Course - Machine Learning Basics with R

© 2014 Persontyle Ltd. All rights reserved.

[Hands-on Introduction of

Supervised Machine Learning Methods]

DURATION – 2 DAY

MACHINE LEARNING

BASICS WITH R

R logo is trademark of the R Foundation, from http://www.r-project.org

www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.


Data generated through our activities captures plethora of information aboutour identity, likes and dislikes etc. This information has tremendous value inevery aspect of human life. Programming computers to unravel this hiddeninformation is what Machine Learning is all about. It is the art and science ofscientifically deriving insights, patterns and predictions from data.

Though it has been an area of active research for over 50 years, MachineLearning is currently undergoing a renaissance driven by Moore's law and therise of big data. Large private and public investment in the area has given usself-driving cars, practical speech recognition, effective web search, and avastly improved understanding of the human genome. Computer basedMachine Learning algorithms now outperform humans on tasks such ashandwritten digit recognition, traffic sign recognition, and even on somecomplex reasoning tasks as demonstrated by IBM's Watson winning Jeopardy

<MACHINE LEARNING>

“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” - Tom Mitchell

Machine Learning models and programs automatically make decisions from datain order to achieve some goal or requirement. Machine learning models matter tothe world. Because they are;

#EFFICIENTMachine Learning models predict and detect partners faster than any othermanual program or method.

#EFFECTIVEMachine Learning models can do better job than humans when analysing andpredicting large scale and streaming data sets (big data).

#SCALEMachine Learning models can provide solutions to large data problems thattraditional systems can not solve.



Machine perception

Computer vision, including object

recognition

Natural language processing

Pattern recognition

Search engines

Medical diagnosis

Bioinformatics

Brain-machine interfaces

Detecting credit card fraud

Stock market analysis

Classifying DNA sequences

Sentiment analysis

Affective computing

Information retrieval

Recommender systems

MACHINE LEARNING CAN APPEAR IN MANY GUISES

Examples in the real world include handwritten recognition, weather

prediction, fraud detection, search, facial recognition, and so forth are all

examples of machine learning in the wild. Applications for MachineLearning include:

“Over the past two decades Machine Learning has become one of the

mainstays of information technology and with that, a rather central, albeitusually hidden, part of our life. With the ever increasing amounts of data

becoming available there is good reason to believe that smart data analysis

will become even more pervasive as a necessary ingredient fortechnological progress.”

DR. ALEXANDER J. SMOLA, PROFESSOR, CARNEGIE MELLON UNIVERSITY



This course is meant to be a fast-paced, hands-on introduction to MachineLearning using R. The course will be focusing mainly on basics of MachineLearning methods and practical implementation of these methods to solvereal-world problems. This course aims to develop basic understanding ofsupervised learning methods, through the use of the R programmingplatform. It describes the different types of learning and the two maincategories of their applications: Classification and Regression. With a focuson the former, it takes a close look at typical Machine Learning techniquesand how they apply on datasets akin to those encountered in the real world.

Our goal is to give you the basic skills that you need to understandsupervised Machine Learning algorithms and models, and interpret theiroutput, which is important for solving a range of data science problems.

Without getting too much into the mathematics of Machine Learning, thiscourse dwells on the ideas and principles of it, along with the two mainmethods that all Machine Learning practitioners use.

With theory and hands-on sessions intertwined, the course illustrates theusefulness of Machine Learning and how you can learn it effectively withoutlosing sight of its elegance and value. Apart from the Classificationtechniques, you will learn about how to validate any classifier'sperformance, when to use what, and how the new innovations come about.

MACHINE LEARNING

BASICS WITH R

“The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it -that’s going to be a hugely important skill in the next decades.”

Hal Varian

WHAT WILL YOU LEARN?

In this course you will learn, among other things:

+ What Machine Learning entails and why it is important

+ The different types of Learning, especially Supervised Learning

+ How Classification and Regression fit in Machine Learning

+ Understand types of classifiers

+ Applied details of following algorithms:

+ Decision Tree and basic overview of Random Forests

+ k Nearest Neighbour (kNN) and a few of its variants

+ Bayes Classifier

+ How to use these algorithms in a variety of benchmark datasets

+ How to fine-tune these algorithms for better performance

+ Validation metrics for a classifier's performance (ROC curve, Accuracy Rate, F1-metric)

+ The various libraries in R for these types of classifiers

+ Which algorithm to choose based on the data you have

+ Data transformation



MACHINE LEARNING

BASICS WITH R

PREREQUISITES

Knowledge of R programming language and familiarity with linear algebra. Basicfamiliarity with statistics and probability theory is recommended.

* If you don't know R then first attend the Getting Started with R and DataAnalysis workshop.

http://www.eventbrite.co.uk/e/getting-started-with-r-and-data-analysis-london-b104-registration-11215823831

SCHEDULE



MACHINE LEARNING

BASICS WITH R

Day 1

Time Topic/Activity

09:00-10:00 What is Machine Learning (ML) and why is it important? Examples of applications

10:00-11:00 Different types of learning and where they are used

11:00-11:15 Coffee Break

11:15-12:00 Overview of Classification and Regression

12:00-13:00 Using R for Machine Learning

13:00-13:30 Lunch Break

13:30-14:15 LAB: Practical examples (using R): Boston Housing Prices (Regression)

14:15-14:45 Classification Learning


15:00-16:00 Decision Tree Learning (ID3, C4.5, and C5.0)

16:00-16:45 LAB: Decision Tree example using R: Japanese Credit Screening (Classification)


17:00-17:30 Boosting method for decision trees

17:30-18:30 LAB: Using the C5.0 Decision Tree (Credit Card dataset)

Day 2

Time Topic/Activity

09:30-10:00 Instance-based learning - the intuitive approach to ML

10:00-11:00 kNN algorithm. kNN bias.

11:00-11:30 Overview of some kNN variants


11:45-12:15 Curse of Dimensionality

12:15-13:00 Guest Speaker

13:00-13:30 Lunch Break

13:30-14:00 Data transformation and normalization

14:00-14:45 LAB: Using kNN for Credit Card dataset14:45-15:15 Bayes rule and Bayesian Learning. Dealing with noisy data


15:30-16:00 Bayesian Learning in action / Bayesian Classification

16:00-16:45 LAB: Using Bayes classifier for Credit Card dataset16:45-17:00 Pros and Cons of methods covered


17:15-17:45 Validating results - ROC curve, Accuracy Rate, F1 metric, Confusion Table,

Precision, Recall17:45-18:30 LAB: creating confusion table and basic validation metrics for all previous drills

(adult census data)



Persontyle trainers are passionate about meeting each participants learningneeds. They have been chosen both for their extensive practical DataScience and Machine Learning experience and for their ability to educateand interact with natural empathy. All of our trainers have worked on avariety of data science and Machine Learning projects. They share theiracademic knowledge and real-world experience and each individual addstheir own unique perspective to the course. Our trainers present in a stylethat is informal, entertaining and highly interactive.

Guest Speakers

Business leaders, Machine Learning practitioners, and academicresearchers covering use cases, case studies and sharing practicalexperience of applying Data Science and Machine Learning in theirorganizations.

COURSE INSTRUCTORS

“A breakthrough in Machine Learning would be worth ten Microsofts”

BILL GATES, CHAIRMAN, MICROSOFT

WHO SHOULD ATTEND

Anyone interested in learning and applying supervised machine learningmethods and R to solve real-world data problems. Ideal for peopleinterested in pursuing career in data science.

This hands-on workshop is aimed at business and technology professionals,Developer, Architect, Manager, Data Analyst, BI Developer/Architect, QA,Performance Engineers, Sales, Pre Sales and Marketing, Project Manager,Public Services, Teaching Staff and all those who already have some basiccompetence in statistics but wish to begin using R for machine learning forthe first time.

MACHINE LEARNING

BASICS WITH R

For corporate bookings or to organize on-site training email [email protected] or call now +44 (0)20 3239 3141

Register Now

RETURN ON INVESTMENT (ROI) CONVINCE YOUR BOSS

The advent of the data driven connected era means that analyzing massive scale, messy, noisy, and unstructured data is going to increasingly form part of everyone's work.

The School of Data Science learning programs provide a unique investment opportunity that pay’s for itself many times over.

"For the best return on your money, pour your purse into your head."

World-class Instructors

Benjamin Franklin

Develop Practical Data Science Skills

Real World Industry Use Cases

Short Courses For Time Convenience

Value For Money

THE SCHOOL OF DATA SCIENCE The School of Data Science, a project of Persontyle, specializes in designing and delivering

structured, relevant and practical learning experiences for all of us to understand data science in

simple human terms.

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MACHINE LEARNING

BASICS WITH R

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Course - Machine Learning Basics with R

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