Course - Get Started in Machine Learning with Python scikit-learn

© 2014 Persontyle Ltd. All rights reserved.

GET STARTED IN

MACHINE LEARNINGWITH PYTHON SCIKIT-LEARN

5 DAY BOOTCAMP21-25 JULY 2014, LONDON

Data generated through our activities captures plethora ofinformation about our identity, likes and dislikes etc. Thisinformation has tremendous value in every aspect of human life.Programming computers to unravel this hidden information iswhat Machine Learning is all about. It is the art and science ofscientifically deriving insights, patterns and predictions from data.

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<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 makedecisions from data in order to achieve some goal or requirement.Machine learning models matter to the 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.

LEARN THE SCIENCE OF DISCOVERING PATTERNS AND MAKING INTELLIGENT PREDICTIONS FROM BIG DATA

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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,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 necessaryingredient for technological progress.”

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

Why write programs when the computer can instead learn them from data? In

this 5 day bootcamp you will learn how to make this happen. 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.

Bootcamp is designed to help you learn basic principles needed to understand

and apply Machine Learning models and methods using Python Scikit-Learn.

Lots of hands-on examples to step through real-world application of Machine

Learning. Attending this bootcamp will enable you to understand the basic

concepts, become confident in applying the tools and techniques, and provide

a firm foundation from which to explore more advanced methods.

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

[5 day bootcamp to learn basic building blocks of practical Machine Learning]

GET STARTED IN MACHINE LEARNINGWITH PYTHON SCIKIT-LEARN

“In a way Machine Learning has become the new black gold. The application areas are literally endless and from where we stand we haven't even reached the inflection point. If you think about it, there are many industries out there that are just waking up to the reality of big data and data science.”

MARTIN HACK, CEO SKYTREE

Python and scikit-learn logos are the property of their respective owners

WHAT WILL YOU LEARN?

Attend the bootcamp to learn the basic concepts, models and techniques required to perform practical Machine Learning.

GET STARTED IN MACHINE LEARNING

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DAY 1

Understand how the structure and function of the human brain is different from a computer and how this affects learning in each.

Define Machine Learning, why it matters, and discuss its relationship to data mining, data science, and statistics.

Understand the steps in the machine learning pipeline, from data acquisition and feature generation, to training and model selection.

Overview of core Machine Learning terminology i.e. features, instance, model selection, bias, variance, generalization, precision, etc.

Review of the fundamentals of linear algebra, calculus, statistics, and probability theory.

DAY 2

Doing Machine Learning - Review fundamentals of practical Machine Learning• Reading the data and cleaning it.• Exploring and understanding the input data.• Analysing how best to present the data to the learning algorithm.• Choosing the right model and learning algorithm.• Measuring the performance correctly.

Basics of Python programming language and environment.

Scientific Python building blocks and workflow • NumPy: Base n-dimensional array package• SciPy: Fundamental library for scientific computing• IPython: Enhanced interactive console• Pandas: Data structures and analysis

Overview of Scikit-learn: Machine Learning in Python

Our first Machine Learning Application - K Nearest Neighbours

Labs• Setting up the environment• Python programing basics (load data, simple histogram, select rows, columns,

scatter plot, simple stats, ...)• Linear Regression

WHAT WILL YOU LEARN?

GET STARTED IN MACHINE LEARNING

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DAY 3

Generally Applied Algorithms and Applications • Naive Bayes • Support Vector Machines• Logistic Regression • Decision Trees

Labs• Detecting Spam using Machine Learning • Predicting house prices with regression• Image recognition with Support Vector Machines

DAY 4

Dimensionality Reduction - Reducing the number of random variables to consider• Feature selection and feature extraction methods• Principal Component Analysis

Clustering - Automatic grouping of similar objects into sets.• Overview of clustering methods• Applications and Algorithms

Basics of Crab - Recommender systems in Python

BigML - Putting the power of Machine Learning in your hands

Labs • Dimensionality reduction practical example • Clustering handwritten digits with k-means

Python and scikit-learn logos are the property of their respective owners.

WHAT WILL YOU LEARN?

GET STARTED IN MACHINE LEARNING

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

DAY 5

Model Selection and Evaluation in Scikit-learn - Comparing, validating and choosing parameters and models

Overview of Pre-processing in Scikit-learn - Feature extraction and normalization.

Putting it all together - Final Kaggle Project

Current Hot Topics• Large scale Machine Learning• Deep Learning• Watson style learning• Probabilistic programming• Machine Learning as a Service

WHO SHOULD TAKE THIS COURSE?

You are interested in Machine Learning. You have read a book or taken an online course and now want to know more and learn how to apply Machine Learning to solve real problems. Well-suited to machine learning beginners or those with some experience.

All Machine Learning

Enthusiasts

Business Professionals

Technologists/ Developers

Data/Market/Research Analysts

Business/ Technology Consultants

PREREQUISITES

Basic understanding of calculus, statistics, probability theory, linear algebra. This will be refreshed but not in detail. Basic knowledge of python is required. All lab sessions will be done using IPython notebooks and Scikit-learn.

Persontyle trainers are passionate about meeting each participantslearning needs. They have been chosen both for their extensivepractical Data Science and Machine Learning experience and fortheir ability to educate and interact with natural empathy. All of ourtrainers have worked on a variety of data science and MachineLearning projects. They share their academic knowledge and real-world experience and each individual adds their own uniqueperspective to the course. Our trainers present in a style that isinformal, 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

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GET STARTED IN MACHINE LEARNING

WHAT SHOULD I BRING?

Along with bringing your laptop and charger, don’t forget to bringloads of curiosity, scepticism, eagerness to participate and thedesire to learn.

THE SCHOOL OF DATA SCIENCE The School of Data Science, a project of Persontyle, specializes in designing and delivering structured, relevant, practical and affordable learning experiences for all of us to understand data science in simple human terms.

RETURN ON INVESTMENT (ROI) CONVINCE YOUR BOSS

We all need to learn how to analyse data, find the value and gleaninsights. The advent of the data driven connected era means thatanalyzing massive scale, messy, noisy, and unstructured data is goingto increasingly form part of everyone's work.

The School of Data Science learning programs provide a uniqueinvestment opportunity that pays for itself many times over.

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

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World-class Instructors

Develop Practical Data Science Skills

Real World Industry Use Cases

Short Courses For Time Convenience

Value For Money

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