PG Program in Artificial Intelligence & Machine Learning - duplicates
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PG Program in Artificial Intelligence & Machine Learning - duplicates at Great Learning Overview
Duration | 12 months |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
PG Program in Artificial Intelligence & Machine Learning - duplicates at Great Learning Highlights
- India's Top 10 Ranked Institute, Hands-on program using AI and ML lab
- 200+ job opportunities posted in last 6 months
- Average salary hike of 48% among transitions
- Dual certification from Stuart School of Business
PG Program in Artificial Intelligence & Machine Learning - duplicates at Great Learning Course details
- This post-graduate program is designed for those who want to enter or advance their careers in this exciting and well-paying field. Applicants should have programming knowledge and a minimum of 3 years of work experience.
- Develop expertise in popular AI & ML technologies
- Develop ability to independently solve business problems
- Learn to use popular AI & ML technologies like Python, Tensorflow and Keras
- Build expertise in AI & ML which are quickly becoming the most sought-after skills around the world
- Develop a verified portfolio with 8 projects to showcase the new skills acquired
- It is a 12-month program offered in the blended format with weekend classroom sessions and online learning. Covers Artificial Intelligence & Machine Learning technologies and applications including Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Intelligent Virtual Agents, Neural Network, Tensor Flow and many more.
- AIML is among the most sought after, highest paid and future secure digital economy skill. There is a 60% RISE IN DEMAND for Artificial Intelligence and Machine Learning experts in 2018.
PG Program in Artificial Intelligence & Machine Learning - duplicates at Great Learning Curriculum
Module 1: Foundations of AI
Python for AI (Signi?cant Functions, Packages and Routines)
Statistics & Probability (Descriptive & Inferential Stats, Probability & Conditional Prob)
Visualization principles and techniques
Module 2: Machine Learning:Supervised Learning
Regression (Linear, Multiple, Logistic)
Classi?cation (k-NN, naive Bayes) techniques
Decision Trees
Module 3: Machine Learning:Unsupervised Learning
Clustering (k-means, hierarchical, high-dimensional)
Expectation Maximization
Module 4: Machine Learning: Ensemble Techniques
Boosting and Bagging
Random Forests
Module 5: Machine learning : Reinforcement Learning
Value-based methods (e.g. Q-learning)
Policy-based methods
Module 6: Deep Learning
Statistical NLP and text similarity
Syntax and Parsing techniques
Text Summarization Techniques
Semantics and Generation
Module7: Deep Learning VUsing TensorFlow
Neural Network Basics
Deep Neural Networks
Tensor flow for Neural Networks & Deep Learning
Module 8: Computer Vision
Convolutional Neural Networks
Keras library for deep learning in Python
Pre-processing Image Data
Object & face recognition using techniques above
Module 9: Intelligent Agents
Uninformed and heuristic-based search techniques
Adversarial search and its uses
Planning and constraint satisfaction techniques
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