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PG Program in Artificial Intelligence And Machine Learning

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Cloud computing courses. Distance certification programme Offered by: Great Lakes

Duration-  1 Year | TechnologyPost Graduation
Affiliation: NA 
Course Fees:  Rs 3,25,000/-

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The Post Graduate Program in Artificial Intelligence and Machine Learning is designed to develop competence in AI and ML for future-oriented working professionals. PGP-AIML is a 12-month blended program with weekend classroom sessions and online learning. Classes will be held one weekend a month at the Great Learning centre and supplemented by online learning content. The program constitutes of nearly 400 learning hours with 250 classroom learning hours. Classroom sessions are held one weekend a month, alternating as 3 days (Friday, Saturday, and Sunday) and 2 days (Saturday and Sunday) each month.

DELIVERY MODE OF THE PROGRAM
Weekend Classroom + Online Learning
Learn by doing (Hands-on Sessions using AI and ML lab)
Learning from both academic faculty and industry practitioner experts
Peer Learning
LEARNING OUTCOMES
Develop expertise in popular AI & ML technologies and problem-solving methodologies
Develop ability to independently solve business problems using AI & ML
Learn to use popular AI & ML technologies like Python, Tensorflow and Keras to develop applications
Develop a verified portfolio with 8 projects that will showcase the new skills acquired

Topics Covered

Python for AI (Significant Functions, Packages and Routines)
Statistics & Probability (Descriptive & Inferential Stats, Probability & Conditional Prob)
Visualization principles and techniques
MACHINE LEARNING: SUPERVISED LEARNING
Topics Covered

Regression (Linear, Multiple, Logistic)
Classification (k-NN, naïve Bayes) techniques
Decision Trees
MACHINE LEARNING: UNSUPERVISED LEARNING
Topics Covered

Clustering (k-means, hierarchical, high-dimensional)
Expectation Maximization
MACHINE LEARNING: ENSEMBLE TECHNIQUES
Topics Covered

Boosting and Bagging
Random Forests
MACHINE LEARNING: REINFORCEMENT LEARNING
Topics Covered

Value-based methods (e.g. Q-learning)
Policy-based methods
DEEP LEARNING
Topics Covered

Neural Network Basics
Deep Neural Networks
Recurrent Neural Networks (RNN)
Deep Learning applied to Images using CNN
Tensor Flow for Neural Networks & Deep Learning
COMPUTER VISION
Topics Covered

Convolutional Neural Networks
Keras library for deep learning in Python
Pre-processing Image Data
Object & face recognition using techniques above
NATURAL LANGUAGE PROCESSING
Topics Covered

Statistical NLP and text similarity
Syntax and Parsing techniques
Text Summarization Techniques
Semantics and Generation
INTELLIGENT AGENTS
Topics Covered

Uninformed and heuristic-based search techniques
Adversarial search and its uses
Planning and constraint satisfaction techniques

Applicants should have a minimum of 3 years of experience in a technology role, including some programming knowledge preferably in Python. For candidates who do not know Python, we offer a free pre-program tutorial.

Interested candidates need to apply by filling a simple online application form. Following which you will need to go through a screening process which happens over a call with the Admission Director’s office. Post screening, your profile along with the review from the screening panel will be shared with the Program Director for final selection.

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