Certified Machine Learning Specialist (CMLS)

Certified Machine Learning Specialist

(CMLS)

Course Outline

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Course Outline

Machine Learning is the science of getting computing systems to act with minimal human

Intervention. Such systems change behaviors without being explicitly programmed. It is

nothing but automation of Feature Engineering

With Machine Learning factors such as increasing volume and variety of available data,

cheaper and powerful computational processing and affordable data storage will result in

high-value predictions that can guide better decisions.

Gartner predicts that by 2020, Artificial Intelligence will be one among the top five investment

priority for more than 30 percent of CIOs. Machine Learning is going to be a critical driver for

demand, fraud and failure predictions by 2019.

This specialist course on Machine Learning gives an overview of many concepts, techniques,

and algorithms. The course covers basic topics such as classification and linear regression to

more advanced topics such as boosting, support vector machines, hidden Markov models, and

Bayesian networks in Machine Learning.

This course will introduce professionals to open source Machine Learning tools such as WEKA

and Scikit Learn. Professionals attending the course will learn the different algorithms in some

of most widely adopted Machine Learning methods such as Supervised Learning,

Unsupervised learning and Reinforcement Learning.

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 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

• Learn the fundamentals of AI and machine learning and how it could impact your workthrough several real-life use case

• Understand Machine Learning techniques/method: Supervised, Unsupervised &Reinforcement Learning through hands-on examples

• Learn key ML concepts like Principle Component Analysis (PCA), Hyperparameter tuning,Clustering, Classification ,Regression ,Neural Network etc.

• Get skilled in popular machine learning algorithms using Python Programming( SckitLearn, TensorFlow ) ,Weka, RapidMiner

•


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 – 4.45)

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)

Hands-On (WEKA)

Day 3 A Brief review on

SciPy

Random Forest and Markov

Decision Process Algorithm

Hands-On

(SciKit

learn)

Hands-On

(Scikit

Learn)

Day 4

Google’s Go Programming with k-nearest

Neighbor’s Algorithm

Hands-On (Spark MLLIB)

Review and Discussion

Written Examination


COURSE OUTLINE

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 neighbor’s 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


Hands On

The main objective of this course is to train the professional with open source tools such as

Google GO, Scikit Learn and WEKA, Spark MLLIB.

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 and Spark MLLIB (10%)

• C 5.0 based decision tree algorithm (10%)

Tools Used:

• WEKA

• SCIPY

• Spark MLLIB

• GO


Certified Machine Learning Specialist (CMLS)

Education

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