Course - Machine Learning Basics with R
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Transcript of 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.
R logo is trademark of the R Foundation, from http://www.r-project.org
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.
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
R logo is trademark of the R Foundation, from http://www.r-project.org
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
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
R logo is trademark of the R Foundation, from http://www.r-project.org
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
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
R logo is trademark of the R Foundation, from http://www.r-project.org
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.
SCHEDULE
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
R logo is trademark of the R Foundation, from http://www.r-project.org
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
14:45-15:00 Coffee Break
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)
16:45-17:00 Coffee Break
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
11:30-11:45 Coffee Break
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
15:15-15:30 Coffee Break
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
17:00-17:15 Coffee Break
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)
www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
R logo is trademark of the R Foundation, from http://www.r-project.org
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