• GO
• WEKA
• SCIPY

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:

• Introduction and basic concepts in Machine learning (10%)
• Introduction to Theories used in Machine Learning (10%)
• Supervised learning vs. Unsupervised learning (10%)
• Model selection in Machine learning (10%)
• Role of Weka in Machine Learning (10%)
• Decision Tree and Rule mining using Weka (10%)
• A Brief review on SciPy(10%)
• Random Forest and Markov Decision Process algorithm (10%)
• Google’s Go Programming with k-nearest neighbor’s algorithm (10%)
• C 5.0 based decision tree algorithm (10%)

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 
Evaluation..

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

• Definition of machine learning systems
• Goals and applications of machine learning
• Classification of machine learning algorithms
• Supervised learning
• Unsupervised learning
• Reinforcement learning

Unit 2: Introduction to Theories used in Machine Learning

• Introduction to probability theory
• Discrete random variables and Fundamental rules
• Independence and conditional independence
• What is Information theory?
• How Decision Theory is helpful in machine learning
• Learning Theory of the machine learnings

Unit 3:  Supervised learning vs. Unsupervised learning

• Supervised learning setup
• Logistic regression
• Gaussian discriminant analysis and Naive Bayes
• Support vector machines
• Clustering and K-means
• PCA (Principal components analysis)
• ICA (Independent components analysis)
• Evaluating and debugging learning algorithms

Unit 4: Model selection in Machine learning

• Bayesian model selection
• Model selection for probabilistic models
• Model selection for non-probabilistic methods
• Probabilistic Generative Models
• Probabilistic Discriminative Models

Unit 5: Role of Weka in Machine Learning

• Introduction to weka
• How to install Weka
• The Knowledge Flow interface
• The Command Line interface
• Classification Rules and association Rules
• Attribute Selection and Fast attribute selection using ranking

Unit 6: Decision Tree and Rule mining using Weka

• ID3 based decision tree algorithm
• Entropy and Information gain
• ID3 implementation using weka
• Association rule mining using Frequent Pattern (FP) Growth algorithm
• FP-Tree structure
• FP-Growth Algorithm
• Implementation of FP-Growth using weka

Unit 7: A Brief review on SciPy

• SciPy - A introduction
• Scipy installation in ubuntu
• Python Scientific Computing Environment
• The SciPy Library/Package
• Data structures and function of Scipy
• Numpy

Unit 8: Random Forest and Markov Decision Process algorithm 

• Decision tree learning
• Tree bagging
• Random forests generation
• Relationship to nearest neighbors
• Markov model and Hidden Markov Model (HMM)
• Inference in HMM
• Learning and generalizations of HMM

Unit 9: Google’s Go Programming with k-nearest neighbor’s algorithm

• Introduction to Go Programming
• Mark Bates on Go Core Techniques and Tools
• Mark Bates on Go Database Web Frameworks and Techniques
• k-nearest neighbors algorithm
• Parameter selection
• Metric learning and Feature extraction
• Dimension reduction and Decision boundary
• Selection of class-outliers

Unit 10: C 5.0 based decision tree algorithm

• Introduction to C4.5 and C 5 Decision Tree
• Divide and Conquer Technique
• Feature Selection
• Regression Trees
• Selecting and Candidate testing
• Estimating True Error rate
• Pruning Decision Trees

• Understanding of Machine Learning techniques
• Explore on Machine Learning Software’s includes go programming, Scipy and Weka
• Decision Tree will be focused and demonstrated.
• Demonstration of Machine Learning with Weka
• Learn the Machine Learning Packages in Scipy.
• Role of Go Programming

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.

• Data Analyst - Statistics and Mining
• Data Analyst - Text Analytics
• Operations Research Analyst
• Senior Data Analyst - Statistics and Mining

32 Hours (4 Days)