Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
Data Science : Make Smarter Business DecisionsEdureka!
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. A Data Scientist deals with all the phases of data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages. The following blog will help you understand the significance of R Analytics training:
www.edureka.co/blog/r-training-first-step-to-become-a-data-scientist/
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
Webinar : Introduction to R Programming and Machine LearningEdureka!
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
Data Science : Make Smarter Business DecisionsEdureka!
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. A Data Scientist deals with all the phases of data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages. The following blog will help you understand the significance of R Analytics training:
www.edureka.co/blog/r-training-first-step-to-become-a-data-scientist/
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
Webinar : Introduction to R Programming and Machine LearningEdureka!
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Application of Clustering in Data Science using Real-life Examples Edureka!
Clustering data into subsets is an important task for many data science applications. It is considered as one of the most important unsupervised learning technique. Keeping this in mind, we have come with a free webinar ‘Application of Cluster in Data Science using Real-life examples.’
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...Edureka!
( Python Data Science Training : https://www.edureka.co/python )
This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn.
This video helps you to learn the below topics:
1. Need of Data Science
2. What is Data Science?
3. How Python is used for Data Science?
4. Data Manipulation in Python
5. Implement Machine Learning using Python
6. Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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LinkedIn: https://www.linkedin.com/company/edureka
EN: Statistics has never been a hacker's business.
In recent years, however, the need for "data science" has emerged.
This is the unprecedented intersection of computer science or hacking and, additionally, statistically relevant looking graphics and the so-called "Big Data".
The latter are heaps of data that are somewhat larger than they used to be.
With Perl and with Raku.
DE: Statistik war noch nie jederHackers Sache.
In den letzten Jahern wurde jedoch Bedarf für "Data Science" aufgetan.
Das ist die zuvor noch nicht dagewesene Schnittmenge aus Informatik bzw. Hacking und dazu statistisch relevant aussehenden Grafiken und den sogenannten "Big Data".
Letzteres sind Datenmengen, die etwas größer sind, als man es bisher gewohnt war.
Mit Perl und mit Raku.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
Data Science is one of the hottest career options globally right now with data scientists earning an average of 15 lacs to 18 lacs annually. This deck explains the fundamentals of Data Science, the role of a Data Scientist.
The deck also introduces the Certificate Masterclass in Data Science with Python by Spotle Learn. This course is specifically designed by the experts for the people who want to build a career in data science. This course will equip you with the fundamental knowledge and practical expertise required for data science careers through a rigorous pedagogy based on videos, live projects, interactive classes and integrated internships.
In our last paper we compared two alternate machine-learning techniques from
the Apache Mahout stable, namely: Apache Sparks’, spark-itemsimilarity, and its
counterpart Apache Hadoop’s MapReduce. We saw how Apache Spark was better
both qualitatively as well as quantitatively even for moderately sized sites.
In this paper, we look at how we can further optimize the efficiency of these runs
without compromising on quality. We determine how the two algorithms we
studied last time perform when run on all data available and when run only with
success data. In the e-commerce domain, success data is defined, as a subset of
the total data, which we heuristically believe, does not include noise.
Application of Clustering in Data Science using Real-life Examples Edureka!
Clustering data into subsets is an important task for many data science applications. It is considered as one of the most important unsupervised learning technique. Keeping this in mind, we have come with a free webinar ‘Application of Cluster in Data Science using Real-life examples.’
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...Edureka!
( Python Data Science Training : https://www.edureka.co/python )
This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn.
This video helps you to learn the below topics:
1. Need of Data Science
2. What is Data Science?
3. How Python is used for Data Science?
4. Data Manipulation in Python
5. Implement Machine Learning using Python
6. Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
EN: Statistics has never been a hacker's business.
In recent years, however, the need for "data science" has emerged.
This is the unprecedented intersection of computer science or hacking and, additionally, statistically relevant looking graphics and the so-called "Big Data".
The latter are heaps of data that are somewhat larger than they used to be.
With Perl and with Raku.
DE: Statistik war noch nie jederHackers Sache.
In den letzten Jahern wurde jedoch Bedarf für "Data Science" aufgetan.
Das ist die zuvor noch nicht dagewesene Schnittmenge aus Informatik bzw. Hacking und dazu statistisch relevant aussehenden Grafiken und den sogenannten "Big Data".
Letzteres sind Datenmengen, die etwas größer sind, als man es bisher gewohnt war.
Mit Perl und mit Raku.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
Data Science is one of the hottest career options globally right now with data scientists earning an average of 15 lacs to 18 lacs annually. This deck explains the fundamentals of Data Science, the role of a Data Scientist.
The deck also introduces the Certificate Masterclass in Data Science with Python by Spotle Learn. This course is specifically designed by the experts for the people who want to build a career in data science. This course will equip you with the fundamental knowledge and practical expertise required for data science careers through a rigorous pedagogy based on videos, live projects, interactive classes and integrated internships.
In our last paper we compared two alternate machine-learning techniques from
the Apache Mahout stable, namely: Apache Sparks’, spark-itemsimilarity, and its
counterpart Apache Hadoop’s MapReduce. We saw how Apache Spark was better
both qualitatively as well as quantitatively even for moderately sized sites.
In this paper, we look at how we can further optimize the efficiency of these runs
without compromising on quality. We determine how the two algorithms we
studied last time perform when run on all data available and when run only with
success data. In the e-commerce domain, success data is defined, as a subset of
the total data, which we heuristically believe, does not include noise.
Inteligencia artificial para android como empezarIsabel Palomar
Aprenderás los conceptos basico de deep learning y como crear tu aplicación de Android que puede detectar y etiquetar imágenes utilizando un modelo de Tensorflow Lite
Machine Learning encompasses data acquisition, transmission, retention, analysis, and reduction. The expected outgrowth of 24x7 data systems and operations centers is Knowledge Engineering and Data Intensive Analytics AKA Machine Learning. This presentation will develop and apply Machine Learning concepts to the Upstream O&G industry. Specific focus will be given to the fundamental concepts and definitions of Machine Learning along with the application of Machine Learning.
Data mining or Knowledge discovery (KDD) is
extracting unknown (hidden) and useful knowledge from data.
Data mining is widely used in many areas like retail, sales, ecommerce,
remote sensing, bioinformatics etc. Student’s
performance has become one of the most complex puzzle for
universities and colleges in recent past with the tremendous
growth. In this paper, authors deployed data mining techniques
like classification, association rule, chi-square etc. for knowledge
discovery. For this study, authors have used data set containing
Approx. 180 MCA (post graduate) students results data of 3
colleges. Study found that one can apply data mining
functionalities like Chi-square, Association rule and Lift in
Education and discover areas of improvement.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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Similar to Logistic Regression In Data Science (20)
What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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YouTube Link:https://youtu.be/CVv8zhYEjUE
Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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YouTube Link:https://youtu.be/LvgqSMlIXFs
Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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YouTube Link: https://youtu.be/xHUiYEIcY_I
** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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PGP in AI and Machine Learning (9 Months Online Program): https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka PPT on "Cognitive AI" explains cognitive computing and how it helps in making better human decisions at work. Also, it explains the differences between cognitive computing and artificial intelligence.
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Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | EdurekaEdureka!
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | EdurekaEdureka!
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. www.edureka.in/data-scienceSlide 2 www.edureka.co/r-for-analyticsSlide 2 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
Objectives
What is data mining
Stages of data mining??
What is R
What is data science??
What is needed of data scientist??
Roles and Responsibilities of a Data Scientist.
Logistic Regression
At the end of this session, you will be able to
5. www.edureka.in/data-scienceSlide 5Slide 5Slide 5 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
Cross Industry standard Process for data mining ( CRISP – DM )
Stages of Analytics / Data Mining
7. www.edureka.in/data-scienceSlide 7Slide 7Slide 7 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
Components data science
R Programming Language
8. Slide 8 www.edureka.in/data-science
Data Science: Demand Supply Gap
Big Data Analyst
Big Data Architect
Big Data Engineer
Big Data Research Analyst
Big Data Visualizer
Data Scientist
50
43
44
31
23
18
50
57
56
69
77
82
Filled job vs unfilled jobs in big data
Filled Unfilled
Vacancy/Filled(%)
Gartner Says Big Data Creates Big Jobs: 4.4 Million IT
Jobs Globally to Support Big Data By
2015http://www.gartner.com/newsroom/id/2207915
10. Slide 10 www.edureka.in/data-science
Machine Learning
We have so many algorithms for data mining which can be used to build systems that can read past data and can
generate a system that can accommodate any future data and derive useful insight from it
Machine learning focuses on the development of computer programs that can teach themselves to grow and change
when exposed to new data
11. Slide 11 www.edureka.in/data-science
Types of Learning
Supervised Learning Unsupervised Learning
1. Uses a known dataset to make
predictions.
2. The training dataset includes
input data and response values.
3. From it, the supervised learning
algorithm builds a model to make
predictions of the response
values for a new dataset.
1. Draw inferences from datasets
consisting of input data without
labeled responses.
2. Used for exploratory data analysis
to find hidden patterns or grouping
in data
3. The most common unsupervised
learning method is cluster analysis.
Machine Learning
12. Slide 12 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
• Common Machine Learning Algorithms
14. Slide 14 www.edureka.in/data-science
Logistic Regression
In statistics, logistic regression, or logit regression, or logit model is a direct probability
model
Rather than modeling this response Y directly, logistic regression models the probability
that Y belongs to a particular category
In logistic regression, we use the logistic function,
15. Slide 15 www.edureka.in/data-science
Logistic Regression
After some calculations we can get : p(X) /1−p(X) = eA+BX
The quantity p(X)/[1−p(X)] is called the odds, and can take on values between 0 and ∞.
Values of the odds close to 0 and ∞ indicate very low and very high probabilities resp.
Finally we get: log (p(X)/1-p(X)) = A + BX which is called the log-odds or logit
Logistic Regression is linear in x.
18. Slide 18 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
Maximum Likelihood Estimation (MLE)
→ MLE is a statistical method for estimating the coefficients of a model.
→ The likelihood function (L) measures the probability of observing the
particular set of dependent variable values (p1, p2, ..., pn) that occur in the
sample:
L = Prob (p1* p2* * * pn)
→ The higher the L, the higher the probability of observing the ps in the sample.
19. Slide 19 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
Maximum Likelihood Estimation (MLE)
→ MLE involves finding the coefficients (, ) that makes the log of the
likelihood function (LL < 0) as large as possible
→ Or, finds the coefficients that make -2 times the log of the likelihood function
(-2LL) as small as possible
→ The maximum likelihood estimates solve the following condition:
{Y - p(Y=1)}Xi = 0
summed over all observations, i = 1,…,n
20. www.edureka.in/pmi-acpSlide 20 www.edureka.in/data-science
Module 1
» Introduction to Data Science
Module 2
» Basic Data Manipulation using R
Module 3
» Machine Learning Techniques using R Part -1
- Clustering
- TF-IDF and Cosine Similarity
- Association Rule Mining
Module 4
» Machine Learning Techniques using R Part -2
- Supervised and Unsupervised Learning
- Decision Tree Classifier
Course Topics
Module 5
» Machine Learning Techniques using R Part -3
- Random Forest Classifier
- Naïve Bayer’s Classifier
Module 6
» Introduction to Hadoop Architecture
Module 7
» Integrating R with Hadoop
Module 8
» Mahout Introduction and Algorithm
Implementation
Module 9
» Additional Mahout Algorithms and Parallel
Processing in R
Module 10
» Project
Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
21. www.edureka.in/pmi-acp
Slide 21
Questions?
Enroll for the Complete Course at : www.edureka.in/data_science
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www.edureka.in/data_science
Please Don’t forget to fill in the survey report
Class Recording and Presentation will be available in 24 hours at:
http://www.edureka.in/blog/application-of-clustering-in-data-science-using-real-life-examples/