During this course you will master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modelling to develop algorithms and prepare you for the role of a Machine Learning Engineer. You will master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling, gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end to end projects and 25+ hands-on exercises. You will also acquire thorough knowledge of the statistical and heuristic aspects of machine learning, implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python. You will also validate machine learning models and decode various accuracy metrics and comprehend theoretical concepts and how they relate to the practical aspects of machine learning.
If you’re a corporate enterprise please fill in the form below to send us your enquiry about this course and we will respond within 48 hours.