all the neural networks’ applications and go much deeper into the perceptron.

    • Batch Normalization
    • Normalisation is a technique to change the values of numeric columns in the dataset to a standard scale, without distorting differences in the ranges of values. In Deep Learning, rather than just performing normalisation once in the beginning, you’re doing it all over the network. This is called batch normalisation. The output from the activation function of a layer is normalised and passed as input to the next layer.

    • Activation and Loss functions
    • Activation Function is used for defining the output of a neural network from several inputs. Loss Function is a technique for prediction error of neural networks.

    • Hyper parameter tuning
    • This module will drive you through all the concepts involved in hyperparameter tuning, an automated model enhancer provided by AI training.

    • Deep Neural Networks
    • An Artificial Neural Network (ANN) having several layers between the input and output layers is known as a Deep Neural Network (DNN). You will learn everything about deep neural networks in this module.

    • Tensor Flow & Keras for Neural Networks & Deep Learning
    • TensorFlow is created by Google, which is an open-source library for numerical computation and wide-ranging machine learning. Keras is a powerful, open-source API designed to develop and evaluate deep learning models. This module will teach you how to implement TensorFlow and Keras from scratch. These libraries are widely used in Python for AIML.

    Computer Vision

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    3 Quizzes

    quizz

    2 Projects

      In this Computer Vision course, we will learn how to process and work with images for Image classification using Neural Networks. Going beyond plain Neural Networks, we will also learn a more advanced architecture - Convolutional Neural Networks.

    • Introduction to Image data
    • This module will teach you how to process the image and extract all the data from it, which can be used for image recognition in deep learning.

    • Introduction to Convolutional Neural Networks
    • Convolutional Neural Networks (CNN) are used for image processing, classification, segmentation, and many more applications. This module will help you learn everything about CNN.

    • Famous CNN architectures
    • In this module, you will learn everything you need to know about several CNN architectures like AlexNet, GoogLeNet, VGGNet, etc.

    • Transfer Learning
    • Transfer learning is a research problem in deep learning that focuses on storing knowledge gained while training one model and applying it to another model.

    • Object detection
    • Object detection is a computer vision technique in which a software system can detect, locate, and trace objects from a given image or video. Face detection is one of the examples of object detection. You will learn how to detect any object using deep learning algorithms in this module.

    • Semantic segmentation
    • The goal of semantic segmentation (also known as dense prediction) in computer vision is to label each pixel of the input image with the respective class representing a specific object/body.

    • Instance Segmentation
    • Object Instance Segmentation takes semantic segmentation one step ahead in a sense that it aims towards distinguishing multiple objects from a single class. It is considered as a Hybrid of Object Detection and Semantic Segmentation tasks.

    • Other variants of convolution
    • This module will drive you several other essential variants in Convolutional Neural Networks (CNN).

    • Metric Learning
    • Metric Learning is a task of learning distance metrics from supervised data in a machine learning manner. It focuses on computer vision and pattern recognition.

    • Siamese Networks
    • A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that contains two or more identical subnetworks which means they have the same configuration with the same parameters and weights. This module will help you find the similarity of the inputs by comparing the feature vectors of subnetworks.

    • Triplet Loss
    • In learning a projection where the inputs can be distinguished, the triplet loss is similar to metric learning. The triplet loss is used for understanding the score vectors for the images. You can use the score vectors of face descriptors for verifying the faces in Euclidean Space.

    Natural Language Processing

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    4 Quizzes

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    2 Projects

      Learn how to work with natural language processing with Python using traditional machine learning methods. Then, deep dive into the realm of Sequential Models and state of the art language models.

    • Introduction to NLP
    • Natural language processing applies computational linguistics to build real-world applications that work with languages comprising varying structures. We try to teach the computer to learn languages, and then expect it to understand it, with suitable, efficient algorithms. This module will drive you through the introduction to NLP and all the essential concepts you need to know.

    • Preprocessing text data
    • Text preprocessing is the method to clean and prepare text data. This module will teach you all the steps involved in preprocessing a text like Text Cleansing, Tokenization, Stemming, etc.

    • Bag of Words Model
    • Bag of words is a Natural Language Processing technique of text modelling. In technical terms, we can say that it is a method of feature extraction with text data. This approach is a flexible and straightforward way of extracting features from documents. In this module, you will learn how to keep track of words, disregard the grammatical details, word order, etc.

    • TF-IDF
    • TF is the term frequency (TF) of a word in a document. There are several ways of calculating this frequency, with the simplest being a raw count of instances a word appears in a document. IDF is the inverse document frequency(IDF) of the word across a set of documents. This suggests how common or rare a word is in the entire document set. The closer it is to 0, the more common is the word.

    • N-grams
    • An N-gram is a series of N-words. They are broadly used in text mining and natural language processing tasks.

    • Word2Vec
    • Word2vec is a method to create word embeddings by using a two-layer neural network efficiently. It was developed by Tomas Mikolov et al. at Google in 2013 to make the neural-network-based training of the embedding more efficient and since then has become the de facto standard for developing pre-trained word embedding.

    • GLOVE
    • GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm, which is an alternate method to create word embeddings. It is based on matrix factorisation techniques on the word-context matrix.

    • POS Tagging & Named Entity Recognition
    • We have learned the differences between the various parts of speech tags such as nouns, verbs, adjectives, and adverbs in elementary school. Associating each word in a sentence with a proper POS (part of speech) is known as POS tagging or POS annotation. POS tags are also known as word classes, morphological classes, or lexical tags. NER, short for, Named Entity Recognition is a standard Natural Language Processing problem which deals with information extraction. The primary objective is to locate and classify named entities in text into predefined categories such as the names of persons, organisations, locations, events, expressions of times, quantities, monetary values, percentages, etc.

    • Introduction to Sequential models
    • A sequence, as the name suggests, is an ordered collection of several items. In this module, you will learn how to predict what letter or word appears using the Sequential model in NLP.

    • Need for memory in neural networks
    • This module will teach you how critical is the need for memory in Neural Networks.

    • Types of sequential models – One to many, many to one, many to many
    • In this module, you will go through all the types of Sequential models like one-to-many, many-to-one, and many-to-many.

    • Recurrent Neural networks (RNNs)
    • An artificial neural network that uses sequential data or time-series data is known as a Recurrent Neural Network. It can be used for language translation, natural language processing (NLP), speech recognition, and image captioning.

    • Long Short Term Memory (LSTM)
    • LSTM is a type of Artificial Recurrent Neural Network that can learn order dependence in sequence prediction problems.

    • GRU
    • Great Recurrent Unit (GRU) is a gating mechanism in RNN. You will learn all you need to about the mechanism in this module.

    • Applications of LSTMs
    • You will go through all the significant applications of LSTM in this module.

    • Sentiment analysis using LSTM
    • An NLP technique to determine whether the data is positive, negative, or neutral is known as Sentiment Analysis. The most commonly used example is Twitter.

    • Time series analysis
    • Time-Series Analysis comprises methods for analysing data on time-series to extract meaningful statistics and other relevant information. Time-Series forecasting is used to predict future values based on previously observed values.

    • Neural Machine Translation
    • Neural Machine Translation (NMT) is a task for machine translation that uses an artificial neural network, which automatically converts source text in one language to the text in another language.

    • Advanced Language Models
    • This module will teach several other widely used and advanced language models used in NLP.

    Capstone Project

    You will get your hands dirty with a real-time project under industry experts’ guidance from introducing you to Python to the introduction to artificial intelligence and machine learning and everything in between Python for AIML. Successful completion of the project will earn you a post-graduate certificate in artificial intelligence and machine learning.

    Career Assistance: Resume building and Mock interviews

    This post-graduate certificate program on artificial intelligence and machine learning will guide you through your career path to building your professional resume, attending mock interviews to boost your confidence and nurture you nailing your professional interviews.

    PG Certificate from Great Lakes & UT Austin

    Earn a Postgraduate Certificate in the top-rated Artificial Intelligence and Machine Learning online course from Great Lakes & UT Austin. Its exhaustive Curriculum will foster you into a highly-skilled professional and help you land a job at the world’s leading corporations.

Languages and Tools covered

Python numpy Keras tensorflow matplotlib scikit Seaborn Statsmodels

Hands-On Case Studies

  • 1

    Automobile

    Classifying Silhouettes Of Vehicles

    Classify a given silhouette as one of three types of vehicles, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.
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  • 2

    Object detection

    Face Mask Segmentation

    Predict and apply masks over the faces within images using CNN and image recognition algorithms. In this hands-on project, the goal is to build a system, which includes building a face detector to locate the position of a face in an image and apply a segmentation mask on the face.
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  • 3

    E-commerce

    Product Recommendation System

    Online E-commerce websites like Amazon, Flipkart uses different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time. Use similar concepts to create your own product recommendation system.
    Learn More
  • 4

    Entertainment

    Sentiment Analysis Using LSTM

    Word embedding is a type of word representation that allows words with similar meaning to have a similar representation. It is a distributed representation for the text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems. We will use the IMDb dataset to learn word embedding as we train our dataset. This dataset contains 25,000 movie reviews from IMDB, labeled with a sentiment.
    Learn More
  • 5

    Cement manufacturing

    Predicting The Strength Of High-Performance Concrete

    Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. You will predict the strength of concrete using different concepts of ML.
    Learn More
  • 6

    Object recognition

    Detecting Digits On Vehicle Number Plates

    Recognizing multi-digit numbers in photographs captured at street level is an important component of modern-day map making. A classic example of a corpus of such street-level photographs is Google’s Street View imagery composed of hundreds of millions of geo-located 360-degree panoramic images. The ability to automatically transcribe an address number from a geolocated patch of pixels and associate the transcribed number with a known street address helps pinpoint, with a high degree of accuracy, the location of the building it represents.
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  • 7

    Computer Vision

    Face Recognition

    Recognize, identify, and classify faces within images using CNN and image recognition algorithms. In this hands-on project, the goal is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces.
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  • 8

    News

    Sarcasm Detection

    Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets. In this hands-on project, the goal is to build a model to detect whether a sentence is sarcastic or not, using Bidirectional LSTMs.
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Learner Testimonials

The wealth of knowledge that the faculty members come with is what I liked the most about the course.

Bishnoo Ananth

Senior Manager

I started looking at things from a broader perspective and this has helped me in my workplace as well.

Madhukar Anant

Senior Lead- Analytics

The professors were able to explain the topic in the real depth of it and also handle it in a simple way.

Madhan Seduraman

Solutions Lab - Artificial Intelligence

The capstone project gave me an opportunity to optimize and automate the best practises used in the industry.

Shiva Rama Krishna Reddy

Senior System Architect

The course provides good balance of less theory and more practical application of different techniques covered through sessions taken by renowned faculty.

Arjun Anand Ganapathy

Software Development

Program Fees

You can also pay the entire fee of ₹ 3,50,000 + GST

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First American

Pay in Full

Discount % Available
Discounted Fee :

₹ 3,37,289 + GST

Fee waiver of ₹ 15,000 on making lumpsum payment

First American

Pay in Installments

Recommended

Low Cost EMI at ₹ 8,813/month

for 60 months

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Benefits of learning with us

  • Award-winning faculty
  • 12+ Industry Projects
  • Dedicated career support
  • 9+ Languages & Tools
  • PG Certificate from Great Lakes Executive Learning & UT Austin

“Great Learning had been the ideal choice when I was in a fix to change track in my IT career.”

Nishitha R

Senior Business Analyst, Tredence

Take Your Data Analytics Skills to the Next Level with Add-On Courses

PREMIUM CERTIFICATION

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All the features of PG Program +

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    Live Virtual Classes with Microsoft Certified Instructors

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    Hands-on Learning and Academic Support

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    Exam Preparation Guide

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    Mock Exams

₹4,20,000 + GST View Fee Breakup

Inclusive of the total program fee + Microsoft Power BI fee

Application Process

1

Fill the application form

Apply by filling a simple online application form.

2

Interview Process

Go through a screening call with the Admission Director’s office.

3

Join program

An offer letter will be rolled out to the select few candidates. Secure your seat by paying the admission fee.

Apply Now

Deadline: 12th Mar 2024

Batch Start Dates

Bangalore Center

Bangalore

To be announced

Chennai Center

Chennai

To be announced

Still have queries?
Contact Us

Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at aiml@greatlearning.in or
080-6945-2473

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Check out the program and fee details in our brochure

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Application Closes 12th Mar 2024

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