Machine Learning Workshop Dubai #MLDXB

INFORM– INSPIRE–EMPOWER–INNOVATE– TRANSFORMEverati is business and technology innovation media company specializing in intelligent enterprise transformation. We bring our exceptional cross-industry knowledge to the government, industry and the professionals through a combination of events, training, open meetings and summits, providing cutting-edge insights to drive growth in digital economy.We are dedicated to enable enterprise innovation, digital leadership and talent development through premier and specialized events.We connect industry leaders across the business spectrum, from leading companies to disruptive and exciting new startups, facilitating the sharing of ideas. Our specialties include Data Science, Big Data, Machine Learning, Internet of Things (IoT), Open Data, Smart Cities, Smart Government, AI, Deep Learning, Robotics, Cybersecurity, Digital Transformation, Enterprise Analytics, Innovation, Digital Strategy and 21st Digital Talent.Everati is an independent platform for the interaction and discussion of the latest business, technology and industry strategies and trends. Everati strives to combine excellence and professionalism through collaborations and partnerships with inter-governmental, professional and industry groups across GCC, as well as private and public sector organizations.Everati is a brand of Persontyle Group, UK.

EVERATI

<H2>Data Science Middle East (#DSME) in partnership with PAPIs are excited to announce this 2-day hands-on Machine Learning Workshop. </H2>
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<H2>> Machine Learning Made Easy and Practical! </H2>
Most Machine Learning courses are given from the perspective of a researcher/academic and focus on the theory and mathematics of the machine learning models. This workshop takes the perspective of learning by working on real machine learning problems using open source tools and platforms. We’ll go all the way from data preparation to the integration of predictive models in applications and their deployment in production.
"Just like development where you don’t need to know a thing about computability or <A TARGET="_blank" HREF="https://en.wikipedia.org/wiki/Big_O_notation" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">big-O notation</A> to write code and ship useful and reliable software, you can work machine learning problems end-to-end without a background in statistics, probability and linear algebra." Jason Brownlee, Machine Learning Mastery 
<H2>> The Right Mix of Theory and Hands-on Work</H2>
The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML & Cortana Intelligence Suite, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles.
Workshop is 2 day long and comprises 8 modules of 3 blocks of 30’ each—including time for questions. Blocks are either Theory or Exercise, with at least one Exercise per module. The goal is to make you operational with machine learning at the end of the workshop.
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<H2>> What You Will Learn</H2>
<UL>
<LI>How Machine Learning works, its possibilities, its limitations, and the importance of data</LI>
<LI>How to create, evaluate and deploy predictive models, via open source libraries, APIs and ML-as-a-Service platforms</LI>
<LI>How to formulate ML problems that create value from data and that power predictive applications with innovative features</LI>
</UL>
<H3>Day 1: Core ML</H3>
<H4>Module 1: Introduction to ML</H4>
<UL>
<LI CLASS="MsoNormal">A data-driven digital world</LI>
<LI CLASS="MsoNormal">Define machine learning, why it matters, and discuss its relationship to analytics, data science, and big data.</LI>
<LI CLASS="MsoNormal">Key ML concepts and terminology</LI>
<LI CLASS="MsoNormal">Possibilities and use cases</LI>
</UL>
Coffee break
<H4>Module 2: Model creation</H4>
<UL>
<LI CLASS="MsoNormal">[Theory] Intuitions behind learning techniques and high-level overview (nearest neighbors, linear models, logistic regression, decision trees)</LI>
<LI CLASS="MsoNormal">[Exercise] Introduction to <A TARGET="_blank" HREF="http://jupyter.org/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Jupyter notebooks</A> and Python programming</LI>
<LI CLASS="MsoNormal">[Exercise] Creating a predictive model with <A TARGET="_blank" HREF="http://scikit-learn.org/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">scikit-learn</A></LI>
</UL>
Lunch break
<H4>Module 3: Operationalization</H4>
<UL>
<LI CLASS="MsoNormal">[Theory] Functioning of REST APIs and importance for ML deployment in production</LI>
<LI CLASS="MsoNormal">[Exercise] Deploying your own models as scalable APIs with <A TARGET="_blank" HREF="http://azure.microsoft.com/services/machine-learning/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Microsoft Azure ML</A> & Cortana Intelligence Suite, and querying them anywhere</LI>
<LI CLASS="MsoNormal">[Theory] Overview of ML-as-a-Service and Predictive API technologies (open source, proprietary and hybrid)</LI>
</UL>
Coffee break
<H4>Module 4: Evaluation</H4>
<UL>
<LI CLASS="MsoNormal">[Theory] Evaluating ML models: criteria, methods, basic performance measures and baselines</LI>
<LI CLASS="MsoNormal">[Theory] Performance measures for classification (recall and precision)</LI>
<LI CLASS="MsoNormal">[Exercise] MLaaS evaluation: <A TARGET="_blank" HREF="https://aws.amazon.com/machine-learning/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Amazon ML</A> and <A TARGET="_blank" HREF="http://www.bigml.com/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">BigML</A> (APIs, Python wrappers, command-line tools and web dashboards)</LI>
</UL>
<H3 CLASS="MsoNormal"> </H3>
<H3 CLASS="MsoNormal">Day 2: Going further with ML</H3>
<H4>Module 5: Model selection</H4>
<UL>
<LI CLASS="MsoNormal">[Theory] Boosting predictions’ accuracy with ensembles</LI>
<LI CLASS="MsoNormal">[Exercise] Comparing models efficiently with <A TARGET="_blank" HREF="http://skll.readthedocs.org/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">SKLL</A></LI>
<LI CLASS="MsoNormal">[Theory] Cross-validation</LI>
</UL>
Coffee break
<H4>Module 6: Data preparation</H4>
<UL>
<LI CLASS="MsoNormal">[Theory] Limitations of ML</LI>
<LI CLASS="MsoNormal">[Exercise] Feature engineering on select use cases (priority inbox, real-estate price prediction, credit scoring, churn detection)</LI>
<LI CLASS="MsoNormal">[Exercise] Finding issues in data and fixing them in <A TARGET="_blank" HREF="http://pandas.pydata.org/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Pandas</A></LI>
</UL>
Lunch break
<H4>Module 7: Advanced topics: Unsupervised Learning, Deep Learning and Recommender Systems</H4>
<UL>
<LI CLASS="MsoNormal">[Exercise] Running automated clustering and anomaly detection with BigML’s API and interpreting results</LI>
<LI CLASS="MsoNormal">[Theory] Automatic feature extraction from text and images (illustrated with <A TARGET="_blank" HREF="http://www.indico.io/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Indico’s</A> Deep Learning API)</LI>
<LI CLASS="MsoNormal">[Theory] Building recommender systems by reduction to classification and collaborative filtering</LI>
</UL>
Coffee break
<H4>Module 8: Developing your own use case</H4>
<UL>
<LI CLASS="MsoNormal">[Theory] Formulating your own problem: asking the right questions and specifying key aspects of the problem with the <A TARGET="_blank" HREF="http://github.com/louisdorard/machinelearningcanvas" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Machine Learning Canvas</A></LI>
<LI CLASS="MsoNormal">[Exercise] Applying the Canvas to your own problem, or to credit scoring</LI>
<LI CLASS="MsoNormal">Conclusions: recap of key take-aways</LI>
</UL>
<H3> </H3>
<H2>> Target Audience</H2>
This workshop is ideal for:
-          All of you interested in practically learning how to use Machine Learning to make your Apps and Systems more intelligent and smart. 
-          Developers / Programmers / Software Engineers
-          Technology Architects, Development Team Leads, Data Analysts and IT Project Manager
-           CTOs , CIOs and CSOs
From industries including:
-          Government/ Public Sector
-          Technology Companies and Start-ups
-          Banking and Finance
-          Retail and Media
-          Aviation
-          Telecommunication
-          Manufacturing
-          Healthcare
-          Universities and Colleges 
<H2> </H2>
<H2 CLASS="MsoTitle">> Workshop Facilitators</H2>
<H3 CLASS="MsoNormal">Louis Dorard</H3>
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Louis Dorard is General Chair of the <A TARGET="_blank" HREF="http://papis.io/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">PAPIs</A> conferences, author of <A TARGET="_blank" HREF="http://louisdorard.com/machine-learning-book" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Bootstrapping Machine Learning</A>, and an independent consultant. Louis holds a PhD in Machine Learning from University College London.
“Louis is an excellent teacher and you can feel how knowledgeable and passionate he is about this domain. I highly recommend this course!” 
— Charles-Emmanuel Camus, Web Developer at Groupe Express-Roularta
 
 
<H3 CLASS="MsoNormal">Ali Syed</H3>
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Chief Data Scientist and Strategist <A TARGET="_blank" HREF="http://www.persontyle.com/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Persontyle,</A> UK and The School of Data Science, UK
As a global data and analytics thought leader, Ali is collaborating with thinkers, researchers, designers, makers, doers, and business leaders. He has more than 16 years of professional experience and success assisting public and commercial organisations in using data analytics, insights and machine intelligence as a value amplifier. He works with people to understand and translate their aspirations into data and analytical solutions that enhance their ability to make choices, better decisions, realise performance gains and uncover opportunities. Before founding Persontyle, Ali worked with some of the leading technology and consulting organisations of the world namely PwC, KPMG BearingPoint, Sapient, EMC, UBS, NHS UK, and Capgemini.
<H2>> Registration</H2>
Exclusive 20% Discount – Register before 29th May 2016 for just US $998. Standard Price is US $1248
You can reserve your seat by registering online or by emailing Natasha at <A TARGET="_blank" HREF="mailto:natasha@datasciencetraining.me" REL="nofollow noopener noreferrer">natasha@datasciencetraining.me</A> or by calling us on +971 506024248
For more information about the workshop please visit <A TARGET="_blank" HREF="http://www.papis.io/workshops/operational-machine-learning" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://www.papis.io/workshops/operational-machine-learning</A> or contact our team at <A TARGET="_top" HREF="mailto:register@datasciencetraining.me" REL="nofollow noopener noreferrer">register@datasciencetraining.me</A>
<H2 CLASS="MsoNormal">> Sponsors and Partners</H2>
 
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Are you looking to showcase big data, data science and analytics technologies and services in front of the professional industry gathering? If the answer is yes, then get in touch!  
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To enquire about and exhibiting, contact our team! Email at <A TARGET="_top" HREF="mailto:sponsorship@datasciencetraining.me" REL="nofollow noopener noreferrer">sponsorship@datasciencetraining.me </A> or call us on +971 50 602 4248. 
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<H2> > About Data Science Middle East (DSME)</H2>
Data Science Middle East (<A TARGET="_blank" HREF="http://www.persontyle.com/datasciencemiddleeast/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">DSME</A>) is an open collaboration programme for data talent and digital leadership development for Middle East and GCC countries. For membership details, collaborations and industry partnership opportunities, please email <A TARGET="_top" HREF="mailto:partners@persontyle.com" REL="nofollow noopener noreferrer">partners@persontyle.com</A>

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Machine Learning Workshop Dubai #MLDXB

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