Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
This slide includes :
Types of Machine Learning
Supervised Learning
Brain
Neuron
Design a Learning System
Perspectives
Issues in Machine Learning
Learning Task
Learning as Search
Hypothesis
Version Spaces
Candidate elimination algorithm
linear Discriminant
Perception
Linear Separability
Linear Regression
Unsupervised Learning
Reinforcement Learning
Evolutionary Learning
This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define predictive modeling as a statistical model or machine learning model used to predict future behavior based on past behavior.
Predictive modeling and predictive analytics has often been used as synonyms. In recent years, that has been changing and predictive modeling is really as subset of analytics, which may also include descriptive and decision modeling. Of course it also encompasses the data mining and analysis that must be performed before and after.
In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)—this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
Anomaly Detection and Spark Implementation - Meetup Presentation.pptxImpetus Technologies
StreamAnalytix sponsored a meetup on “Anomaly Detection Techniques and Implementation using Apache Spark” which took place on Tuesday December 5, 2017 at Larkspur Landing Milpitas Hotel, Milpitas, CA. The meetup was led by Maxim Shkarayev, Lead Data Scientist, Impetus Technologies along with Punit Shah, Solution Architect, StreamAnalytix and Anand Venugopal, Product Head & AVP, StreamAnalytix, who introduced and summarized the vast field of Anomaly Detection and its applications in various industry problems. The speakers at the event also offered a structured approach to choose the right anomaly detection techniques based on specific use-cases and data characteristics which was followed by a demonstration of some real-world anomaly detection use-cases on Apache Spark based analytics platform.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Data Science is concerned with the analysis of large amounts of data. When the volume of data is really large, it requires the use of cooperating, distributed machines. The most popular method of doing this is Hadoop, a collection of programs to perform computations on connected machines in a cluster. Hadoop began life as an open-source implementation of MapReduce, an idea first developed and implemented by Google for its own clusters. Though Hadoop's MapReduce is Java-based, and quite complex, this talk focuses on the "streaming" facility, which allows Python programmers to use MapReduce in a clean and simple way. We will present the core ideas of MapReduce and show you how to implement a MapReduce computation using Python streaming. The presentation will also include an overview of the various components of the Hadoop "ecosystem."
NYC Data Science Academy is excited to welcome Sam Kamin who will be presenting an Introduction to Hadoop for Python Programmers a well as a discussion of MapReduce with Streaming Python.
Sam Kamin was a professor in the University of Illinois Computer Science Department. His research was in programming languages, high-performance computing, and educational technology. He taught a wide variety of courses, and served as the Director of Undergraduate Programs. He retired as Emeritus Associate Professor, and worked at Google until taking his current position as VP of Data Engineering in NYC Data Science Academy.
--------------------------------------
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
This slide includes :
Types of Machine Learning
Supervised Learning
Brain
Neuron
Design a Learning System
Perspectives
Issues in Machine Learning
Learning Task
Learning as Search
Hypothesis
Version Spaces
Candidate elimination algorithm
linear Discriminant
Perception
Linear Separability
Linear Regression
Unsupervised Learning
Reinforcement Learning
Evolutionary Learning
This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define predictive modeling as a statistical model or machine learning model used to predict future behavior based on past behavior.
Predictive modeling and predictive analytics has often been used as synonyms. In recent years, that has been changing and predictive modeling is really as subset of analytics, which may also include descriptive and decision modeling. Of course it also encompasses the data mining and analysis that must be performed before and after.
In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)—this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
Anomaly Detection and Spark Implementation - Meetup Presentation.pptxImpetus Technologies
StreamAnalytix sponsored a meetup on “Anomaly Detection Techniques and Implementation using Apache Spark” which took place on Tuesday December 5, 2017 at Larkspur Landing Milpitas Hotel, Milpitas, CA. The meetup was led by Maxim Shkarayev, Lead Data Scientist, Impetus Technologies along with Punit Shah, Solution Architect, StreamAnalytix and Anand Venugopal, Product Head & AVP, StreamAnalytix, who introduced and summarized the vast field of Anomaly Detection and its applications in various industry problems. The speakers at the event also offered a structured approach to choose the right anomaly detection techniques based on specific use-cases and data characteristics which was followed by a demonstration of some real-world anomaly detection use-cases on Apache Spark based analytics platform.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Data Science is concerned with the analysis of large amounts of data. When the volume of data is really large, it requires the use of cooperating, distributed machines. The most popular method of doing this is Hadoop, a collection of programs to perform computations on connected machines in a cluster. Hadoop began life as an open-source implementation of MapReduce, an idea first developed and implemented by Google for its own clusters. Though Hadoop's MapReduce is Java-based, and quite complex, this talk focuses on the "streaming" facility, which allows Python programmers to use MapReduce in a clean and simple way. We will present the core ideas of MapReduce and show you how to implement a MapReduce computation using Python streaming. The presentation will also include an overview of the various components of the Hadoop "ecosystem."
NYC Data Science Academy is excited to welcome Sam Kamin who will be presenting an Introduction to Hadoop for Python Programmers a well as a discussion of MapReduce with Streaming Python.
Sam Kamin was a professor in the University of Illinois Computer Science Department. His research was in programming languages, high-performance computing, and educational technology. He taught a wide variety of courses, and served as the Director of Undergraduate Programs. He retired as Emeritus Associate Professor, and worked at Google until taking his current position as VP of Data Engineering in NYC Data Science Academy.
--------------------------------------
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
Kaggle Top1% Solution: Predicting Housing Prices in Moscow Vivian S. Zhang
This project was completed by students graduated from NYC Data Science Academy 12-week Data Science Bootcamp. Learn more about the bootcamp: http://nycdatascience.com/data-science-bootcamp/
Watch the project presentation: https://youtu.be/W530d2ZdbJE
Ranked #15 out of 3,274 teams on Kaggle Team Members - Brandy Freitas, Chase Edge and Grant Webb
Given 4 years of housing price data in a foreign market, predicting the following year’s prices should be pretty straightforward, right? But what if in that last year of data, the country’s stock market, the value of its currency and the price of its number 1 export, all dropped by nearly 50%. And on top of all that, the country was slapped with economic sanctions by the EU and the US. This was Moscow in 2014 and as you can see, it was anything but straightforward.
We were able to overcome these challenges and in the two weeks of working together, were able to achieve a top 1% ranking on Kaggle. Our success is a product of our in depth data cleaning, feature engineering and our approach to modeling. With a focus on interpretability and simplicity, we begin modeling using linear regression and decision trees which gave us a better understanding of the data. We then utilized more complicated models such as random forests and XGBoost which ultimately resulted in our top submission.
Twitter: @NycDataSci
Learn with our NYC Data Science Program (weekend courses for working professionals and 12 week full time for whom are advancing their career into Data Science)
Our next 12-Week Data Science Bootcamp starts in Jun. (Deadline to apply is May 1st, all decisions will be made by May 15th.)
====================================
Max Kuhn, Director is Nonclinical Statistics of Pfizer and also the author of Applied Predictive Modeling.
He will join us and share his experience with Data Mining with R.
Max is a nonclinical statistician who has been applying predictive models in the diagnostic and pharmaceutical industries for over 15 years. He is the author and maintainer for a number of predictive modeling packages, including: caret, C50, Cubist and AppliedPredictiveModeling. He blogs about the practice of modeling on his website at ttp://appliedpredictivemodeling.com/blog
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His Feb 18th course can be RSVP at NYC Data Science Academy.
Syllabus
Predictive Modeling using R
Description
This class will get attendees up to speed in predictive modeling using the R programming language. The goal of the course is to understand the general predictive modeling process and how it can be implemented in R. A selection of important models (e.g. tree-based models, support vector machines) will be described in an intuitive manner to illustrate the process of training and evaluating models.
Prerequisites:
Attendees should have a working knowledge of basic R data structures (e.g. data frames, factors etc) and language fundamentals such as functions and subsetting data. Understanding of the content contained in Appendix B sections B1 though B8 of Applied Predictive Modeling (free PDF from publisher [1]) should suffice.
Outline:
- An introduction to predictive modeling
- R and predictive modeling: the good and bad
- Illustrative example
- Measuring performance
- Data splitting and resampling
- Data pre-processing
- Classification trees
- Boosted trees
- Support vector machines
If time allows, the following topics will also be covered
- Parallel processing
- Comparing models
- Feature selection
- Common pitfalls
Materials:
Attendees will be provided with a copy of Applied Predictive Modeling[2] as well as course notes, code and raw data. Participants will be able to reproduce the examples described in the workshop.
Attendees should have a computer with a relatively recent version of R installed.
About the Instructor:
More about Max's work:
[1] http://rd.springer.com/content/pdf/bbm%3A978-1-4614-6849-3%2F1.pdf
[2] http://appliedpredictivemodeling.com
Hack session for NYTimes Dialect Map Visualization( developed by R Shiny)Vivian S. Zhang
Data Science Academy, Hack session, NY Times, Dialect Map, Data science by R, Vivian S. Zhang, see www.nycdatascience.com for more details. Joint work by Data Scientist team of SupStat Inc. a New York based data analytic and visualization consulting firm.
This project was completed by Scott Dobbins and Rachel Kogan, who enrolled in the NYC Data Science Academy's 12-Week Data Science Bootcamp. Learn more about the program: http://nycdatascience.com/data-science-bootcamp/
Given that both Wikipedia and comments sections of most websites are freely open to anyone to edit at any time, how has Wikipedia managed to remain such a useful resource while most comments sections are ridden with vandalism, ads, and other counterproductive user behavior?
We believe the answer is two-fold: 1) Wikipedia has an army of bots that quickly identify and revert vandalism so that the worst edits are usually never seen by people and the site generally maintains itself in a well-kempt state, and 2) Wikipedia has a strong community of administrators and other contributors who routinely clean the site’s flagged contents.
Vandalism is relatively easy to flag, though a few clever edits manage to stay on the site for a long time. What about site content problems that are more subjective, like bias? Wikipedia users do routinely manually flag pages with point-of-view (POV) issues, though with millions of pages and no machine-based approaches, the site can only manage to confidently maintain neutrality on the more well-trafficked pages.
Here we propose a solution to solve some of the more intractable content issues for Wikipedia and other sites using Natural Language Processing (NLP) and machine learning approaches. The sheer quantity of data managed by Wikipedia and similar sites requires distributed computing approaches, so we show here how Apache Spark can upgrade common algorithms to run on massive data sets.
A Hybrid Recommender with Yelp Challenge Data Vivian S. Zhang
Developed by Chao Shi, Sam O'Mullane, Sean Kickham, Reza Rad and Andrew Rubino
Watch the project presentation: https://youtu.be/gkKGnnBenyk
This project was completed by students from NYC Data Science Academy's 12-Week Bootcamp. Learn more about the bootcamp: http://nycdatascience.com/data-science-bootcamp/
People make decisions on where to eat based on friends’ recommendations. Since they know you, their suggestions matter more than those of strangers.
For the capstone project, we built a hybrid Yelp recommendation system that can provide individualized recommendations based on your friend’s reviews on the social network. We built the machine learning models using Spark, and set up a Flask-Kafka-RDS-Databricks pipeline that allows a continuous stream of user requests.
During the presentation, we will talk about the development framework and technical implementation of the pipeline.
Read on their project posts and code:
https://blog.nycdatascience.com/student-works/capstone/yelp-recommender-part-1/
https://blog.nycdatascience.com/student-works/yelp-recommender-part-2/
Using Machine Learning to aid Journalism at the New York TimesVivian S. Zhang
This talk was presented to NYC Open Data Meetup Group on Nov 11, 2014.
Speaker:
Daeil Kim is currently a data scientist at the Times and is finishing up his Ph.D at Brown University on work related to developing scalable inference algorithms for Bayesian Nonparametric models. His work at the Times spans a variety of problems related to the company's business interests, audience development, as well as developing tools to aid journalism.
Topic:
This talk will focus mostly on how machine learning can help problems that prop up in journalism. We'll begin first by talking about using popular supervised learning algorithms such as regularized Logistic Regression to help assist a journalist's work in uncovering insights into a story regarding the recall of Takata airbags in cars. Afterwards, we'll think about using topic modeling to deal with large document dumps generated from FOIA (Freedom of Information Act) requests and Refinery, a simple web based tool to ease the implementation of such tasks. Finally, if there is time, we will go over how topic models have been extended to assist in the problem of designing an efficient recommendation engine for text-based content.
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Lab 2: Classification and Regression Prediction Models, training and testing ...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Lab 2: Classification and Regression Prediction Models, training and testing splits, optimization of K Nearest Neighbors (KD tree), optimization of Random Forest, optimization of Naive Bayes (Gaussian), advantages and model comparisons, feature importance, Feature ranking with recursive feature elimination, Two dimensional Linear Discriminant Analysis
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
Nyc open-data-2015-andvanced-sklearn-expandedVivian S. Zhang
Scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners.
This talk will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines, model evaluation, parameter search, and out-of-core learning.
Apart from metrics for model evaluation, we will cover how to evaluate model complexity, and how to tune parameters with grid search, randomized parameter search, and what their trade-offs are. We will also cover out of core text feature processing via feature hashing.
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Andreas is an Assistant Research Scientist at the NYU Center for Data Science, building a group to work on open source software for data science. Previously he worked as a Machine Learning Scientist at Amazon, working on computer vision and forecasting problems. He is one of the core developers of the scikit-learn machine learning library, and maintained it for several years.
Material will be posted here:
https://github.com/amueller/pydata-nyc-advanced-sklearn
Blog:
peekaboo-vision.blogspot.com
Twitter:
https://twitter.com/t3kcit
The presentation focuses on the facilities available in Oracle 10g for SQL and database tuning, the identification of database problems using wait events, and some common configuration problems.
Data science with R - Clustering and ClassificationBrigitte Mueller
This presentation guides you through your first steps to a prediction with R. We predict flight delays using classification. I prepared and cleaned the data and split them into train and test data (github link /mbbrigitte).
The talk was held in May 2016 for Ruby programmers.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
A Comprehensive and detailed approach using Machine Learning - An international E-Commerce company wants to use some of the most advanced machine learning techniques to analyse their customers with respect to their services and some important customer success matrix. They also have future expansion plans to India.
This document list the reasons why our past alumni chose NYC Data Science Academy over other programs.
Machine Learning Bootcamp is our flagship program and well received by our community.
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
R003 laila restaurant sanitation report(NYC Data Science Academy, Data Scienc...Vivian S. Zhang
NYC Data Science Academy, Data Science by R Intensive Beginner level, R003 student, Laila, presented on restaurant sanitation report using NYC Open Data Set, see her blog post at http://nycdatascience.com/2014/05/pizza-everyone-loves-pizza/
R003 jiten south park episode popularity analysis(NYC Data Science Academy, D...Vivian S. Zhang
NYC Data Science Academy, Data Science by R Intensive Beginner level, R003 student, Jiten presented how he scrapped dataset and did south park episode popularity analysis.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2. Outline
Introduction of data mining and caret
before model training
building model
advance topic
exercise
·
·
visualization
pre-processing
Data slitting
-
-
-
·
Model training and Tuning
Model performance
variable importance
-
-
-
·
feature selection
parallel processing
-
-
·
/
4. Introduction of caret
The caret package (short for Classification And REgression Training) is a set of functions that
attempt to streamline the process for creating predictive models. The package contains tools for:
data splitting
pre-processing
feature selection
model tuning using resampling
variable importance estimation
·
·
·
·
·
/
7. visualizations
The featurePlot function is a wrapper for different lattice plots to visualize the data.
Scatterplot Matrix
boxplot
featurePlot(x=iris[,1:4],
y=iris$Species,
plot="pairs",
##Addakeyatthetop
auto.key=list(columns=3))
featurePlot(x=iris[,1:4],
y=iris$Species,
plot="box",
##Addakeyatthetop
auto.key=list(columns=3))
/
15. data splitting
create balanced splits of the data
set.seed(1)
trainIndex<-createDataPartition(iris$Species,p=0.8,list=FALSE, times=1)
head(trainIndex)
irisTrain<-iris[trainIndex,]
irisTest<-iris[-trainIndex,]
summary(irisTest$Species)
createResample can be used to make simple bootstrap samples
createFolds can be used to generate balanced cross–validation groupings from a set of data.
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16. Model Training and Parameter Tuning
The train function can be used to
evaluate, using resampling, the effect of model tuning parameters on performance
choose the "optimal" model across these parameters
estimate model performance from a training set
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20. Model Training and Parameter Tuning
define the type of resampling method
k-fold cross-validation (once or repeated)
leave-one-out cross-validation
bootstrap (simple estimation or the 632 rule)
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fitControl<-trainControl(method="repeatedcv",
#10-foldcrossvalidation
number=10,
#repeated3times
repeats=3)
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23. The trainControl Function
method: The resampling method
number and repeats: number controls with the number of folds in K-fold cross-validation or
number of resampling iterations for bootstrapping and leave-group-out cross-validation.
verboseIter: A logical for printing a training log.
returnData: A logical for saving the data into a slot called trainingData.
classProbs: a logical value determining whether class probabilities should be computed for held-
out samples during resample.
summaryFunction: a function to compute alternate performance summaries.
selectionFunction: a function to choose the optimal tuning parameters.
returnResamp: a character string containing one of the following values: "all", "final" or "none".
This specifies how much of the resampled performance measures to save.
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24. Alternate Performance Metrics
Performance Metrics:
Another built-in function, twoClassSummary, will compute the sensitivity, specificity and area under
the ROC curve
regression: RMSE and R2
classification: accuracy and Kappa
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fitControl<-trainControl(method="repeatedcv",
number=10,
repeats=3,
classProbs=TRUE,
summaryFunction=twoClassSummary)
treemodel<-train(x=trainData,
y=trainClass,
method='rpart',
trControl=fitControl,
tuneGrid=tunedf,
metric="ROC")
treemodel
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26. Evaluating Test Sets
caret also contains several functions that can be used to describe the performance of classification
models
testPred<-predict(treemodel,testData)
testPred.prob<-predict(treemodel,testData,type='prob')
postResample(testPred,testClass)
confusionMatrix(testPred,testClass)
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29. Exploring and Comparing Resampling
Distributions
Given these models, can we make statistical statements about their performance differences? To do
this, we first collect the resampling results using resamples.
We can compute the differences, then use a simple t-test to evaluate the null hypothesis that there is
no difference between models.
resamps<-resamples(list(tree=treemodel,
nnet=nnetmodel))
bwplot(resamps)
densityplot(resamps,metric='ROC')
difValues<-diff(resamps)
summary(difValues)
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30. Variable importance evaluation
Variable importance evaluation functions can be separated into two groups:
model-based approach
Model Independent approach
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For classification, ROC curve analysis is conducted on each predictor.
For regression, the relationship between each predictor and the outcome is evaluated
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#model-basedapproach
treeimp<-varImp(treemodel)
plot(treeimp)
#ModelIndependentapproach
RocImp<-varImp(treemodel,useModel=FALSE)
plot(RocImp)
#or
RocImp<-filterVarImp(x=trainData,y=trainClass)
plot(RocImp)
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31. feature selection
Many models do not necessarily use all the predictors
Feature Selection Using Search Algorithms("wrapper" approach)
Feature Selection Using Univariate Filters('filter' approach)
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33. feature selection: wrapper approach
feature selection based on random forest model
pre-defined sets of functions: linear regression(lmFuncs), random forests (rfFuncs), naive Bayes
(nbFuncs), bagged trees (treebagFuncs)
ctrl<-rfeControl(functions=rfFuncs,
method="repeatedcv",
number=10,
repeats=3,
verbose=FALSE,
returnResamp="final")
Profile<-rfe(x=trainData,
y=trainClass,
sizes=1:8,
rfeControl=ctrl)
Profile
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