MACHINE LEARNING BASICS WITH R

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1 MACHINE LEARNING [Hands-on Introduction of Supervised Machine Learning Methods] DURATION 2 DAY

2 The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. - Tom Mitchell < M A C H I N E L E A R N I N G > Data generated through our activities captures plethora of information about our identity, likes and dislikes etc. This information has tremendous value in every aspect of human life. Programming computers to unravel this hidden information is what Machine Learning is all about. It is the art and science of scientifically deriving insights, patterns and predictions from data. Though it has been an area of active research for over 50 years, Machine Learning is currently undergoing a renaissance driven by Moore's law and the rise of big data. Large private and public investment in the area has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Computer based Machine Learning algorithms now outperform humans on tasks such as handwritten digit recognition, traffic sign recognition, and even on some complex reasoning tasks as demonstrated by IBM's Watson winning Jeopardy Machine Learning models and programs automatically make decisions from data in order to achieve some goal or requirement. Machine learning models matter to the world. Because they are; #EFFICIENT Machine Learning models predict and detect partners faster than any other manual program or method. #EFFECTIVE Machine Learning models can do better job than humans when analysing and predicting large scale and streaming data sets (big data). #SCALE Machine Learning models can provide solutions to large data problems that traditional systems can not solve.

3 Over the past two decades Machine Learning has become one of the mainstays of information technology and with that, a rather central, albeit usually 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 for technological progress. DR. ALEXANDER J. SMOLA, PROFESSOR, CARNEGIE MELLON UNIVERSITY 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 Machine Learning include: 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

4 MACHINE LEARNING This course is meant to be a fast-paced, hands-on introduction to Machine Learning using R. The course will be focusing mainly on basics of Machine Learning methods and practical implementation of these methods to solve real-world problems. This course aims to develop basic understanding of supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression. With a focus on the former, it takes a close look at typical Machine Learning techniques and 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 understand supervised Machine Learning algorithms and models, and interpret their output, which is important for solving a range of data science problems. Without getting too much into the mathematics of Machine Learning, this course dwells on the ideas and principles of it, along with the two main methods that all Machine Learning practitioners use. With theory and hands-on sessions intertwined, the course illustrates the usefulness of Machine Learning and how you can learn it effectively without losing sight of its elegance and value. Apart from the Classification techniques, you will learn about how to validate any classifier's performance, when to use what, and how the new innovations come about. 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

5 MACHINE LEARNING WHAT WILL YOU LEARN? PREREQUISITES 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 Knowledge of R programming language and familiarity with linear algebra. Basic familiarity with statistics and probability theory is recommended. * If you don't know R then first attend the Getting Started with R and Data Analysis workshop.

6 SCHEDULE Day 1 MACHINE LEARNING 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 dataset 14:45-15:15 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 dataset 16:45-17:00 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, Recall 17:45-18:30 LAB: creating confusion table and basic validation metrics for all previous drills (adult census data)

7 WHO SHOULD ATTEND COURSE INSTRUCTORS Persontyle trainers are passionate about meeting each participants learning needs. They have been chosen both for their extensive practical Data Science and Machine Learning experience and for their ability to educate and interact with natural empathy. All of our trainers have worked on a variety of data science and Machine Learning projects. They share their academic knowledge and real-world experience and each individual adds their own unique perspective to the course. Our trainers present in a style that is informal, entertaining and highly interactive. Guest Speakers MACHINE LEARNING Anyone interested in learning and applying supervised machine learning methods and R to solve real-world data problems. Ideal for people interested 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 basic competence in statistics but wish to begin using R for machine learning for the first time. Business leaders, Machine Learning practitioners, and academic researchers covering use cases, case studies and sharing practical experience of applying Data Science and Machine Learning in their organizations. A breakthrough in Machine Learning would be worth ten Microsofts BILL GATES, CHAIRMAN, MICROSOFT

8 MACHINE LEARNING 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. World-class Instructors Develop Practical Data Science Skills Real World Industry Use Cases Short Courses For Time Convenience Value For Money "For the best return on your money, pour your purse into your head." Benjamin Franklin Limited seats. We encourage you to register as soon as you can. Register Now For corporate bookings or to organize on-site training hello@persontyle.com or call now +44 (0) 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. /school Follow us on Like us on Facebook Get in touch! hello@personyyle.com