SlideShare une entreprise Scribd logo
1  sur  67
Télécharger pour lire hors ligne
@dhianadeva
MACHINE LEARNING
FOR EVERYONE
Demystifying machine learning!
AGENDA
Goal:
Encourage you to start a machine learning project. Today!
● About me
● About you
● Machine Learning
● Problems
● Design
● Algorithms
● Evaluation
● Code snippets
● Pay-as-you-go
● Competitions
ABOUT ME
Electronics Engineering, Software Development and Data
Science… Why not?
DHIANA DEVA
NEURALTB
CERN
NEURALRINGER
DJBRAZIL
HIGGS CHALLENGE
ABOUT YOU
You can do it!
FOR ALL
MASSIVE ONLINE
OPEN COURSES
OPEN SOURCE TOOLS
OPEN SOURCE
PYTHON TOOLS
PAY-AS-YOU-GO
SERVICES
MACHINE LEARNING
Learning, machine learning!
EXPECTATIONS
REALITY
FEATURE
EXTRACTION
Item {
Feature 1
Feature 2
…
Feature N
FEATURE
SPACE
SUPERVISED
LEARNING
458316,86.513,24.312,64.983,65.8
58317,135.493,2.204,101.966,46.5
458318,-999.0,91.803,113.007,120.
Feature VectorsItems
New item
Machine Learning
Algorithm
458316,86.513,24.312,64.983,65.8
Feature Vector
133
Expected
Label
Predictive Model
150
623
74
Labels
UNSUPERVISED
LEARNING
Items
New item
Machine Learning
Algorithm
458316,86.513,24.312,64.983,65.8
Feature Vector
Predictive Model
Better
Representation
458316,86.513,24.312,64.983,65.8
58317,135.493,2.204,101.966,46.5
458318,-999.0,91.803,113.007,120.
Feature Vectors
MODELS
BIOLOGICAL
MOTIVATION
PROBLEMS
I've got 99 problems, but machine learning ain't one!
CLASSIFICATION
?
A
B
CLASSIFICATION
REGRESSION
? 8 15 7 1
11 13 6 3
REGRESSION
CLUSTERING
CLUSTERING
DIMENSIONALITY
REDUCTION
DIMENSIONALITY
REDUCTION
DESIGN DECISIONS
1, 2 steps!
NORMALIZATION
● min-max
● z-score
TRAINING
REGULARIZATION
RELEVANCE ANALYSIS
CROSS VALIDATION
ALGORITHMS
Cheat sheet included!
CHEAT SHEET
CHEAT SHEET
ALGORITHMS PT. I
Linear Regression
Decision Trees Random Forest
ALGORITHMS PT. II
K-Nearest Neighbors K-Means
NEURAL NETWORKS
SELF-ORGANIZING MAPS
PRINCIPAL
COMPONENTS ANALYSIS
T-SNE
EVALUATION
How you doin'?
PRECISION AND
ACCURACY
CONFUSION MATRIX
Precision =
TP
Recall =
F1-score =
2 * precision * recall
precision + recall
TP + FP
TP
TP + FN
TP = True Positives
TN = True Negatives
FP = False Positives
FN = False Negatives
ROC CURVE
A/B TESTS
Code Snippets
"Hello, Machine Learning"
MATLAB 101
[x,y] = ovarian_dataset;
net = patternnet(5);
[net,tr] = train(net,x,y);
testX = x(:,tr.testInd);
testY = net(testX);
MATLAB 201
net = patternnet(14);
net.input.processFcns = {'mapminmax', 'fixunknowns', 'processpca'};
net.inputs{1}.processParams{3}.maxfrac = 0.02;
net.trainFcn = 'trainlm';
net.performFcn = 'mse';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
[net, tr] = train(net_config, test_inputs, train_targets);
outputs = net(test_inputs);
R
library(randomForest)
raw.orig < - read.csv(file="train.txt", header=T, sep="t")
frmla = Metal ~ OTW + AirDecay + Koc
fit.rf = randomForest(frmla, data=raw)
print(fit.rf)
importance(fit.rf)
SCIKIT LEARN
dataset = pd.read_csv('Data/train.csv')
target = dataset.Activity.values
train = dataset.drop('Activity', axis=1).values
test = pd.read_csv('Data/test.csv').values
rf = RandomForestClassifier(n_estimators=100, n_jobs=-1)
rf.fit(train, target)
predicted_probs = [x[1] for x in rf.predict_proba(test)]
importances = rf.feature_importances_
PAY-AS-YOU-GO
SERVICES
Amazon Machine Learning
AMAZON
MACHINE LEARNING
Five easy steps
1. Upload csv dataset to Amazon S3
2. Create Datasource with metadata about uploaded
dataset
3. Create ML Model with configurations for model training
4. Create Evaluation to analyse and tune model efficiency
5. Create Prediction to use trained model with new data
EVALUATION
SDKs
DATA SCIENCE
COMPETITIONS
Challenge accepted!
KAGGLE
SPONSORED
END TO END
TRAINTRAIN.CSV
TEST.CSV
TRAINED.DAT
RUN SOLUTION.CSV
THANK YOU
Questions?
Dhiana Deva
ddeva@thoughtworks.com

Contenu connexe

Tendances

A Comprehensive Review of Large Language Models for.pptx
A Comprehensive Review of Large Language Models for.pptxA Comprehensive Review of Large Language Models for.pptx
A Comprehensive Review of Large Language Models for.pptxSaiPragnaKancheti
 
Game Development With Python and Pygame
Game Development With Python and PygameGame Development With Python and Pygame
Game Development With Python and PygameChariza Pladin
 
PPT on Data Science Using Python
PPT on Data Science Using PythonPPT on Data Science Using Python
PPT on Data Science Using PythonNishantKumar1179
 
Python programming introduction
Python programming introductionPython programming introduction
Python programming introductionSiddique Ibrahim
 
Event Handling in java
Event Handling in javaEvent Handling in java
Event Handling in javaGoogle
 
Computer Graphics Programes
Computer Graphics ProgramesComputer Graphics Programes
Computer Graphics ProgramesAbhishek Sharma
 
pandas - Python Data Analysis
pandas - Python Data Analysispandas - Python Data Analysis
pandas - Python Data AnalysisAndrew Henshaw
 
Python Pandas for Data Science cheatsheet
Python Pandas for Data Science cheatsheet Python Pandas for Data Science cheatsheet
Python Pandas for Data Science cheatsheet Dr. Volkan OBAN
 
Python Programming Language | Python Classes | Python Tutorial | Python Train...
Python Programming Language | Python Classes | Python Tutorial | Python Train...Python Programming Language | Python Classes | Python Tutorial | Python Train...
Python Programming Language | Python Classes | Python Tutorial | Python Train...Edureka!
 
Maharishi University of Management (MSc Computer Science test questions)
Maharishi University of Management (MSc Computer Science test questions)Maharishi University of Management (MSc Computer Science test questions)
Maharishi University of Management (MSc Computer Science test questions)Dharma Kshetri
 

Tendances (20)

Introduction to ChatGPT and Overview of its capabilities and functionality.pdf
Introduction to ChatGPT and Overview of its capabilities and functionality.pdfIntroduction to ChatGPT and Overview of its capabilities and functionality.pdf
Introduction to ChatGPT and Overview of its capabilities and functionality.pdf
 
NUMPY
NUMPY NUMPY
NUMPY
 
ChatGPT PPT
ChatGPT PPTChatGPT PPT
ChatGPT PPT
 
A Comprehensive Review of Large Language Models for.pptx
A Comprehensive Review of Large Language Models for.pptxA Comprehensive Review of Large Language Models for.pptx
A Comprehensive Review of Large Language Models for.pptx
 
Game Development With Python and Pygame
Game Development With Python and PygameGame Development With Python and Pygame
Game Development With Python and Pygame
 
Uses of AI text bot.pdf
Uses of AI text bot.pdfUses of AI text bot.pdf
Uses of AI text bot.pdf
 
PPT on Data Science Using Python
PPT on Data Science Using PythonPPT on Data Science Using Python
PPT on Data Science Using Python
 
Python : Functions
Python : FunctionsPython : Functions
Python : Functions
 
Threads in python
Threads in pythonThreads in python
Threads in python
 
Hackathon winning pitch
Hackathon winning pitchHackathon winning pitch
Hackathon winning pitch
 
Python programming introduction
Python programming introductionPython programming introduction
Python programming introduction
 
Python programming : Standard Input and Output
Python programming : Standard Input and OutputPython programming : Standard Input and Output
Python programming : Standard Input and Output
 
Numpy tutorial
Numpy tutorialNumpy tutorial
Numpy tutorial
 
Event Handling in java
Event Handling in javaEvent Handling in java
Event Handling in java
 
Computer Graphics Programes
Computer Graphics ProgramesComputer Graphics Programes
Computer Graphics Programes
 
pandas - Python Data Analysis
pandas - Python Data Analysispandas - Python Data Analysis
pandas - Python Data Analysis
 
Python Pandas for Data Science cheatsheet
Python Pandas for Data Science cheatsheet Python Pandas for Data Science cheatsheet
Python Pandas for Data Science cheatsheet
 
Python Programming Language | Python Classes | Python Tutorial | Python Train...
Python Programming Language | Python Classes | Python Tutorial | Python Train...Python Programming Language | Python Classes | Python Tutorial | Python Train...
Python Programming Language | Python Classes | Python Tutorial | Python Train...
 
Python ppt
Python pptPython ppt
Python ppt
 
Maharishi University of Management (MSc Computer Science test questions)
Maharishi University of Management (MSc Computer Science test questions)Maharishi University of Management (MSc Computer Science test questions)
Maharishi University of Management (MSc Computer Science test questions)
 

Similaire à QCon Rio - Machine Learning for Everyone

Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scalaAutomate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scalaChetan Khatri
 
maxbox starter60 machine learning
maxbox starter60 machine learningmaxbox starter60 machine learning
maxbox starter60 machine learningMax Kleiner
 
Learning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and KaggleLearning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and KaggleYvonne K. Matos
 
Deep Learning with Apache Spark: an Introduction
Deep Learning with Apache Spark: an IntroductionDeep Learning with Apache Spark: an Introduction
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
 
MATLAB for Technical Computing
MATLAB for Technical ComputingMATLAB for Technical Computing
MATLAB for Technical ComputingNaveed Rehman
 
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...Chetan Khatri
 
AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...
AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...
AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...GeeksLab Odessa
 
Machine Learning and Go. Go!
Machine Learning and Go. Go!Machine Learning and Go. Go!
Machine Learning and Go. Go!Diana Ortega
 
Object Oriented Programming in Matlab
Object Oriented Programming in Matlab Object Oriented Programming in Matlab
Object Oriented Programming in Matlab AlbanLevy
 
Faster computation with matlab
Faster computation with matlabFaster computation with matlab
Faster computation with matlabMuhammad Alli
 
Feature Engineering - Getting most out of data for predictive models - TDC 2017
Feature Engineering - Getting most out of data for predictive models - TDC 2017Feature Engineering - Getting most out of data for predictive models - TDC 2017
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
 
Workshop - Introduction to Machine Learning with R
Workshop - Introduction to Machine Learning with RWorkshop - Introduction to Machine Learning with R
Workshop - Introduction to Machine Learning with RShirin Elsinghorst
 
What is in that image
What is in that image What is in that image
What is in that image Debarko De
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionTe-Yen Liu
 
Feature Engineering - Getting most out of data for predictive models
Feature Engineering - Getting most out of data for predictive modelsFeature Engineering - Getting most out of data for predictive models
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
 
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...IRJET Journal
 
Accelerated Training of Transformer Models
Accelerated Training of Transformer ModelsAccelerated Training of Transformer Models
Accelerated Training of Transformer ModelsDatabricks
 
Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...
Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...
Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...Universitat Politècnica de Catalunya
 

Similaire à QCon Rio - Machine Learning for Everyone (20)

Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scalaAutomate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
Automate ml workflow_transmogrif_ai-_chetan_khatri_berlin-scala
 
maxbox starter60 machine learning
maxbox starter60 machine learningmaxbox starter60 machine learning
maxbox starter60 machine learning
 
Learning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and KaggleLearning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and Kaggle
 
Deep Learning with Apache Spark: an Introduction
Deep Learning with Apache Spark: an IntroductionDeep Learning with Apache Spark: an Introduction
Deep Learning with Apache Spark: an Introduction
 
MATLAB for Technical Computing
MATLAB for Technical ComputingMATLAB for Technical Computing
MATLAB for Technical Computing
 
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
TransmogrifAI - Automate Machine Learning Workflow with the power of Scala an...
 
AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...
AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...
AI&BigData Lab 2016. Руденко Петр: Особенности обучения, настройки и использо...
 
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
 
Machine Learning and Go. Go!
Machine Learning and Go. Go!Machine Learning and Go. Go!
Machine Learning and Go. Go!
 
Object Oriented Programming in Matlab
Object Oriented Programming in Matlab Object Oriented Programming in Matlab
Object Oriented Programming in Matlab
 
Faster computation with matlab
Faster computation with matlabFaster computation with matlab
Faster computation with matlab
 
K fold validation
K fold validationK fold validation
K fold validation
 
Feature Engineering - Getting most out of data for predictive models - TDC 2017
Feature Engineering - Getting most out of data for predictive models - TDC 2017Feature Engineering - Getting most out of data for predictive models - TDC 2017
Feature Engineering - Getting most out of data for predictive models - TDC 2017
 
Workshop - Introduction to Machine Learning with R
Workshop - Introduction to Machine Learning with RWorkshop - Introduction to Machine Learning with R
Workshop - Introduction to Machine Learning with R
 
What is in that image
What is in that image What is in that image
What is in that image
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis Introduction
 
Feature Engineering - Getting most out of data for predictive models
Feature Engineering - Getting most out of data for predictive modelsFeature Engineering - Getting most out of data for predictive models
Feature Engineering - Getting most out of data for predictive models
 
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...
 
Accelerated Training of Transformer Models
Accelerated Training of Transformer ModelsAccelerated Training of Transformer Models
Accelerated Training of Transformer Models
 
Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...
Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...
Software Frameworks for Deep Learning (D1L7 2017 UPC Deep Learning for Comput...
 

Plus de Dhiana Deva

Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Dhiana Deva
 
Machine Learning in Python - PyLadies Stockholm
Machine Learning in Python - PyLadies StockholmMachine Learning in Python - PyLadies Stockholm
Machine Learning in Python - PyLadies StockholmDhiana Deva
 
Machine Learning for Everyone
Machine Learning for EveryoneMachine Learning for Everyone
Machine Learning for EveryoneDhiana Deva
 
Um Pouquinho Sobre Métodos Ágeis - Rails Girls SP
Um Pouquinho Sobre Métodos Ágeis - Rails Girls SPUm Pouquinho Sobre Métodos Ágeis - Rails Girls SP
Um Pouquinho Sobre Métodos Ágeis - Rails Girls SPDhiana Deva
 
Agile Data Science
Agile Data ScienceAgile Data Science
Agile Data ScienceDhiana Deva
 
My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!
My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!
My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!Dhiana Deva
 
AR Post-its @ CBSOFT
AR Post-its @ CBSOFTAR Post-its @ CBSOFT
AR Post-its @ CBSOFTDhiana Deva
 
Self-Organizing Maps 101 (Dhiana Deva)
Self-Organizing Maps 101 (Dhiana Deva)Self-Organizing Maps 101 (Dhiana Deva)
Self-Organizing Maps 101 (Dhiana Deva)Dhiana Deva
 
Sistemas de recomendação
Sistemas de recomendaçãoSistemas de recomendação
Sistemas de recomendaçãoDhiana Deva
 

Plus de Dhiana Deva (10)

Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019
 
Machine Learning in Python - PyLadies Stockholm
Machine Learning in Python - PyLadies StockholmMachine Learning in Python - PyLadies Stockholm
Machine Learning in Python - PyLadies Stockholm
 
Machine Learning for Everyone
Machine Learning for EveryoneMachine Learning for Everyone
Machine Learning for Everyone
 
Um Pouquinho Sobre Métodos Ágeis - Rails Girls SP
Um Pouquinho Sobre Métodos Ágeis - Rails Girls SPUm Pouquinho Sobre Métodos Ágeis - Rails Girls SP
Um Pouquinho Sobre Métodos Ágeis - Rails Girls SP
 
Agile Data Science
Agile Data ScienceAgile Data Science
Agile Data Science
 
We love NLTK
We love NLTKWe love NLTK
We love NLTK
 
My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!
My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!
My First Attempt on Kaggle - Higgs Machine Learning Challenge: 755st and Proud!
 
AR Post-its @ CBSOFT
AR Post-its @ CBSOFTAR Post-its @ CBSOFT
AR Post-its @ CBSOFT
 
Self-Organizing Maps 101 (Dhiana Deva)
Self-Organizing Maps 101 (Dhiana Deva)Self-Organizing Maps 101 (Dhiana Deva)
Self-Organizing Maps 101 (Dhiana Deva)
 
Sistemas de recomendação
Sistemas de recomendaçãoSistemas de recomendação
Sistemas de recomendação
 

Dernier

ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.JasonViviers2
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptaigil2
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024Becky Burwell
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)Data & Analytics Magazin
 

Dernier (17)

ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .ppt
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)
 

QCon Rio - Machine Learning for Everyone