Course Overview
This course covers broad range of emerging AI techniques and supporting technologies such as: ML, neural networks, deep reinforcement learning, and AI Infrastructure.
The course provides a detailed description about the technical and operational aspect of AI and ML and helps the learners to understand the concepts of AI, ML, neural network, reinforcement learning, NLP and artificial ecosystem. The course also provides a detailed description about the need of AI ready infrastructure, AI and ML frameworks, and also know about the implemented machine learning models, and use cases across the industry.
The offering is an engaging mix of key technologies, hands-on labs, case examples, and business insights.
Who should attend
This course is intended for data engineers, data scientists, data architects, or anyone else who wants to learn artificial intelligence and machine learning.
Prerequisites
Recommended: Data Science and Big Data Analytics (MR-1CP-DSBDA)
Course Objectives
Upon successful completion of this course, participants should be able to:
- Describe the impact and scope of artificial intelligence
- Describe the concepts of machine learning, deep learning, and neural network
- Know about Python and explain the importance of Python in AI and ML
- Explain roles and responsibilities in an organization
- Describe data preparation and feature engineering
- Explain the concept and techniques of supervised and unsupervised learning
- Understand the concepts of deep reinforcement learning, reinforcement learning, neural network, NLP
- Explore the stages of AI and ML workflow
- Describe the need of AI Infrastructure and list the considerations of AI Infrastructure
- Know about AI and ML frameworks and understand their applications
- Know about some industry applications of AI to solve some advanced problems
- Understand the role of AI in business strategy
- Explain strategies and steps to manage AI applications
- Understand ethical issues principles with respect to AI and describe different types of biases
Course Content
- Understanding the concepts of AI and ML
- AI and ML through python
- Roles and responsibilities
- Introduction to machine learning strategies, algorithms, and techniques
- Data preparation and feature engineering
- Supervised Learning
- Unsupervised Learning
- Overview deep reinforcement learning
- Reinforcement learning
- Neural Networks
- Natural Language Processing
- Introduction to AI and ML workflow
- Planning and designing of AI infrastructure
- Modernized AI infrastructure
- Introduction to AI and ML frameworks
- Explore AI and ML frameworks
- ML models and use cases
- Managing AI Applications
- Ethics and avoiding bias