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GOOD NEWS FOR COMPUTER ENGINEERS
INTRODUCING
5 MINUTES ENGINEERING
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SUBJECT :-
Artificial Intelligence(AI)
Database Management System(DBMS)
Software Modeling and Designing(SMD)
Software Engineering and Project Planning(SEPM)
Data mining and Warehouse(DMW)
Data analytics(DA)
Mobile Communication(MC)
Computer networks(CN)
High performance Computing(HPC)
Operating system
System programming (SPOS)
Web technology(WT)
Internet of things(IOT)
Design and analysis of algorithm(DAA)
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EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES.
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THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES.
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YOU JUST NEED TO DO
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Views: 17906
5 Minutes Engineering

📚📚📚📚📚📚📚📚
GOOD NEWS FOR COMPUTER ENGINEERS
INTRODUCING
5 MINUTES ENGINEERING
🎓🎓🎓🎓🎓🎓🎓🎓
SUBJECT :-
Discrete Mathematics (DM)
Theory Of Computation (TOC)
Artificial Intelligence(AI)
Database Management System(DBMS)
Software Modeling and Designing(SMD)
Software Engineering and Project Planning(SEPM)
Data mining and Warehouse(DMW)
Data analytics(DA)
Mobile Communication(MC)
Computer networks(CN)
High performance Computing(HPC)
Operating system
System programming (SPOS)
Web technology(WT)
Internet of things(IOT)
Design and analysis of algorithm(DAA)
💡💡💡💡💡💡💡💡
EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES.
💡💡💡💡💡💡💡💡
THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES.
🙏🙏🙏🙏🙏🙏🙏🙏
YOU JUST NEED TO DO
3 MAGICAL THINGS
LIKE
SHARE
&
SUBSCRIBE
TO MY YOUTUBE CHANNEL
5 MINUTES ENGINEERING
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Views: 13765
5 Minutes Engineering

#kmean datawarehouse #datamining #lastmomenttuitions
Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in DWM
To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/
Buy the Notes
https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/
if you have any query email us at
[email protected]
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 351223
Last moment tuitions

Hamming distance in two strings is the number of mismatches at the same position.

Views: 19367
Vivekanand Khyade - Algorithm Every Day

[http://bit.ly/overfit] Training error is something we can always compute for a (supervised) learning algorithm. But what we want is the error on the future (unseen) data. We define the generalization error as the expected error of all possible data that could come in the future. We cannot compute it, but can approximate it with error computed over a testing set.

Views: 4828
Victor Lavrenko

In this video we have explain Back propagation concept used in machine learning
visit our website for full course
www.lastmomenttuitions.com
Ml full notes rupees 200 only
ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1
Machine learning introduction : https://goo.gl/wGvnLg
Machine learning #2 : https://goo.gl/ZFhAHd
Machine learning #3 : https://goo.gl/rZ4v1f
Linear Regression in Machine Learning : https://goo.gl/7fDLbA
Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM
decision tree : https://goo.gl/Gdmbsa
K mean clustering algorithm : https://goo.gl/zNLnW5
Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8
Apriori Algorithm : https://goo.gl/hGw3bY
Naive bayes classifier : https://goo.gl/JKa8o2

Views: 44549
Last moment tuitions

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: 159235
Udacity

Take the Full Course of Artificial Intelligence
What we Provide
1) 28 Videos (Index is given down)
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in Artificial Intelligence
Sample Notes : https://goo.gl/aZtqjh
To buy the course click
https://goo.gl/H5QdDU
if you have any query related to buying the course feel free to email us : [email protected]
Other free Courses Available :
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Artificial Intelligence Index
1)Agent and Peas Description
2)Types of agent
3)Learning Agent
4)Breadth first search
5)Depth first search
6)Iterative depth first search
7)Hill climbing
8)Min max
9)Alpha beta pruning
10)A* sums
11)Genetic Algorithm
12)Genetic Algorithm MAXONE Example
13)Propsotional Logic
14)PL to CNF basics
15) First order logic solved Example
16)Resolution tree sum part 1
17)Resolution tree Sum part 2
18)Decision tree( ID3)
19)Expert system
20) WUMPUS World
21)Natural Language Processing
22) Bayesian belief Network toothache and Cavity sum
23) Supervised and Unsupervised Learning
24) Hill Climbing Algorithm
26) Heuristic Function (Block world + 8 puzzle )
27) Partial Order Planing
28) GBFS Solved Example

Views: 223528
Last moment tuitions

In the bayesian classification
The final ans doesn't matter in the calculation
Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result.
-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

Views: 164866
Well Academy

Full lecture: http://bit.ly/D-Tree
A decision tree can always classify the training data perfectly (unless there are duplicate examples with different class labels). In the process of doing this, the tree might over-fit to the peculiarities of the training data, and will not do well on the future data (test set). We avoid overfitting by pruning the decision tree.

Views: 106882
Victor Lavrenko

This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501

Views: 83150
Udacity

understanding how the input flows to the output in back propagation neural network with the calculation of values in the network.
the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example

Views: 118746
Naveen Kumar

Support Vector Machine (SVM) - Fun and Easy Machine Learning
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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.
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Views: 171894
Augmented Startups

Back propagation algorithm is used for error detection and correction in Neural Network.

Views: 13082
Rudra Singh

-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

Views: 180265
Well Academy

Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.
Code for this video:
https://github.com/llSourcell/loss_functions_explained
Please Subscribe! And like. And comment. That's what keeps me going.
Want more education? Connect with me here:
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This video is apart of my Machine Learning Journey course:
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More Learning Resources:
http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2
https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
https://blog.algorithmia.com/introduction-to-loss-functions/
http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/
https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words
http://rohanvarma.me/Loss-Functions/
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Views: 37186
Siraj Raval

Full lecture: http://bit.ly/D-Tree
A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.

Views: 506788
Victor Lavrenko

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
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#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
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Views: 45800
Simplilearn

Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).
Machine Learning and Predictive Analytics. #MachineLearning
Generalization (Algorithms) is 4th in this machine learning course. This video explains an algorithm's ability to generalize beyond data that we have available. This allows the algorithm to choose the best model even if we are lacking historical data to fully represent reality. Consider also generalization as a measurement of how well an algorithm is able to predict an entity's target feature value even though we do not have historical data to match such entity.
This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1
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Views: 4607
Caleb Curry

K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi)
Data Warehouse and Data Mining Lectures in Hindi

Views: 46904
Easy Engineering Classes

Full lecture: http://bit.ly/D-Tree
Which attribute do we select at each step of the ID3 algorithm? The attribute that results in the most pure subsets. We can measure purity of a subset as the entropy (degree of uncertainty) about the class within the subset.

Views: 178366
Victor Lavrenko

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses clustering.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 84461
MIT OpenCourseWare

The problem of over fitting can be addressed using pruning method. The process of adjusting decision tree to minimize classification error is pruning. Using a sample data set in the lab exercise, the method of pruning to overcome the problem of over fitting is explained in detail. Watch the video for more information!
Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science"
Our "Machine Learning" course is now available on Udemy
https://www.udemy.com/machine-learning-made-easy-beginner-to-advance-using-r/
Part 1 – Introduction to R Programming.
This is the part where you will learn basic of R programming and familiarize yourself with R environment. Be able to import, export, explore, clean and prepare the data for advance modeling. Understand the underlying statistics of data and how to report/document the insights.
Part 2 – Machine Learning using R
Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it. Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
Data science YouTube playlist.
https://www.youtube.com/statinferanalytics
Facebook link:-
(Visit our facebook page we are sharing data science videos)
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Views: 3009
Statinfer Analytics

Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly.
This video will talk about below:
1. Machine Learning Classification
2. Naive Bayes Theorem
About us: HackerEarth is building the largest hub of programmers to help them practice and improve their programming skills.
At HackerEarth, programmers:
1. Solve problems on Algorithms, DS, ML etc(https://goo.gl/6G4NjT).
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Views: 91371
HackerEarth

In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.

Views: 307673
Thales Sehn Körting

Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 3: Classification by regression
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/augc8F
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 27814
WekaMOOC

How KNN algorithm works with example: K - Nearest Neighbor, Classifiers, Data Mining, Knowledge Discovery, Data Analytics

Views: 125588
shreyans jain

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

Views: 413961
Thales Sehn Körting

Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 5: Pruning decision trees
http://weka.waikato.ac.nz/
Slides (PDF):
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 38214
WekaMOOC

This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501

Views: 26602
Udacity

#ArtificialNeuralNetwork | Beginners guide to how artificial neural network model works. Learn how neural network approaches the problem, why and how the process works in ANN, various ways errors can be used in creating machine learning models and ways to optimise the learning process.
- Watch our new free Python for Data Science Beginners tutorial: https://greatlearningforlife.com/python
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#ANN #MachineLearning #DataMining #NeuralNetwork
About Great Learning:
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- Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM
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Views: 68709
Great Learning

This is not my work! Please give credits to the original author:
https://vimeo.com/110060516
To calculate means from cluster centers:
For example, if a cluster contains three data points such as {32,65}, {16,87} and {17,60}, the mean of this cluster is (32+16+17)/3 and (65+87+60)/3.

Views: 154879
Iulita

Views: 32046
Machine Learning- Sudeshna Sarkar

This video provides an introduction to the technique of bootstrap resampling, which is a computational method of measuring the error in a statistic's estimator.

Views: 102965
Nick Hand

Linear Regression - Machine Learning Fun and Easy
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Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
Dependent Variable – Variable who’s values we want to explain or forecast
Independent or explanatory Variable that Explains the other variable. Values are independent.
Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents.
And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways
Used for 2 Applications
To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables-
• To see how increase in sin tax has an effect on how many cigarettes packs are consumed
• Sleep hours vs test scores
• Experience vs Salary
• Pokemon vs Urban Density
• House floor area vs House price
Forecast new observations – Can use what we know to forecast unobserved values
Here are some other examples of ways that linear regression can be applied.
• So say the sales of ROI of Fidget spinners over time.
• Stock price over time
• Predict price of Bitcoin over time.
Linear Regression is also known as the line of best fit
The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x
You most likely learnt this in school.
So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis.
M is your slope or gradient, if you change this, then your line rotates along the intercept.
Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression
Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series.
So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e
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Augmented Startups

Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Linear Regression - Least Squares Criterion. In this video I just give a quick overview of linear regression and what the 'least square criterion' actually means. In the second video, I will actually use my data points to find the linear regression / model.

Views: 440633
patrickJMT

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
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Cognitive Class

In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting.
Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform.
This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl
For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
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InfoQ

A simple methodology for soft cost-sensitive classification
KDD 2012
Te-Kang Jan
Da-Wei Wang
Chi-Hung Lin
Hsuan-Tien Lin
Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.

Views: 2
Research in Science and Technology

Subject: Geology
Paper: Remote sensing and GIS and GPS
Module: Classification accuracy assessment and errors
Content Writer: Dr. Manika Gupta

Views: 3400
Vidya-mitra

Provides steps for applying random forest to do classification and prediction.
R code file: https://goo.gl/AP3LeZ
Data: https://goo.gl/C9emgB
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- random forest model
- why and when it is used
- benefits & steps
- number of trees, ntree
- number of variables tried at each step, mtry
- data partitioning
- prediction and confusion matrix
- accuracy and sensitivity
- randomForest & caret packages
- bootstrap samples and out of bag (oob) error
- oob error rate
- tune random forest using mtry
- no. of nodes for the trees in the forest
- variable importance
- mean decrease accuracy & gini
- variables used
- partial dependence plot
- extract single tree from the forest
- multi-dimensional scaling plot of proximity matrix
- detailed example with cardiotocographic or ctg data
random forest is an important tool related to analyzing big data or working in data science field.
Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 58531
Bharatendra Rai

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5 Minutes Engineering

Provides easy to apply example of eXtreme Gradient Boosting XGBoost Algorithm with R .
Data: https://goo.gl/VoHhyh
R file: https://goo.gl/qFPsmi
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- Packages needed and data
- Partition data
- Creating matrix and One-Hot Encoding for Factor variables
- Parameters
- eXtreme Gradient Boosting Model
- Training & test error plot
- Feature importance plot
- Prediction & confusion matrix for test data
- Booster parameters
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 20596
Bharatendra Rai

This is additional material for Advanced Data Mining Class of WILP Students. It addresses pruning in GSP.

Views: 7218
Kamlesh Tiwari

Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 2: Training and testing
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/D3ZVf8
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 74133
WekaMOOC

How does a Decision Tree Work?
A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class)
and OMG wow! I'm SHOCKED how easy it was .. No wonder others going crazy sharing this??? Share it with your other friends too!
Code for visualising a decision tree -
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#decisiontree #Gini #machinelearning

Views: 23505
Bhavesh Bhatt