Artificial Intelligence & Data Science
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Post Graduate Certificate in Advanced Machine Learning & AI

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Course Features

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Duration

6 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

Course Description

Artificial Intelligence (AI) and related technologies are revolutionizing the lives of people around the world. Due to widespread adoption of AI (and Deep Learning innovations, (DL), there is a growing demand for AI talent across all industries. Chatbots, Siri and Alexa, as well as humanoids, are all examples of this. This advanced certificate will give your career as an AI/DL professional a jump start.

This six-month intensive program will allow you to increase your knowledge about deep learning techniques and their application in AI.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

How to write code using common Tensorflow functionality

How to write code using common Keras functionality

How to work with data and perform data exploration tasks using Tensorflow

How to create interactive visualizations using TensorBoard

How to pre-process and prepare data and perform in-depth exploration using Tensorflow and Keras

How to train ML models and make predictions using Tensorflow and Keras

How to evaluate performance of ML models and interpret the results

Become familiar with the Kaggle platform and understand the lifecycle of a machine learning project

How to develop Artificial Neural Networks algorithms and concepts

How to train ANN models and make predictions using Tensorflow and Keras

How to use the backpropagation algorithm and other modern variants of it

How to fine-tune Neural Network hyperparameters

How to build, train, and apply fully connected deep neural networks

How to improve the performance of deep neural networks by hyperparameter tuning, regularization and optimization

How to split your data and when to use multi-task, transfer, and end-to-end deep learning

How to build, train, and apply convolution neural networks

Characteristics of various architectures of deep convolutional neural networks and applications in computer vision

How to improve the performance of convolutional neural networks

How to build, train, and apply recurrent neural networks

Architectures of recurrent neural networks and applications in Natural Language Processing (NLP)

How to apply feature engineering to image and text data

Utilize Transfer Learning

How to build fusion neural networks consisting of CNNs and RNNs

Course Instructors

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Dr. Partha Pratim Roy

Faculty, Department of Computer Science and Engineering

Instructor at Coursera
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Dr. Gaurav Dixit

Faculty, Center of AI & Data Science

Instructor at Coursera
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