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Machine Learning Tutorial | Machine Learning Algorithm | Machine Learning Engineer Program | Edureka
 
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***** Python Certification Training for Data Science : https://www.edureka.co/python ***** This Edureka video on "Machine Learning Tutorial" will help you get started with all the Machine Learning concepts. Below are the topics covered in this video: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. What can you do with Machine Learning? 5. Machine Learning Demo in Python Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #DataScience #MachineLearningTutorial #MachineLearningAlgorithm - - - - - - - - - - - - - - - - - About the Course Edureka's Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms. Edureka’s Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence. ----------------------------------------------------------- Course Objectives After completing this Data Science Certification training, you will be able to: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python for Data Science? It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger. It 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. It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free) Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 32550 edureka!
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
 
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** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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
Views: 39388 edureka!
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a dataset for model creation and validation and how you can create a model using any machine learning algorithm! In this Machine Learning Algorithms Tutorial video you will understand: 1) What is an Algorithm? 2) What is Machine Learning? 3) How is a problem solved using Machine Learning? 4) Types of Machine Learning 5) Machine Learning Algorithms 6) Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #MachineLearningAlgorithms #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole 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. - - - - - - - - - - - - - - Why Learn Data Science? 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. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 155720 edureka!
Python Machine Learning Tutorial | Machine Learning Algorithms | Python Training | Edureka
 
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( Python Training : https://www.edureka.co/python ) This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. Supervised Learning 5. KNN algorithm 6. Unsupervised Learning 7. K-means Clustering Algorithm Check out our playlist for more videos: https://goo.gl/Na1p9G Subscribe to our channel to get video updates. Hit the subscribe button above. #Python #PythonTutorial #PythonMachineLearning #PythonTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? 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 continues to be a favorite 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. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 140685 edureka!
Data Mining Fourth Edition Practical Machine Learning Tools and Techniques Morgan Kaufmann
 
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For read and download book Visit Link in the video
Views: 15 Sambessi
Senior Machine Learning Developer - Hot IT Jobs for Developers.
 
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Senior Machine Learning Developer - Hot IT Jobs for Developers. Горячие вакансии для разработчиков. Много интересных вакансий на http://jobs.zhuko.net/?ref=113 http://fb.com/zhukonet1 http://instagram.com/zhukonet http://twitter.com/zhukonet http://linkedin.com/in/lototskij You will be joining a small team, aimed at designing, developing, testing, documenting and reviewing several in-house projects. Required Skills: - B.A/M.A in the field of: Artificial Intelligence / Natural Language -- Processing (NLP) / Information Retrieval / Machine Learning - Ability to perform in-depth research with practical implementation - Java SE / C++ - Understanding of development methodology (Waterfall, Agile etc.) - Experience with issue/task tracking tools (Youtrack etc.) - Versioning (SVN, Git, etc.) - Team player and eager to learn Skills considered to be a plus: - Ph.D/Ph.D. candidate - SQL (MySQl, PostgreSQL, etc.) and database programming - Several years of commercial experience with one the above programming languages or running projects - Web crawling - Data mining We offer: - Interesting and challenging work in a dynamically developing company - Exciting projects - Professional development opportunities - Excellent compensation package - Modern and comfortable office facilities - Flexible working hours Больше о вакансии: http://jobs.zhuko.net/?id=2027&ref=113
Views: 640 Zhuko.Net
Weka classifier from Java
 
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Final proyect, using classifier on diabetes dataset. Authors: Oyervide Jonnathan & Poveda Adrian
Views: 5306 Adrian Poveda
Alexey Zinoviev - Java in production for Data Mining Research projects (Ru)
 
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Speaker: Alexey Zinoviev Topic: Java in production for Data Mining Research projects Abstract: Java is often criticized for hard parsing CSV datasets, poor matrix and vectors manipulations. This makes it hard to easy and efficiently implement certain types of machine learning algorithms. In many cases data scientists choose R or Python languages for modeling and problem solution and you as a Java developer should rewrite R algorithms in Java or integrate many small Python scripts in Java application. But why so many highload tools like Cassandra, Hadoop, Giraph, Spark are written in Java or executed on JVM? What the secret of successful implementation and running? Maybe we should forget old manufacturing approach of dividing on developers and research engineers in production projects? During the report, we will discuss how to build full Java-stack Data Mining application, deploy it, make charts, integrate with databases, how to improve performance with JVM tuning and etc. Attendees of my talk will become familiar with the development and deploy of an research Java projects, Hadoop/Spark basics and will get useful tips about possible integration ways.
Views: 164 jetconf
Java in production for Data Mining Research projects (JavaDayKiev'15)
 
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Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 318 Alexey Zinoviev
KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Training | Edureka
 
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** Python for Data Science: https://www.edureka.co/python ** This Edureka video on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this video includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Check out our playlist for more videos: http://bit.ly/2taym8X Subscribe to our channel to get video updates. Hit the subscribe button above. #KNNAlgorithm #MachineLearningUsingPython #MachineLearningTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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
Views: 27735 edureka!
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
27:05
** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? 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 continues to be a favorite 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. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 22661 edureka!
Java as a fundamental working tool of the Data Scientist (Alexey Zinoviev, Joker, 2014)
 
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Alexey Zinoviev presented this paper on the Jocker conference http://jokerconf.com/#zinoviev. Slides: http://www.slideshare.net/zaleslaw/java-for-data-scientist-zinoviev This paper covers next topics: Data Mining, Machine Learning, Mahout, Spark, MLlib, Python, Octave, R language
Views: 2401 Alexey Zinoviev
Naive Bayes Classifier in Python | Naive Bayes Algorithm | Machine Learning Algorithm | Edureka
 
30:19
** Machine Learning Training with Python: https://www.edureka.co/python ** This Edureka video will provide you with a detailed and comprehensive knowledge of Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial: 1. What is Naive Bayes? 2. Bayes Theorem and its use 3. Mathematical Working of Naive Bayes 4. Step by step Programming in Naive Bayes 5. Prediction Using Naive Bayes Check out our playlist for more videos: http://bit.ly/2taym8X Subscribe to our channel to get video updates. Hit the subscribe button above. #MachineLearningUsingPython #MachineLearningTraning How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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
Views: 17991 edureka!
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
50:19
( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole 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. - - - - - - - - - - - - - - Why Learn Data Science? 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. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 60979 edureka!
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
07:28
Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 140732 Augmented Startups
Data Science in 25 Minutes with GP Pulipaka (Ganapathi Pulipaka): Mastering TensorFlow Tutorial
 
25:15
Ganapathi Pulipaka Chief Data Scientist for AI strategy, neural network architectures, application development of Machine learning, Deep Learning algorithms, experience in applying algorithms, integrating IoT platforms, Python, PyTorch, R, JavaScript, Go Lang, and TensorFlow, Big Data, IaaS, IoT, Data Science, Blockchain, Apache Hadoop, Apache Kafka, Apache Spark, Apache Storm, Apache Flink, SQL, NoSQL, Mathematics, Data Mining, Statistical Framework, SIEM with 6+ Years of AI Research and Development Experience in AWS, Azure, and GCP. Education: PostDoc– CS, PhD in Machine Learning, AI, Big Data Analytics, Engineering and CS, Colorado Technical University, Colorado Springs PhD, Business Administration in Data Analytics, Management Information Systems and Enterprise Resource Management, California University, Irvine Design, develop, and deploy machine learning and deep learning applications to solve the real-world problems in natural language processing, speech recognition, text to speech, chatbots, and speech to text analytics. Experience in data exploration, data preparation, applying supervised and unsupervised machine learning algorithms, machine learning model training, machine learning model evaluation, predictive analytics, bio-inspired algorithms, genetic algorithms, and natural language processing. I wrote around 400 research papers, published two books as a bestselling author on Amazon "The Future of Data Science and Parallel Computing," "Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data," and with a vast number of big data tool installations, SQL, NoSQL, practical machine learning project implementations, data analytics implementations, applied mathematics and statistics for publishing with the Universities as part of academic research programs. Currently, I’m working a video course “Mastering PyTorch for Advanced Data Scientist,” to build millions of data scientists around the world for AI practice. I implemented Many projects for Fortune 100 corporations Aerospace, manufacturing, IS-AFS (Apparel footwear solutions), IS-MEDIA (Media and Entertainment), ISUCCS (Customer care services), IS-AUTOMOTIVE (Automotive), IS-Utilities, retail, high-tech, life sciences, healthcare, chemical industry, banking, and service management. Public Keynote Speaker on Robotics and artificial intelligence held on May 21-22 at Los Angeles, CA. Published eBook in November 2017 for SAP Leonardo IoT “The Digital Evolution of Supply Chain Management with SAP Leonardo,” sponsored by SAP. Published eBook in December 2017 for Change HealthCare (McKesson’s HealthCare Corporation) on Machine Learning and Artificial Intelligence for Enterprise HealthCare and Health. Building recommendation systems and applying algorithms for anomaly detection in the financial industry. Deep reinforcement learning algorithms for robotics and IoT. Applying convolutional neural networks, recurrent neural networks, and long-term short memory with deep learning techniques to solve various conundrums. Developed number of machine learning and deep learning programs applying various algorithms and published articles with architecture and practical project implementations on GitHub, medium.com, data driven investor Experience with Python, TensorFlow, Caffe, Theano, Keras, Java, and R Programming languages implementing stacked auto encoders, backpropagation, perceptron, Restricted Boltzmann machines, and Deep Belief Networks. Experience in multiple IoT platforms. Twitter: https://twitter.com/gp_pulipaka Facebook: https://www.facebook.com/ganapathipulipaka LinkedIn: https://www.linkedin.com/in/dr-ganapathi-pulipaka-56417a2
Views: 116 GP Pulipaka
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
01:04:06
( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole 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. - - - - - - - - - - - - - - Why Learn Data Science? 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. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."
Views: 43561 edureka!
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
36:36
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole 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. - - - - - - - - - - - - - - Why Learn Data Science? 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. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 59406 edureka!
Weka Data Mining Tutorial for First Time & Beginner Users
 
23:09
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 441944 Brandon Weinberg
Brian Kent: Density Based Clustering in Python
 
39:24
PyData NYC 2015 Clustering data into similar groups is a fundamental task in data science. Probability density-based clustering has several advantages over popular parametric methods like K-Means, but practical usage of density-based methods has lagged for computational reasons. I will discuss recent algorithmic advances that are making density-based clustering practical for larger datasets. Clustering data into similar groups is a fundamental task in data science applications such as exploratory data analysis, market segmentation, and outlier detection. Density-based clustering methods are based on the intuition that clusters are regions where many data points lie near each other, surrounded by regions without much data. Density-based methods typically have several important advantages over popular model-based methods like K-Means: they do not require users to know the number of clusters in advance, they recover clusters with more flexible shapes, and they automatically detect outliers. On the other hand, density-based clustering tends to be more computationally expensive than parametric methods, so density-based methods have not seen the same level of adoption by data scientists. Recent computational advances are changing this picture. I will talk about two density-based methods and how new Python implementations are making them more useful for larger datasets. DBSCAN is by far the most popular density-based clustering method. A new implementation in Dato's GraphLab Create machine learning package dramatically speeds up DBSCAN computation by taking advantage of GraphLab Create's multi-threaded architecture and using an algorithm based on the connected components of a similarity graph. The density Level Set Tree is a method first proposed theoretically by Chaudhuri and Dasgupta in 2010 as a way to represent a probability density function hierarchically, enabling users to use all density levels simultaneous, rather than choosing a specific level as with DBSCAN. The Python package DeBaCl implements a modification of this method and a tool for interactively visualizing the cluster hierarchy. Slides available here: https://speakerdeck.com/papayawarrior/density-based-clustering-in-python Notebooks: http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_dbscan.ipynb http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_DeBaCl.ipynb
Views: 13647 PyData
Practical Application of Data Mining Tool assignment 2
 
06:05
This is our Assignment 2 data mining about Weka tools.. this video show how to implement weka..
Views: 268 arelyn syaz
Java Implementation of K-Nearest Neighbors (kNN) Classifier 1/2
 
08:47
The code can be found here: www.imperial.ac.uk/people/n.sadawi Go to Tutorials and then Machine Learning section!
Views: 33096 Noureddin Sadawi
How to use WEKA software for data mining tasks
 
04:54
In this video, I'll guide you how to use WEKA software for preprocessing, classifying, clustering, association. WEKA is a collection of machine learning algorithms for performing data mining tasks. #RanjiRaj #WEKA #DataMining Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj Get WEKA from here : http://www.cs.waikato.ac.nz/ml/weka/
Views: 16526 Ranji Raj
Data Mining Tool: extra features
 
02:17
Some extra features of the Data Mining Tool. Heatmaps and Gene Set Enrichment.
Views: 59 QMRIBioinf
Automated Software Defect Prediction Using Machine Learning
 
17:40
Software code is composed of several components (e.g., several Java classes). Testing all these components can be a very expensive task. If we know which components are likely to be defective, we can concentrate testing on these components, increasing the chances of finding software defects while reducing testing effort. The task of software defect prediction is concerned with predicting which software components are likely to be defective, helping to increase testing cost-effectiveness. In this talk, I will show how software defect prediction can be performed by using automated machine learning approaches. I will also go through some important issues to be considered when using such automated approaches.
Views: 5847 Mike Bartley
Hadoop Mahout | Steps for AWS Installation powered by MIRI Infotech Inc.
 
15:48
Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification. Many of the implementations use the Apache Hadoop platform. Mahout also provides Java libraries for common maths operations (focused on linear algebra and statistics) and primitive Java collections. Miri Infotech is launching a product which will configure and publish Mahout, to produce free implementations of distributed or otherwise scalable machine learning algorithms which is embedded pre-configured tool with Ubuntu and ready-to-launch AMI on Amazon EC2 that contains Mahout, Hadoop, Scala, Sparks. Apache Mahout is an open source project that is primarily used for creating scalable machine learning algorithms. It implements popular machine learning techniques such as: Recommendation, Classification and Clustering.
Views: 201 Miri Cloud Services
CS 430   Business Intelligence and Data Mining
 
02:47
Organizations increasingly rely on business intelligence and data mining systems to provide them the relevant information and insights to support decision-making. This course covers the fundamental concepts and tools for managing and mining data to generate new insights. Topics include design and development of data warehouses; and data mining tools, including statistical and machine learning techniques, to identify new patterns. Popular data mining systems will be used to develop practical skills. (4 credits) Prerequisite: Consent of the faculty. Video by Professor Anil Maheshwari.
Views: 823 mumcompro
Using Data Mining to Predict Hospital Admissions From the Emergency Department
 
11:16
Using Data Mining to Predict Hospital Admissions From the Emergency Department -- The World Health Organization estimates that by 2030 there will be approximately 350 million young people (below 30 to 40 years) with various diseases associated with renal complications, stroke and peripheral vascular disease. Our aim is to analyze the risk factors and system conditions to detect disease early with prediction strategies. By using the effective methods to identify and extract key information that describes aspects of developing a prediction model, sample size and number of events, risk predictor selection. Crowding within emergency departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions. This system highlights the potential utility of three common machine learning algorithms in predicting patient admissions. In this proposed approach, we considered a heart disease as a main concern and we start prediction over that disease. Because in India a strategic survey on 2015-6016 resulting that every year half-a million of people suffer from various heart diseases. Practical implementation of the models developed in this paper in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM's will be useful where accuracy is paramount. Using the strategic algorithm such as Logistic Regression, Decision Trees and Gradient Boosted Machine, we can easily identify the disease with various attributes and risk factor specifications. Based on these parameters, the analysis of high risk factors of developing disease is identified using mining principles. Use of data mining algorithms will result in quick prediction of disease with high accuracy. Data mining, emergency department, hospitals, machine learning, predictive models -- For More Details, Contact Us -- Arihant Techno Solutions www.arihants.com E-Mail-ID: [email protected] Mobile: +91-75984 92789
Machine learning W10 1  Learning With Large Datasets
 
05:46
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Reference: https://class.coursera.org/ml-007
Views: 3882 Alan Saberi
Predictive Analytics, Machine Learning, and Recommendation Systems on Hadoop
 
01:05:01
Originally recorded January 30, 2014. In the world of ever growing data volumes, how do you extract insight, trends and meaning from all that data in Hadoop? Do you need help transforming your big data into big knowledge? Organizations know that the key to competitive advantage is in using advanced analytics to discover trends and use them to your advantage faster than the competition. Getting relevant information from big data requires a different approach. Churning out a couple of analytical models a week isn't going to cut it. If you're using big data to identify trends, spot weaknesses and predict outcomes, you need proven analytical software that's a lot faster, more efficient, accurate, and easy to use. Learn more about how to reveal insights in your Big data and redefine how your organization solves complex problems. You will learn how to: Use sophisticated analytics in both a visual interface and a coding interface. Prepare, explore and model multiple scenarios using data volumes never before possible to generate accurate and rapid insights. Interact with the data to add or drop variables into the model and instantly see how their influence provides increased predictive power. Easily perform modeling tasks interactively and on-the-fly Quickly understand your model fit with model diagnostics - interactively and in real time (typically in seconds instead of hours or days). Ask what-if questions on all the data. Use a scalable recommendation system to help improve customer experience through profiling users and items and finding how to relate them About Wayne Thompson Wayne Thompson is the Manager of SAS Predictive Analytics Product Management at SAS.Over the course of his 20-year tenure at SAS he has been credited with bringing to market landmark SAS analytics technologies (SAS Text Miner, SAS Credit Scoring for Enterprise Miner, SAS Model Manager, SAS Rapid Predictive Modeler, SAS Scoring Accelerator for Teradata, and SAS Analytics Accelerator for Teradata). Current focus initiatives include easy to use self-service data mining tools for business analysts, decision management and massively parallel high performance analytics. Wayne received his Ph.D. and M.S from the University of Tennessee in 1992 and 1987, respectively. During his PhD program, he was also a visiting scientist at the Institut Superieur d'Agriculture de Lille, Lille, France. Georgia Mariani is Principal Product Marketing Manager for Statistics at SAS. She drives marketing direction for SAS' statistics software initiatives. Georgia began her career at SAS as a systems engineer, consulting with sales prospects in the government and education industries regarding their analytical business questions and implementing SAS software and solutions. Georgia received her M.S. degree in Mathematics with a concentration in Statistics in 1996 and her B.S. degree in Mathematics in 1992 from the University of New Orleans. During her Master's program she was awarded a fellowship with NASA. Produced by: Yasmina Greco Don't miss an upload! Subscribe! http://goo.gl/szEauh - Stay Connected to O'Reilly Media. Visit http://oreillymedia.com Sign up to one of our newsletters - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 3340 O'Reilly
Machine Learning In Python | Python Machine Learning Tutorial | Deep Learning Python | Edureka
 
24:07
** Data Science Master's Program: https://www.edureka.co/masters-program/data-scientist-certification ** In this Edureka tutorial on "Machine Learning In Python", we will be covering all the fundamentals of Machine Learning. This is the second video in the "Python for Deep Learning" series, below are the first and third tutorials. Part 1 (Python For Deep Learning): https://www.youtube.com/watch?v=azfWrlxVxDU Part 3 (Introduction To TensorFlow): https://www.youtube.com/watch?v=uh2Fh6df7Lg Below are the topics we will cover in this live session: 1. What is Machine Learning? 2. Machine Learning Applications 3. Types Of Machine Learning 4. Use-Case Demo How it Works? 1. This is a 6 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Python Data Science course is designed to make you grab the concepts of Machine Learning. The course will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. - - - - - - - - - - - - - - - - - - - Why learn Data Science? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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
Views: 13604 edureka!
Data Mining Presentation
 
04:32
Presentation by Naveed Hussain and Ben Newman. COMP3776 - Data Mining and Text Analytics. Coursework 2.
Views: 64 Naveed Hussain
Understanding Apriori Algorithm | Apriori Algorithm Using Mahout | Edureka
 
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Watch Sample Class Recording: http://www.edureka.co/mahout?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. This video gives you a brief insight of Apriori algorithm. Related Blogs: http://www.edureka.co/blog/introduction-to-clustering-in-mahout/?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo http://www.edureka.co/blog/k-means-clustering/?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Apriori Algorithm’ have extensively been covered in our course ‘Machine Learning with Mahout’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 14375 edureka!
Web Scraping   Data Mining   Data Exctraction   Data Entry Java Custom Application
 
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Web Scraping Data Mining Data Exctraction Data Entry Java Custom Application Searches related to web scraping data mining data extraction difference between web scraping and data mining web crawler tool free difference between screen scraping and web scraping best open source web crawler web scraping vs crawling data crawling data mining from websites web scraping courses
Views: 41 Web Scraping
Bioinformatics part 2 Databases (protein and nucleotide)
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 89438 Shomu's Biology
Machine Learning Classification kNN in R (Breast Cancer)
 
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Se desarrolla un ejercicio de machine learning de clasificación en R. El algoritmo usado es el conocido como kNN k nearest neighbors y se aplica a un conjunto de datos de cáncer de mama. El ejercicio fue publicado originalmente en "Machine Learning in R" by Brett Lantz, PACKT publishing 2015. Pueden encontrar el ejercicio y la base de datos en: https://github.com/pakinja/Data-R-Value/tree/master/MachineLearning_KNN
Real Time Blockchain Concepts in Python! - Hashes and PoW
 
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Don't let the long time fool you. Give this 5 minutes and see if the advanced topics are broken down in a way that makes sense. Check out our open-source blockchain developer tool project: https://lamden.io/
Views: 14314 Lamden
A Machine Learning Approach to Software Requirements Prioritization (JAVA)
 
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Views: 317 1 Crore Projects
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1661 Audiopedia
Unity 2017 Game AI Programming, Third Edition | 2. Finite State Machines and You
 
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This is a tutorial video which shows the steps needed to be followed to run all of the code that is present in chapter 2 of the book Unity 2017 Game AI Programming, Third Edition, by Packt. Video Creator - Ray Barrera, who is also one of the Authors of the book. Unity 2017 provides game and app developers with a variety of tools to implement Artificial Intelligence. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. What You Will Learn • Understand the basic terminology and concepts in game AI • Explore advanced AI Concepts such as Neural Networks • Implement a basic finite state machine using state machine behaviors in Unity 2017 • Create sensory systems for your AI and couple it with a Finite State Machine • Wok with Unity 2017's built-in NavMesh features in your game • Build believable and highly-efficient artificial flocks and crowds • Create a basic behavior tree to drive a character's actions You can purchase this book at: Packt: https://goo.gl/kfrFfj Amazon: https://goo.gl/b9iPsE Also check out Mapt – Practical learning for experienced developers, for a free trial, click here: https://goo.gl/UhcfN4
Writing a Multistep MapReduce Job Using the mrjob Python Library: From Data Just Right LiveLessons
 
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http://www.informit.com/store/data-just-right-livelessons-video-training-9780133807141 Writing a Multistep MapReduce Job Using the mrjob Python Library is a video sample excerpt from, Data Just Right LiveLessons Video Training -- 7 Hours of Video Instruction Overview Data Just Right LiveLessons provides a practical introduction to solving common data challenges, such as managing massive datasets, visualizing data, building data pipelines and dashboards, and choosing tools for statistical analysis. You will learn how to use many of today's leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Data Just Right LiveLessons shows how to address each of today's key Big Data use cases in a cost-effective way by combining technologies in hybrid solutions. You'll find expert approaches to managing massive datasets, visualizing data, building data pipelines and dashboards, choosing tools for statistical analysis, and more. These videos demonstrate techniques using many of today's leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Data Engineer and former Googler Michael Manoochehri provides viewers with an introduction to implementing practical solutions for common data problems. The course does not assume any previous experience in large scale data analytics technology, and includes detailed, practical examples. Skill Level Beginner What You Will Learn Mastering the four guiding principles of Big Data success--and avoiding common pitfalls Emphasizing collaboration and avoiding problems with siloed data Hosting and sharing multi-terabyte datasets efficiently and economically "Building for infinity" to support rapid growth Developing a NoSQL Web app with Redis to collect crowd-sourced data Running distributed queries over massive datasets with Hadoop and Hive Building a data dashboard with Google BigQuery Exploring large datasets with advanced visualization Implementing efficient pipelines for transforming immense amounts of data Automating complex processing with Apache Pig and the Cascading Java library Applying machine learning to classify, recommend, and predict incoming information Using R to perform statistical analysis on massive datasets Building highly efficient analytics workflows with Python and Pandas Establishing sensible purchasing strategies: when to build, buy, or outsource Previewing emerging trends and convergences in scalable data technologies and the evolving role of the "Data Scientist" Who Should Take This Course Professionals who need practical solutions to common data challenges that they can implement with limited resources and time. Course Requirements Basic familiarity with SQL Some experience with a high-level programming language such as Java, JavaScript, Python, R Experience working in a command line environment http://www.informit.com/store/data-just-right-livelessons-video-training-9780133807141
Views: 8115 LiveLessons
SPMF PRODUCTIONS GR
 
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Intro Uploaded by Upload to YouTube for Windows Phone
Views: 18 stelios papalanis
Tutorial on Large Scale Distributed Data Science from Scratch with Apache Spark 2.0 & Deep Learning
 
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In the continuing big data revolution, Apache Spark’s open-source cluster computing framework has overtaken Hadoop MapReduce as the big data processing engine of choice. Spark maintains MapReduce’s linear scalability and fault tolerance, but offers two key advantages: Spark is much faster – as much as 100x faster for certain applications; and Spark is much easier to program, due to its inclusion of APIs for Python, Java, Scala, SQL and R, plus its user-friendly core data abstraction, the distributed data frame. In addition, Spark goes far beyond traditional batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming data, machine learning, and graph processing. This tutorial offers you an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. The tutorial includes discussions of algorithm design, presentation of illustrative algorithms, relevant case studies, and practical advice and experience in writing Spark programs and running Spark clusters. Part I familiarizes you with fundamental Spark concepts, including Spark Core, functional programming a la MapReduce, RDDs/data frames/datasets, the Spark Shell, Spark Streaming and online learning, Spark SQL, MLlib, and more. Part 2 gives you hands-on algorithmic design and development experience with Spark, including building algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as PageRank and shortest path, gradient descent algorithms such as support vector machines and matrix factorization, distributed parameter estimation, and deep learning. Your homegrown implementations will shed light on the internals of Spark’s MLlib libraries and on typical challenges in parallelizing machine learning algorithms. You will see examples of industrial applications and deployments of Spark.
Views: 194 Ms Jessica PEH _

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