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Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Data Science |Simplilearn
 
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This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 51552 Simplilearn
Working with Time Series Data in MATLAB
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 56832 MATLAB
Grabbing a Dataset
 
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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.
Naive Bayes: A Final Note on Naive Bayesian Model
 
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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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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 ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ 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: 213214 Augmented Startups
Naive Bayes: Building a Model with Categorical Data
 
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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.
An Introduction to Classification
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Develop predictive models for classifying data. For more videos, visit http://www.mathworks.com/products/statistics/examples.html
Views: 13707 MATLAB
Mastering Machine Learning with MATLAB : Feature Selection | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2E4TL6x]. Selection of features is necessary to create a functional model so as to achieve a reduction in cardinality, imposing a limit greater than the number of features that must be considered during its creation. Feature selection is based on finding a subset of the original variables, usually iteratively, thus detecting new combinations of variables and comparing prediction errors. • Learn the basics of stepwise regression • Explore stepwise regression in MATLAB For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 3759 Packt Video
SD IEEE Dotnet 03 Criminals and crime hotspot detection using data mining algorithms
 
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We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw ieee projects, ieee java projects , ieee dotnet projects, ieee android projects, ieee matlab projects, ieee embedded projects,ieee robotics projects,ieee ece projects, ieee power electronics projects, ieee mtech projects, ieee btech projects, ieee be projects,ieee cse projects, ieee eee projects,ieee it projects, ieee mech projects ,ieee e&I projects, ieee IC projects, ieee VLSI projects, ieee front end projects, ieee back end projects , ieee cloud computing projects, ieee system and circuits projects, ieee data mining projects, ieee image processing projects, ieee matlab projects, ieee simulink projects, matlab projects, vlsi project, PHD projects,ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects,ieee B tech projects,ieee ns2 projects,ieee ns3 projects,ieee networking projects,ieee omnet++ projects,ieee hfss antenna projects,ieee ADS antenna projects,ieee LABVIEW projects,ieee bigdata projects,ieee hadoop projects,ieee network security projects. ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects, download IEEE PROJECTS,ieee B tech projects,ieee 2015 projects. Image Processing ieee projects with source code,VLSI projects source code,ieee online projects.best projects center in Chennai, best projects center in trichy, best projects center in bangalore,ieee abstract, project source code, documentation ,ppt ,UML Diagrams,Online Demo and Training Sessions., Engineering Project Consultancy, IEEE Projects for M.Tech, IEEE Projects for BE,IEEE Software Projects, IEEE Projects in Bangalore, IEEE Projects Diploma, IEEE Embedded Projects, IEEE NS2 Projects, IEEE Cloud Computing Projects, Image Processing Projects, Project Consultants in Bangalore, Project Management Consultants, Electrical Consultants, Project Report Consultants, Project Consultants For Electronics, College Project Consultants, Project Consultants For MCA, Education Consultants For PHD, Microsoft Project Consultants, Project Consultants For M Phil, Consultants Renewable Energy Project, Engineering Project Consultants, Project Consultants For M.Tech, BE Project Education Consultants, Engineering Consultants, Mechanical Engineering Project Consultants, Computer Software Project Management Consultants, Project Consultants For Electrical, Project Report Science, Project Consultants For Computer, ME Project Education Consultants, Computer Programming Consultants, Project Consultants For Bsc, Computer Consultants, Mechanical Consultants, BCA live projects institutes in Bangalore, B.Tech live projects institutes in Bangalore,MCA Live Final Year Projects Institutes in Bangalore,M.Tech Final Year Projects Institutes in Bangalore,B.E Final Year Projects Institutes in Bangalore , M.E Final Year Projects Institutes in Bangalore,Live Projects,Academic Projects, IEEE Projects, Final year Diploma, B.E, M.Tech,M.S BCA, MCA Do it yourself projects, project assistance with project report and PPT, Real time projects, Academic project guidance Bengaluru, projects at Bangalore, Bangalore at projects,Vlsi projects at Bangalore, Matlab projects at Bangalore, power electronics projects at Bangalore,ns2 projects at Bangalore,ns3 project at Bangalore, Engineering Project Consultants bangalore, Engineering projects jobs Bangalore, Academic Project Guidance for Electronics, Free Synopsis, Latest project synopsiss ,recent ieee projects ,recent engineering projects ,innovative projects. image processing projects, ieee matlab ldpc projects, ieee matlab DCT and DWT projects, ieee matlab Data hiding projects, ieee matlab steganography projects, ieee matlab 2D,3D projects, ieee matlab face detection projects, ieee matlab iris recognition projects, ieee matlab motion detection projects, ieee matlab image denoising projects, ieee matlab finger recognition projects, ieee matlab segmentation projects, ieee matlab preprocessing projects, ieee matlab biomedical projects.
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** 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: 47381 edureka!
Best Laptop for Machine Learning
 
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What kind of laptop should you get if you want to do machine learning? There are a lot of options out there and in this video i'll describe the components of an ideal laptop for ML. I'll also mention the ideal desktop, DIY machine, and cloud option. We'll discuss how RAM, GPUs, CPUs, motherboards, hard drives, and other components affect training and inference time. This video was not sponsored. Only a few days left to signup for my dapps course! https://www.theschool.ai Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Helpful resources: https://lambdal.com/raw-configurator?product=quad https://www.nvidia.com/en-us/geforce/products/10series/laptops/ https://www.google.com/chromebook/device/acer-chromebook-11/ https://medium.com/yanda/building-your-own-deep-learning-dream-machine-4f02ccdb0460 https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 267943 Siraj Raval
22.Images Segmentation Using K-Means Clustering in Matlab with Source code
 
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#SubScribeOurChannel k means clustering example #KMeanscClustering #ImagesSegmentation Subscribe Our Channel:https://www.youtube.com/c/ProgrammingTech676 HI welcome to programming tech in this tutorial we learn how to image segmentation using k-mean. computer vision tools Detect a tumor in brain using k-mean. Image segmentation is the classification of an image into different groups. Many researches have been done in the area of image segmentation using clustering. ... K -means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. Source Code:https://programmingtech6.blogspot.com/p/k-mean-segmenation.html 3.Matlab Basic Tutorial Command Window Base Coding and Function. https://youtu.be/YHPULfu2ai0 4.Matlab Basic Tutorial video About Vector function and how use Matrix operation. https://youtu.be/i5sSbfgI3ow 5.Matlab Basic Tutorial video About Matrix Function and Operation. https://youtu.be/4XZG2RNhrcA 6.How to Connect Mobile Camera And Webcam with MATLAB/Laptop https://youtu.be/7th54GDufuY #7.How to plot a Graph in Matlab and Read Image show using Subploting Concept. https://youtu.be/IVFWeWzZjEw #8.How to Browse Images From Drive & HOW to apply Histogram/Equalize Histogram on Image In Matlab https://youtu.be/ZO6LVdoF4M8 #10. [Point Processing #2].How Power Law Transformation Implement On Image Using Matlab. https://youtu.be/3lw3snlDIoY #11 [POINT PROCESSING#3] How to Convert An Image To Negative Image Using For Loop in Matlab https://youtu.be/MDlKCh_e-WU #12:Extraction of Bit Planes and Merging of Bit Plane Slicing in Matlab code https://youtu.be/TSWYzxZX8EE #13.How to Install Toolboxes in Matlab and Why some Toolboxes are Not install. https://youtu.be/VYGHArawk5s 14.How to Detect Edges Using Sobel and Canny Edge Filters in Matlab. And Comparison Between Two. https://youtu.be/L6F8DgmV8Io #15 How to Detect Edges of an Image using Laplacian Filters in Matlab https://youtu.be/7aNi1m1uXxc #16 How Image Sharpening using Laplacian Filter | Matlab Code https://youtu.be/2t54KkjnV90 18. Matlab code For Smoothing filter in Digital image processing using Neighborhood https://youtu.be/1uXazxD-NaI FOR MORE Matlab Tutorial click on below link : Subscribe Our Channel:https://www.youtube.com/c/ProgrammingTech676 IF YOU like VIDEO PLEASE share video comment and SUBSCRIBE CHANEL to get latest video TUTORIAL notification. Thank you Tags: Matlab Tutorials Matlab Basic Tutorial Matlab advanced Tutorials Matlab Beginner Tutorial Digital image Processing Tutorial Filters Tutorials Mathworks Tutorials Programming Tutorial Thanks
Views: 7206 Programming Tech
Brian Kent: Density Based Clustering in Python
 
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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: 16689 PyData
How do I select features for Machine Learning?
 
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Selecting the "best" features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select? In this video, I'll discuss 7 feature selection tactics used by the pros that you can apply to your own model. At the end, I'll give you my top 3 tips for effective feature selection. WANT TO JOIN MY NEXT WEBCAST? Become a member ($5/month): https://www.patreon.com/dataschool === RELATED RESOURCES === Dimensionality reduction presentation: https://www.youtube.com/watch?v=ioXKxulmwVQ Feature selection in scikit-learn: http://scikit-learn.org/stable/modules/feature_selection.html Sequential Feature Selector from mlxtend: http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/ == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 16877 Data School
Heart Disease Prediction Project
 
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Get this project kit at http://nevonprojects.com/heart-disease-prediction-project/ System allows user to predict heart disease by users symptoms using data mining
Views: 34280 Nevon Projects
PageRank Algorithm - Example
 
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✅ Algorithms and Data Structures Masterclass: http://bit.ly/algorithms-masterclass-java ✅ FREE Java Programming Course: http://bit.ly/first-steps-java ✅ FREE Top Programming Interview Questions: http://bit.ly/top-programming-intervi... ✅ Full Numerical Methods Course: http://bit.ly/numerical-methods-java ✅ Find more: https://www.globalsoftwaresupport.com/ ===================================================== In this course we are going to consider the most relevant numerical methods that are being used on a daily basis. We'll implement the algorithms in Java ✘ matrix operations ✘ how to calculate the inverse of a matrix (Gauss-elimination) ✘ numerical integration ✘ solving differential equations ✘ Euler's method and Runge-Kutta method ===================================================== ✅ Instagram: https://www.instagram.com/global.software.algorithms/ ✅ Facebook: https://www.facebook.com/Global-Software-Support-2420513901306285/
Views: 81520 Balazs Holczer
Why use MATLAB for Machine Learning?
 
03:14
Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.
K-Fold Cross Validation - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 173587 Udacity
Support Vector Machine Tutorial Using R | SVM Algorithm Explained | Data Science Training | Edureka
 
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** Data Science Certification using R: https://www.edureka.co/data-science ** This session is dedicated to how SVM works, the various features of SVM and how it used in the real world. The following topics will be covered today: (01:15) Introduction to machine learning ((04:15) What is Support Vector Machine (SVM)? (06:19) How does SVM work? (09:35) Non-linear SVM (11:20) SVM Use case (12:43) Hands-On Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 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 - - - - - - - - - - - - - - - - - #svmalgorithm #svmwithr #svmclassifier #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling 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. Analyze 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. Analyze 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 online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 8556 edureka!
Genetic Algorithm in MATLAB
 
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Genetic Algorithm in MATLAB using Optimization Toolbox. I discussed an example from MATLAB help to illustrate how to use ga-Genetic Algorithm in Optimization Toolbox window and from the command line in MATLAB program. Amr Abdelnaser 3rd Year (C&E)EE Dept. Sohag University, Sohag, Egypt
Views: 136739 Amr Abdelbari
Properties of Support Vector Machines (SVM) Learned Model in MATLAB
 
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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.
Project PRC: Naive Bayes Classifier 2018 in MATLAB®
 
03:40
*best with earphone and 1080HD. JCB30903 - PATTERN RECOGNITION AND CLASSIFICATION - Meer - Nik - Izhar - Hasiff - Qas MATLAB coding: clc clear %# SUCCESS data x1.facility = xlsread('database pattern.xlsx','Sheet1','D4:D13'); x1.quality = xlsread('database pattern.xlsx','Sheet1','E4:E13'); x1.workmanship = xlsread('database pattern.xlsx','Sheet1','F4:F13'); %# FAIL data x2.facility = xlsread('database pattern.xlsx','Sheet1','D14:D23'); x2.quality = xlsread('database pattern.xlsx','Sheet1','E14:E23'); x2.workmanship = xlsread('database pattern.xlsx','Sheet1','F14:F23'); %% declare XF1=x1.facility; XQ1=x1.quality; XW1=x1.workmanship; XF2=x2.facility; XQ2=x2.quality; XW2=x2.workmanship; n=10; %% calculate means meansXF1 = sum(XF1)/n; meansXQ1 = sum(XQ1)/n; meansXW1 = sum(XW1)/n; meansXF2 = sum(XF2)/n; meansXQ2 = sum(XQ2)/n; meansXW2 = sum(XW2)/n; %% construct covariance matrix %# covariance SUCCESS a1 = XF1-meansXF1; b1 = XQ1-meansXQ1; c1 = XW1-meansXW1; A1 = a1.*a1; B1 = a1.*b1; C1 = a1.*c1; D1 = b1.*a1; E1 = b1.*b1; F1 = b1.*c1; G1 = c1.*a1; H1 = c1.*b1; I1 = c1.*c1; U1 = sum(A1)/(n-1); V1 = sum(E1)/(n-1); W1 = sum(I1)/(n-1); X1 = sum(B1)/(n-1); Y1 = sum(C1)/(n-1); Z1 = sum(F1)/(n-1); covSUCCESS = [U1 X1 Y1;X1 V1 Z1;Y1 Z1 W1]; %# covariance FAIL a2 = XF2-meansXF2; b2 = XQ2-meansXQ2; c2 = XW2-meansXW2; A2 = a2.*a2; B2 = a2.*b2; C2 = a2.*c2; D2 = b2.*a2; E2 = b2.*b2; F2 = b2.*c2; G2 = c2.*a2; H2 = c2.*b2; I2 = c2.*c2; U2 = sum(A2)/(n-1); V2 = sum(E2)/(n-1); W2 = sum(I2)/(n-1); X2 = sum(B2)/(n-1); Y2 = sum(C2)/(n-1); Z2 = sum(F2)/(n-1); covFAIL = [U2 X2 Y2;X2 V2 Z2;Y2 Z2 W2]; %% calculate determinant % SUCCESS determinant detSUCCESS = det(covSUCCESS); % FAIL determinant detFAIL = det(covFAIL); %% calculate inverse covariance % SUCCESS inverse covariance incovSUCCESS = inv(covSUCCESS); % FAIL inverse covariance incovFAIL = inv(covFAIL); %% command for new data a = input('Please enter new Facility of Logistic: '); b = input('Please enter new Quality of Material: '); c = input('Please enter new Level of Workmanship: '); %% generalize equations % SUCCESS equation T1 = [a-meansXF1;b-meansXQ1;c-meansXW1]'*(incovSUCCESS*[a-meansXF1;b-meansXQ1;c-meansXW1]); K1 = (1/sqrt(detSUCCESS*(2*pi)^3))*exp(-1/2*T1); % FAIL equation T2 = [a-meansXF2;b-meansXQ2;c-meansXW2]'*(incovFAIL*[a-meansXF2;b-meansXQ2;c-meansXW2]); K2 = (1/sqrt(detFAIL*(2*pi)^3))*exp(-1/2*T2); %% calculate accuracy %SUCCESS accuracy accuracySUCCESS = K1/ (K1+K2)*100; %FAIL accuracy accuracyFAIL = K2/ (K1+K2)*100; %% Decision if accuracySUCCESS>accuracyFAIL fprintf('\nThe new data is belong to SUCCESS.\n') elseif accuracySUCCESS<accuracyFAIL fprintf('\nThe new data is belong to FAIL.\n') else fprintf('\nThe new data is UNKNOWN\n') end
Views: 700 MEER
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn
 
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This KNN Algorithm tutorial (K-Nearest Neighbor Classification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works. Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial: 1. Why do we need KNN? 2. What is KNN? 3. How do we choose the factor 'K'? 4. When do we use KNN? 5. How does KNN algorithm work? 6. Use case - Predict whether a person will have diabetes or not To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/XP6xcp Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 63982 Simplilearn
Clustering (4): Gaussian Mixture Models and EM
 
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Gaussian mixture models for clustering, including the Expectation Maximization (EM) algorithm for learning their parameters.
Views: 101404 Alexander Ihler
Data Mining Projects in Java | Data Mining Thesis in Java | Data Mining Code Projects in Java
 
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Contact Best Matlab Simulation Projects Visit us: http://matlabsimulation.com/
Views: 484 matlab simulation
Pulmonary Tuberculosis Diagnosis Data Mining Projects
 
03:48
Contact Best Matlab Simulation Projects Visit us: http://matlabsimulation.com/
Views: 43 matlab simulation
10 Myths About Data Science | Uncovering Data Science Myths | Data Science Training | Edureka
 
22:46
** Data Scientist Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka live session on “10 Data Science Myths" attempts to take down some of the misconceptions about Data Science and gives a much clearer picture of what data science really is. 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 ------------------------------------- Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV 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 Slideshare: https://www.slideshare.net/EdurekaIN/ #edureka #edurekadatascience #datascientist #datasciencemyths #top10datasciencemyths -------------------------------------- How it Works? 1. This is a 30-hour Instructor-led Online Course. 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 Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. ------------------------------------- Who should go for this course? The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'R' professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies ------------------------------------- Why learn Data Science? Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modeling, statistics, and analytics. To take complete benefit of these opportunities, you need structured training with an updated curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges. ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 3330 edureka!
How to train neural Network in Matlab ??
 
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This tutorial video teaches about training a neural network in Matlab .....( Download Matlab Code Here: http://www.jcbrolabs.org/matlab-codes)
Views: 70343 sachin sharma
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 21411 Nevon Projects
SD IEEE Dotnet 09 A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
 
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We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw ieee projects, ieee java projects , ieee dotnet projects, ieee android projects, ieee matlab projects, ieee embedded projects,ieee robotics projects,ieee ece projects, ieee power electronics projects, ieee mtech projects, ieee btech projects, ieee be projects,ieee cse projects, ieee eee projects,ieee it projects, ieee mech projects ,ieee e&I projects, ieee IC projects, ieee VLSI projects, ieee front end projects, ieee back end projects , ieee cloud computing projects, ieee system and circuits projects, ieee data mining projects, ieee image processing projects, ieee matlab projects, ieee simulink projects, matlab projects, vlsi project, PHD projects,ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects,ieee B tech projects,ieee ns2 projects,ieee ns3 projects,ieee networking projects,ieee omnet++ projects,ieee hfss antenna projects,ieee ADS antenna projects,ieee LABVIEW projects,ieee bigdata projects,ieee hadoop projects,ieee network security projects. ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects, download IEEE PROJECTS,ieee B tech projects,ieee 2015 projects. Image Processing ieee projects with source code,VLSI projects source code,ieee online projects.best projects center in Chennai, best projects center in trichy, best projects center in bangalore,ieee abstract, project source code, documentation ,ppt ,UML Diagrams,Online Demo and Training Sessions., Engineering Project Consultancy, IEEE Projects for M.Tech, IEEE Projects for BE,IEEE Software Projects, IEEE Projects in Bangalore, IEEE Projects Diploma, IEEE Embedded Projects, IEEE NS2 Projects, IEEE Cloud Computing Projects, Image Processing Projects, Project Consultants in Bangalore, Project Management Consultants, Electrical Consultants, Project Report Consultants, Project Consultants For Electronics, College Project Consultants, Project Consultants For MCA, Education Consultants For PHD, Microsoft Project Consultants, Project Consultants For M Phil, Consultants Renewable Energy Project, Engineering Project Consultants, Project Consultants For M.Tech, BE Project Education Consultants, Engineering Consultants, Mechanical Engineering Project Consultants, Computer Software Project Management Consultants, Project Consultants For Electrical, Project Report Science, Project Consultants For Computer, ME Project Education Consultants, Computer Programming Consultants, Project Consultants For Bsc, Computer Consultants, Mechanical Consultants, BCA live projects institutes in Bangalore, B.Tech live projects institutes in Bangalore,MCA Live Final Year Projects Institutes in Bangalore,M.Tech Final Year Projects Institutes in Bangalore,B.E Final Year Projects Institutes in Bangalore , M.E Final Year Projects Institutes in Bangalore,Live Projects,Academic Projects, IEEE Projects, Final year Diploma, B.E, M.Tech,M.S BCA, MCA Do it yourself projects, project assistance with project report and PPT, Real time projects, Academic project guidance Bengaluru, projects at Bangalore, Bangalore at projects,Vlsi projects at Bangalore, Matlab projects at Bangalore, power electronics projects at Bangalore,ns2 projects at Bangalore,ns3 project at Bangalore, Engineering Project Consultants bangalore, Engineering projects jobs Bangalore, Academic Project Guidance for Electronics, Free Synopsis, Latest project synopsiss ,recent ieee projects ,recent engineering projects ,innovative projects. image processing projects, ieee matlab ldpc projects, ieee matlab DCT and DWT projects, ieee matlab Data hiding projects, ieee matlab steganography projects, ieee matlab 2D,3D projects, ieee matlab face detection projects, ieee matlab iris recognition projects, ieee matlab motion detection projects, ieee matlab image denoising projects, ieee matlab finger recognition projects, ieee matlab segmentation projects, ieee matlab preprocessing projects, ieee matlab biomedical projects.
Euclidean Distance - Practical Machine Learning Tutorial with Python p.15
 
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In the previous tutorial, we covered how to use the K Nearest Neighbors algorithm via Scikit-Learn to achieve 95% accuracy in predicting benign vs malignant tumors based on tumor attributes. Now, we're going to dig into how K Nearest Neighbors works so we have a full understanding of the algorithm itself, to better understand when it will and wont work for us. We will come back to our breast cancer dataset, using it on our custom-made K Nearest Neighbors algorithm and compare it to Scikit-Learn's, but we're going to start off with some very simple data first. K Nearest Neighbors boils down to proximity, not by group, but by individual points. Thus, all this algorithm is actually doing is computing distance between points, and then picking the most popular class of the top K classes of points nearest to it. There are various ways to compute distance on a plane, many of which you can use here, but the most accepted version is Euclidean Distance, named after Euclid, a famous mathematician who is popularly referred to as the father of Geometry, and he definitely wrote the book (The Elements) on it. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 96140 sentdex
Automatic Time Table Generation Using Genetic Algorithm
 
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Title: Automatic Time Table Generation Using Genetic Algorithm Domain: Data Mining Key Features: 1. Generation of time table using genetic algorithm. 2. Time table generation separately for teacher and students. 3. Downloadable in .xls file 4. Facility of curd model for teacher and students, etc. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2016 – 2017 data mining projects 5. 2016 – 2017 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2016 – 2017 ieee titles 8. 2016 – 2017 base paper 9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2016 – 2017 data mining weka projects 13. 2016 – 2017 b.e projects 14. 2016 – 2017 m.e projects 15. 2016 – 2017 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2016 – 2017 ieee base paper free download 23. 2016 – 2017 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 15137 InnovationAdsOfIndia
PageRank Algorithm - Matrix Representation
 
06:52
✅ Algorithms and Data Structures Masterclass: http://bit.ly/algorithms-masterclass-java ✅ FREE Java Programming Course: http://bit.ly/first-steps-java ✅ FREE Top Programming Interview Questions: http://bit.ly/top-programming-intervi... ✅ Full Numerical Methods Course: http://bit.ly/numerical-methods-java ✅ Find more: https://www.globalsoftwaresupport.com/ ===================================================== In this course we are going to consider the most relevant numerical methods that are being used on a daily basis. We'll implement the algorithms in Java ✘ matrix operations ✘ how to calculate the inverse of a matrix (Gauss-elimination) ✘ numerical integration ✘ solving differential equations ✘ Euler's method and Runge-Kutta method ===================================================== ✅ Instagram: https://www.instagram.com/global.software.algorithms/ ✅ Facebook: https://www.facebook.com/Global-Software-Support-2420513901306285/
Views: 21983 Balazs Holczer
K-Means Clustering - The Math of Intelligence (Week 3)
 
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Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this. Code for this video: https://github.com/llSourcell/k_means_clustering Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 107574 Siraj Raval
Data Mining Project Ideas for Students | Data Mining Thesis Ideas for Students
 
02:06
Contact Best Matlab Code Projects Visit us: http://matlab-code.org/
Views: 498 MATLAB PROJECTS
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 293563 CS Dojo
Regression Learner App in Matlab  (machine learning) with prediction
 
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Choose between various algorithms to train and validate regression models. After training multiple models, compare their validation errors side-by-side, and then choose the best model. To help you decide which algorithm to use, see Train Regression Models in Regression Learner App.
Views: 5002 Anselm Griffin
Multi-Class Classifier (One-Vs-All)
 
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Explains the One-Vs-All (Multi class classifier) with example. It also demonstrates the entire classification system by using dataset available at "UCI Machine Learning repository". The source code of the system (used in the demonstration, is available at my website: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/one-vs-all-multi-class-classification)
Views: 16092 Dr. Niraj Kumar
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 230072 Google Developers
Lecture 59 — Hierarchical Clustering | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Feature Selection - Forward, Backward, Stepwise & Genetic Algorithm
 
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Links GitHub: https://github.com/jk6653284/python_feature_and_model MatLab: https://www.youtube.com/watch?v=1i8muvzZkPw
Views: 1228 Louis Rampignon
Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 102555 Siraj Raval
Wine Quality Recognition Matlab code
 
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Wine Taste Preferences Prediction http://www.advancedsourcecode.com/winequaldem.zip Once viewed as a luxury good, nowadays wine is increasingly enjoyed by a wider range of consumers. To support its growth, the wine industry is investing in new technologies for both wine making and selling processes. Wine certification and quality assessment are key elements within this context. Certification prevents the illegal adulteration of wines (to safeguard human health) and assures quality for the wine market. Quality evaluation is often part of the certification process and can be used to improve wine making (by identifying the most influential factors) and to stratify wines such as premium brands (useful for setting prices). We have developed a fast and reliable approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. Wine database includes two datasets, related to red and white wine samples. Input variables based on physicochemical tests are: Fixed acidity Volatile acidity Citric acid Residual sugar Chlorides Free sulfur dioxide Total sulfur dioxide Density pH Sulphates Alcohol The output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Using the leave-one-out cross-validation methodology we have obtained a Mean Absolute Error (MAE) equal to 0.3926 for white wines and 0.4084 for red wines. P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236. Index Terms: Matlab, source, code, wine, quality, recognition, modelling, preferences, physicochemical, properties. URL: http://www.advancedsourcecode.com/wine.asp
Views: 1188 advancedsourcecode
Momentum in ANN
 
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MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. Although MATLAB is intended primarily for numerical computing, but by optional toolboxes, using the MuPAD symbolic engine, has access to symbolic computing capabilities too. One of these toolboxes is Neural Network toolbox. This toolbox is free, open source software for simulating models of brain and central nervous system, based on MATLAB computational platform. In these courses you will learn the general principles of Neural Network Toolbox designed in Matlab and you will be able to use this Toolbox efficiently as well. The list of contents is: Introduction – in this chapter the Neural Network Toolbox is Defined and introduced. An overview of neural network application is provided and the neural network training process for pattern recognition, function fitting and clustering data in demonstrated. Neuron models – A description of the neuron model is provided, including simple neurons, transfer functions, and vector inputs and single and multiple layers neurons are explained. The format of input data structures is very effective in the simulation results of both static and dynamic networks. So this effect is discussed in this chapter too. And finally the incremental and batch training rule is explained. Perceptron networks – In this chapter the perceptron architecture is shown and it is explained how to create a perceptron in Neural network toolbox. The perceptron learning rule and its training algorithm is discussed and finally the network/Data manager GUI is explained. Linear filters – in this chapter linear networks and linear system design function is discussed. The tapped delay lines and linear filters are discussed and at the end of the chapter LMS algorithm and linear classification algorithm used for linear filters are explained. Backpropagation networks – The architecture, simulation, and several high-performance backpropagation training algorithms of backpropagation networks are discussed in this chapter. Conclusion – in this chapter the memory and speed of different backpropagation training algorithms are illustrated. And at the end of the chapter all these algorithms are compared to help you select the best training algorithm for your problem in hand. Matlab Software Installation: You are required to install the Matlab Software on your machine, so you can start executing the codes, and examples we work during the course.
Signal Smoothing
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn how to smooth your signal using a moving average filter and Savitzky-Golay filter using Signal Processing Toolbox™. For more on Signal Processing Toolbox, visit: http://www.mathworks.com/products/signal/ Signal Processing Toolbox™ provides industry-standard algorithms and apps for analog and digital signal processing (DSP). You can use the toolbox to visualize signals in time and frequency domains, compute FFTs for spectral analysis, design FIR and IIR filters, and implement convolution, modulation, resampling, and other signal processing techniques. Algorithms in the toolbox can be used as a basis for developing custom algorithms for audio and speech processing, instrumentation, and baseband wireless communications.
Views: 44708 MATLAB
Algorithms Lecture 1 -- Introduction to asymptotic notations
 
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In this video big-oh, big-omega and theta are discussed
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 398735 APMonitor.com
Simple Data Mining Projects | Simple Data Mining Thesis | Simple Data Mining Code Projects
 
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Contact Best Matlab Projects Visit us: https://matlabprojects.org/
Views: 32 Matlab Projects
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - 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 ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ 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: 238647 Augmented Startups