Machine learning is set to go mainstream as businesses are expected to roll out machine learningbased tolls for business analytics within the next two years. The majority of those companies said the most promising opportunity for machine learning lays in real-time data analysis.
Machine learning is moving past the “hype cycle”, with enterprises looking to automate analytics processes in areas like business intelligence and cyber security. Come year 2020, nearly 8 billion jobs will be provided for the machine learning experts. It will generate a revenue of about 3 billion in the field of machine learning.
The objective of this course is to familiarize the audience with some basic learning algorithms and techniques and their applications. Leading open-source technologies such as GO, WEKA and Sci Py which are used in Big IT companies such as Google, Oracle, Microsoft etc. Techniques on how to implement machine learning will be explained.
The objective of the certification examination is to evaluate the knowledge and skills acquired by the participants during the course. The weightage in key topics of the course as follows:
As part of the written examination, each participant will be assessed individually on the last day
of the training for their understanding of the subject matter and ability to evaluate, choose and
apply them in specific context and also the ability to identify and manage risks. The assessment
focuses on higher levels of learning in Bloom’s taxonomy: Application, Analysis, Synthesis and
This written examination will primarily consist of 40 multiple choice questions spanning various
aspects as covered in the program. It is an individual, competency-based assessment.
The main objective of this course is to train the professional with open source tools such as
Google GO,Scipy and WEKA. The professionals will expertise after the end of the course and they will
be familiar with the tools. It also elaborates the business perspective of machine learning and help
the professional to grow.
Unit 1: Introduction and basic concepts in Machine learning
Unit 2: Introduction to Theories used in Machine Learning
Unit 3: Supervised learning vs. Unsupervised learning
Unit 4: Model selection in Machine learning
Unit 5: Role of Weka in Machine Learning
Unit 6: Decision Tree and Rule mining using Weka
Unit 7: A Brief review on SciPy
Unit 8: Random Forest and Markov Decision Process algorithm
Unit 9: Google’s Go Programming with k-nearest neighbor’s algorithm
Unit 10: C 5.0 based decision tree algorithm
This is a 4-day intensive training program with the following assessment components.
Component 1: Written Examination (MCQ)
Component 2. Project Work Component (PWC)
These components are individual based. Participants will need to obtain 70% in both the components in order to qualify for this certification. If the participant fails one of the components, they will not pass the course and have to re-take that particular failed component. If they fail both components, they will have to re-take the assessment.
Participants are recommended to have preferably min. 2 years of experience in software development, business domain or data/business analysis.
32 Hours (4 Days)