Course Overview:

Machine learning is the science of getting computers to act without being explicitly programmed. It uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars and many more.The objective of this training program is to understand how machines can learn from its past experience which is considered as the core of AI. We focus on creating strong fundamental knowledge sets on building algorithms by mastering the principles of statistical analysis.

Learning Outcome:

Upon completion of the course, you will know how to use statistical computing techniques, data classification methods for Machine Learning applications in various fields. Key topics covered include: 

• Introduction to Machine Learning
• Introduction to R programming
• Statistics Essentials
• Knowledge extraction using Statistics Essentials
• Pre program preparation
• Classification algorithm, Decision Tree
• Linear classifier: Naïve Bayes Classifier, Case study
• Logistic regression, case study
• Regression Analysis, case study
• Survival Analysis, Case study
• Introduction Clustering, Euclidean Distance
• Hierarchical clustering, case study
• k-means clustering, Case Study

Ideal for:

●     Computer science students / professionals

●     Automation experts

●     Industrial domain experts interested in adopting Machine Learning

●     Anybody interested in mastering Machine Learning