NICF- Pattern Recognition and Machine Learning Systems (SF)
Machine learning uses statistical techniques to give computers the ability to "learn" with data without being explicitly programmed. With the most recent breakthrough in the area of deep learning, machine learning has made a big leap. Deep learning has enabled many practical applications in the areas of computer vision, natural language processing, etc.
There are many machine learning techniques available to develop intelligent systems and solve real-world complex problems. In this new era of AI and machine learning, it is important for IT professionals especially AI engineers to acquire the cutting-edge knowledge and skills in this area.
This course will be useful for IT and AI professionals to acquire advanced pattern recognition and machine learning techniques, especially deep learning techniques. Participants will learn how to select and apply the most suitable machine learning techniques to solve the given problems and develop intelligent systems. This course covers general principles and techniques with a unique focus on practical applications.
At the end of the course, participants will be able to:
- Assess and compare the suitability of advanced pattern recognition and machine learning techniques across a range of problem domains
- Apply deep learning and other advanced machine learning techniques to solve given problems
- Build intelligent systems using deep learning and other advanced pattern recognition techniques
- Analyse the results and suggest possible improvements
What Will Be Covered
Intro to Pattern Recognition and Machine Learning Systems
Neural Network Basics
Neural Network Modeling and Design
Deep Learning Systems
Convolutional Neural Networks
Recurrent Neural Networks
Hybrid and Ensemble Approaches
Practical case studies and workshops
Who should attend
This course is for:
- IT professionals who need to apply pattern recognition and machine learning techniques to develop intelligent systems.
- IT professionals who need to assess and compare pattern recognition and machine learning techniques.
- Domain specialists and anyone planning to undertake pattern recognition and machine learning projects.