Lot of Jobs will become obsolete slowly with the advent of Machine Learning. It is important to re skill to stay relevant in the software Industry.This course helps you understand machine learning in a simple and easy way.This course will take you from zero to machine learning hero in 45 days. You don’t need to know any programming language to enroll in this course.Python has become the de facto programming language of data scientists and data analysts. It’s concise, easy to learn and data friendly, making it ideal for data analysis. We will start with a crash course on Python before getting into machine learning using Python. We will also look at Python libraries like NumPy, Pandas and SciKit-Learn that are needed to perform machine learning in Python.Each lecture has detailed and live explanations from the instructor and assignments to test your level of understanding. Once you finish this course you would have taken a giant leap towards the future of data analysis.
Data is being generated at an ever increasing rate. It is originating not only from the conventional sources such as business interactions, scientific research and social media, but also from newer agents like millions of devices that are collectively known as the “Internet of Things” or IOT. This massive quantity of data is impacting the way in which we approach the task of “problem solving”. Instead of creating very specific algorithms targeting specific problems, it is being asked: “Can we have systems that intelligently learn from the available data – and also adapt themselves to changing situations?” While Big Data addresses the issue of handling very large data, the field of Data Analytics and Machine Learning is more concerned with “extracting hidden intelligence” from the available data. Thus Data Analytics is the “brain” while Big Data is the muscle power!
This course is meant for participants who would like to get introduced to Machine Learning and the various applications. The course duration is over 21 hours.The course takes a practical and "hands-on"approach to teaching Machine Learning. Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours.The course covers Statistics, which is so very essential for effective use and also for interpreting results of applying Machine Learning models. It covers the breadth of Machine Learning techniques typically classified into Supervised and Unsupervised learning, and also into Regression / Classification / Clustering. It also introduces participants to the "R" programming language.
This course caters to those who have basic knowledge of machine learning and who would like to get introduced to and master some of the advanced techniques. The course duration is three days.The course takes a practical and "hands-on"approach to teaching Machine Learning. Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours. Use of Python programming language to build machine learning models.Advanced machine learning topics will be covered including: feature selection and reduction, cross validation, advanced regression techniques, decision trees and SVM.
This course covers Artificial Neural Networks and Deep Learning techniques. These techniques are fast becoming popular and they are finding application in many fields. The course takes a practical and "hands-on"approach to teaching Machine Learning. Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours. Use of the popular deep learning tool - Tensorflow.Concepts of Artificial Neural Networks and Deep Learning Networks. Participants will understand these topics through practical tools like Tensorflow.
Big Data Strategy Definition
Architecting Data Platform
Building End To End Analytics Application
Front End Re-Engineering
Back End Re-Engineering