Artificial Intelligence: Cloud and Edge Implementations (online) 

offered by Oxford University

Machine Learning principles

Deep Learning principles

Foundations of Edge computing

Full Stack development (in context of AI)

MLOps -Machine learning and DevOps

Cloud-native development

Cloud development process flows

Hands-on machine learning development including the main libraries like NumPy, Pandas, Matplotlib, SciKit-Learn

Hands-on deep learning development for the main algorithms

This track covers: Classification using Multi-layer perceptron (MLP) by establishing a baseline and improving that baseline using techniques like dropout; Regression -linear regression, logistic regression, multivariate regression, etc.; Convolutional Neural Networks; Natural Language Processing; Recurrent Neural Networks; Autoencoders and Unsupervised Learning (PCA and K-means)

uild and deploy modules using containers on edge devices

End to End deployment of machine learning and deep learning models using the Azure cloud

Autoencoders

Natural Language Processing (NLP)

Unsupervised Learning

Representation Learning

Generative Adversarial Networks (GANs)

Bayesian approaches to machine learning and deep learning

Reinforcement Learning

Probabilistic machine learning

Machine Learning and Deep Learning implementation in the Google Cloud, Azure and Amazon Web Services platforms

Azure Sphere for deploying Machine Learning and Deep Learning implementation models on embedded devices

Time series development

Industrial IoT

Embedded AI (Intel, ARM platforms)

Computer Vision

Predictive Maintenance with MATLAB & Simulink

Signal Processing for Deep Learning with MATLAB

Bioinformatics and Drug discovery

5G

Affective Computing - AI and Emotions

Robotics

MLOps -deployment of Machine Learning and Deep Learning models in containers -on edge devices

Machine learning track -end to end

Deep learning track -end to end

IoT / time series models

IoT anomaly detection

Career mentorship in brief pre-planned sessions with Ajit Jaokar

AI innovation in countries