In this introductory course, you will learn the concepts of artificial intelligence, machine learning, and deep learning, and take a deeper dive into the scope and need of AI in business today. Gain an understanding of the impact of AI in enabling human progress and transforming business and learn about various application areas across the business.

This course is intended for data engineers, data scientists, data architects, or anyone else who wants to learn Artificial Intelligence and Machine Learning

This course provides a background on the need and significance of AI and ML frameworks. It explains in detail about various existing frameworks, their features and limitations, and dives into building NN models in PyTorch, Tensorflow and Caffe2.

The latter part of course compares the pros and cons of various AI/ML frameworks, and the significance of Automated ML frameworks and major Auto ML frameworks. Workflow and high-level programming of PyTorch, TensorFlow and Caffe2 frameworks is discussed.

A separate on-demand lab aligns with this course to reinforce these skills through exercises.

This course describes artificial intelligence and machine learning workflow and its significance, and the need of planning and designing AI infrastructure. Learn about the need of AI infrastructure and understand various infrastructure components and their role in building AI across an organization.

The lab looks at basic docker operations and provides information about how to perform operations on a TensorFlow serving image.

This course provides detail around the scope and need of AI. Learn about the impact of AI in enabling human progress and transforming business. The course also covers various application areas across the business and outlines the importance of building an AI team in order to understand different roles.

The lab provides information about Python programming and explains syntax and libraries used.

This course describes the concept of various machine learning methods and algorithms associated with each method. Learn in-depth machine learning techniques and understand different algorithms. Other techniques covered are classification, regression, clustering, and dimensionality reduction.

The lab demonstrates exploratory data analysis, supervised learning using Python and unsupervised learning using Python to evaluate and predict different dataset models. 

This 24-hour course includes all modules, and covers a broad range of emerging AI techniques and supporting technologies such as: ML, neural networks, deep reinforcement learning, and AI Infrastructure. Learn about the technical and operational aspect of AI and ML and understand the concepts of AI, ML, neural network, reinforcement learning, NLP and artificial ecosystem.

Learn about the need of AI ready infrastructure, AI and ML frameworks, and implemented machine learning models, as well as use cases across the industry. The offering is an engaging mix of key technologies, hands-on labs, case examples, and business insights.

This course describes the concept of building AI ecosystem. The course provides a detailed description of how AI is implemented in a business model and helps to understand the impact of AI in business. The course also provides an overview of ethics in AI, various ethical issues principles, different types of biases and their impacts and what culture should be developed to reduce bias and increase the trust of humans over machines.

This course describes the concepts of deep reinforcement learning, reinforcement learning, neural networks and natural language processing, as well as bellman equations, Mont Carlo, and Q learning algorithms. Learn about the process of training a neural network and the approaches of NLP and their application areas.

The lab helps users build neural networks such as LSTM and CNN and demonstrates the Markov process, Montecarlo and Q learning techniques. NLP and how to build a conversational bot are other topics covered.

The field of Machine Learning and AI is so far reaching that it has had an impact on pretty much every field of study and industry. Some of the use cases of Machine Learning also have profound impact on the way we consume data and share information in the modern world.

This course gives an introduction to application of Machine Learning models in multiple industries and modes of data.