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Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART I

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AI Principles with Edge Computing

Course 3 of 4 in the Intelligent Digital Factories Specialization

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With the paradigm shift of Digital Transformation in industries, there exists a huge volume of digital data in cloud storage about the Men, Materials and Machines of the organization. This data incurs a lot of information which could be used for process planning, predictive failures and business optimization. This course aims to equip the learners with various strategic principles of Artificial Intelligence theory which helps to extract such information from the pool of available data. The reach of AI in every area is consistently growing along with the features of programming. The course introduces appropriate programming skills blended in the modules and the learners will be able to learn by doing lots of practice problems. The long-term vision of AI, with Edge operations are explained in the course along with the principles required in implementing Edge AI. The learner can distinguish and will be able to segment the cloud and edge-based operations appropriately for the real-world problems. The different exercise problems with relevant software and hardware architecture support the learning of Edge AI with suitable metrics. On the whole the learners will get an exciting journey of understanding and applying AI algorithms, processing the algorithms for edge and implementing sample edge AI solutions. Edge AI products available in the market are introduced to the learners and this provides the learners with an ability to map their AI skills with suitable upcoming career options.

From the lesson

Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos

Principles of Unsupervised Machine Learning Algorithm - PART I7:09

Principles of Unsupervised Machine Learning Algorithm - PART II8:09

Clustering Algorithm - Principles with Hands-on approach using K-Means - Part I12:16

Clustering Algorithm - Principles with Hands-on approach using K-Means - Part II11:01

DBSCAN clustering algorithm - A hands on approach - PART I7:23

DBSCAN clustering algorithm - A hands on approach - PART II12:48

Dimensionality Reduction Algorithm 鈥� Principle & Implementation of PCA - PART I12:18

Dimensionality Reduction Algorithm 鈥� Principle & Implementation of PCA - PART II14:17

Linear Discriminant Analysis - A Quantitative Approach9:23

Autonomous vehicle embedded with Dimensionality Reduction Algorithm - PART I7:00

Autonomous vehicle embedded with Dimensionality Reduction Algorithm - PART II6:01

Reinforcement Machine Learning Algorithm 鈥� with a Practice Approach in HVAC System - PART I8:48

Reinforcement Machine Learning Algorithm 鈥� with a Practice Approach in HVAC System - PART II6:50

Reinforcement Machine Learning Algorithm 鈥� with a Practice Approach in HVAC System - PART III7:21

Model Based RL Algorithms 鈥� Principle and Example with DYNA Q Algorithm - PART I6:44

Model Based RL Algorithms 鈥� Principle and Example with DYNA Q Algorithm - PART II5:01

Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART I6:59

Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART II12:19

Model Free Reinforcement learning 鈥� exploring policy based methods - PART I8:28

Model Free Reinforcement learning 鈥� exploring policy based methods - PART II10:17

Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART I6:02

Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART II6:19

Taught By

Subject Matter Expert
L&T Edutech

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