Comprehend Theory and Mathematics behind AI and ML Models
Learn the theory and mathematics behind the most compelling science of Artificial Intelligence and Machine Learning!
What this course covers:
- Introduction to Artificial Intelligence and Machine Learning
- Python essentials with Python libraries used in Machine Learning
- Problem solving in Artificial Intelligence
- Knowledge Representation and Reasoning in context to Artificial Intelligence
- Types of Learning in AI and ML
- Mathematical and Statistical Preliminaries for Machine Learning
- Artificial Neural Networks
- Competitive Learning and Self Organizing Maps
- Fuzzy Neural Networks
- Linear and Logistic Regression
- Support Vector Machines (SVMs)
- Clustering algorithms
- Principle Component Analysis
- Brief Introduction about Deep Learning and Model Optimization Techniques
The field of Artificial Intelligence and Machine Learning requires thorough understanding of the concepts involved when you build, train, test and deploy your model, so that you can fine tune the model as per your needs. But that requires understanding of the core concepts and theory behind those models, and this course covers exactly that!
This course covers the Theory and Mathematics behind Artificial Intelligence and Machine Learning and touches upon various concepts such as Learning, Machine Learning algorithms and Artificial Neural Networks, thereby detailing each topic with elaborate theory involved.
What you will be able to achieve with this course:
1. Understand the concepts behind AI and ML
2. Make your own Machine Learning models and excel at work
So, go ahead and enroll for this course and demonstrate your knowledge to your peers at work or at college!