Duration

4 days

COURSE OBJECTIVES

Machine learning is set to go mainstream as businesses are expected to roll out machine learning based 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.

JOB ROLES IN NICF / TARGETED AUDIENCE

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

PRE-REQUISITES

Participants are preferred to have min. 2 years of experience in software development, business domain or data/business analysis.

PROGRAM STRUCTURE

This is a 4-day intensive training program with the following assessment components.

Component 1. Written Examination

Component 2. Project Work Component

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.

COURSE OUTCOMES

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

COURSE STRUCTURE

Day

Session 1

(9:00 – 10:45)

Session 2

(11:00 – 12:30)

Session 3

(13:45 – 15:15)

Session 4

(15:15 – 17:00)

Day 1

Introduction and Basic concepts in Machine learning

Introduction to Theories used in Machine Learning

Supervised learning vs. Unsupervised learning

Model Selection in Machine learning

Day 2

Role of Weka in Machine Learning

Decision Tree and Rule Mining using Weka

Hands-On (WEKA)

Day 3

A Brief review on SciPy

Random Forest and Markov Decision Process Algorithm

Hands-On (Sci Py)

Day 4

Google’s Go Programming with k-nearest Neighbor’s Algorithm

C 5.0 based Decision Tree Algorithm

Review and Discussion

Written Examination

COURSE OUTLINE

Definition of machine learning systems
Goals and applications of machine learning
Classification of machine learning algorithms
Supervised learning
Unsupervised learning
Reinforcement 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
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
Bayesian model selection
Model selection for probabilistic models
Model selection for non-probabilistic methods
Probabilistic Generative Models
Probabilistic Discriminative Models
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
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
SciPy - A introduction
Scipy installation in ubuntu
Python Scientific Computing Environment
The SciPy Library/Package
Data structures and function of Scipy
Numpy
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
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
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

Hands On

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.

WRITTEN ASSESSMENT

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.

EXAM PREPARATION

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%)

Tools Used:

GO
WEKA
SCIPY

Programme scope, delivery arrangements and suitability are confirmed during enquiry.

Certified Machine Learning Specialist (CMLS)