Search results “Classification and prediction in data mining with neural networks”

Provides steps for applying artificial neural networks to do classification and prediction.
R file: https://goo.gl/VDgcXX
Data file: https://goo.gl/D2Asm7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- neural network model
- input, hidden, and output layers
- min-max normalization
- prediction
- confusion matrix
- misclassification error
- network repetitions
- example with binary data
neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X.
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: 19609
Bharatendra Rai

Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases
Steps of Classification:
1. Model construction: Describing a set of predetermined classes
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute.
The set of tuples used for model construction is training set.
The model is represented as classification rules, decision trees, or mathematical formulae.
2. Model usage: For classifying future or unknown objects
Estimate accuracy of the model
If the accuracy is acceptable, use the model to classify new data
MLP- NN Classification Algorithm
The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer.
Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used.
The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples.
Algorithm of MLP-NN is as follows:
Step 1: Initialize input of all weights with small random numbers.
Step 2: Calculate the weight sum of the inputs.
Step 3: Calculate activation function of all hidden layer.
Step 4: Output of all layers
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E2MATRIX RESEARCH LAB

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech

Views: 60859
Udacity

Support Vector Machine (SVM) - Fun and Easy Machine Learning
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes.
So how do we decide where to draw our decision boundary?
Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class.
These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors.
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Views: 84410
Augmented Startups

Visit https://greatlearningforlife.com our learning portal for 100s of hours of similar free high quality tutorial videos on Python, R, Machine Learning, AI and other similar topics
Watch our new free Python for Data Science Beginners tutorial : https://greatlearningforlife.com/python
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.
Know More about Great Lakes Analytics Programs:
PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi
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Views: 64019
Great Learning

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

Views: 36784
Udacity

How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data.
https://github.com/Hvass-Labs/TensorFlow-Tutorials

Views: 21338
Hvass Laboratories

SSAS - Data Mining - Decision Trees, Clustering, Neural networks

Views: 845
M R Dhandhukia

Free MATLAB Trial: https://goo.gl/yXuXnS
Request a Quote: https://goo.gl/wNKDSg
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Learn more about MATLAB: https://goo.gl/8QV7ZZ
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An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment.
In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone.
We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively.
Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.

Views: 10012
MATLAB

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: 429366
Victor Lavrenko

A quick tutorial on analysing data in Orange using Classification.

Views: 35982
haikel5

We show how to perform artificial neural network prediction using Visual Gene Developer, a free software.
In this tutorial, neural network is trained to learn a complicated function like y = Sin(x) + Abs(y)*Cos(z)
Caption included. Please turn on caption

Views: 78313
Visual Gene Developer

Making predictions with classification tree and logistic regression.
Train data set:
http://tinyurl.com/fruits-and-vegetables-train
Test data set:
http://tinyurl.com/test-fruits-and-vegetables
License: GNU GPL + CC
Music by: http://www.bensound.com/
Website: http://orange.biolab.si/
Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana

Views: 49406
Orange Data Mining

This vlog introduces you to decision tree in R and how categorical data can be classified and predicted by this algorithm.

Views: 1340
Keshav Singh

In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python.
Coding Challenge for this video:
https://github.com/llSourcell/How_to_do_Sentiment_Analysis
Ludo's winning code:
https://github.com/ludobouan/pure-numpy-feedfowardNN
See Jie Xun's runner up code:
https://github.com/jiexunsee/Neural-Network-with-Python
Tutorial on setting up an AMI using AWS:
http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/
More learning resources:
http://deeplearning.net/tutorial/lstm.html
https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis
https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll
http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html
https://www.kaggle.com/c/word2vec-nlp-tutorial
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If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part.
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Views: 128835
Siraj Raval

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 introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 24616
MIT OpenCourseWare

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

Views: 27147
Well Academy

Here I will explain Neural networks in R for Machine learning working,how to fit a machine learning model like neural network in R,plotting neural network for machine learning in R,predictions using neural network in R.neuralnet package is used for this modelling.Also I have described the basic Machine learning modelling procedure in R.Its a neural network tutorial for Machine Learning .

Views: 53657
Data Science by Arpan Gupta IIT,Roorkee

Using R and the multinom function from the { nnet } package we can easily predict discrete / factors of more than 2 levels. With the help of Repeated Cross Validation, we understand the importance of allowing models to converge (reaching global minima). Code and walkthrough: http://amunategui.github.io/multinomial-neuralnetworks-walkthrough/
Support these videos, check out my in-depth classes on Udemy.com (discounts and specials) at
http://amunategui.github.io/udemy/
Note: forgot to mention that the 'maxiter' variable defaults to 100 when omitted, so start with a large number the first around.

Views: 14626
Manuel Amunategui

This video is going to talk about how to apply neural network in R for classification problem.
1. Standardize/Scaling the original data before you apply the algorithm to speed up the process and better convergence
2. Choose number of hidden layers, nodes and other arguments.
3. Convert prediction back to original format.
4. Create confusion matrix and calculate classification error.
Thanks for watching. My website: http://allenkei.weebly.com
If you like this video please "Like", "Subscribe", and "Share" it with your friends to show your support! If there is something you'd like to see or you have question about it, feel free to let me know in the comment section. I will respond and make a new video shortly for you. Your comments are greatly appreciated.

Views: 892
Allen Kei

Provides steps for applying deep learning for developing multilayer perceptron Neural Network for multiclass softmax classification.
R file: https://goo.gl/n5Nyvb
Data: https://goo.gl/MYgpLX
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- installing keras package
- read data
- matrix conversion
- normalize
- data partition
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
Deep learning with neural networks is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X.
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: 10524
Bharatendra Rai

Analysis Of Neural Networks in Data Mining
by,
Venkatraam Balasubramanian
Master's in Industrial and Human Factor Engineering

Views: 3969
prasana sarma

Simple introduction video on how to run neural networks and random forests in weka.

Views: 10516
Gaurav Jetley

If you want to learn how to start using pre-trained models and eventually want to build your own ML models, then you will want to watch this episode of #AskTensorFlow. Google Developer Advocates, Magnus Hyttsten and Laurence Moroney answer questions directly from the TensorFlow Community. Learn about the benefits to using pre-trained models when starting out, regression tools to make predictions, and managing categorical input using TensorFlow.
Neural Networks for Drawing Classification → https://goo.gl/JfCdgp
TensorFlow Medium Blog → https://goo.gl/eL6iyv
TensorFlow Website → https://goo.gl/ri3xbn
Have a question for us? Ask us on Twitter! → https://goo.gl/fguA5f
Watch more #AskTensorFlow → https://goo.gl/3AwMKZ
Subscribe to TensorFlow channel to catch all the latest → https://goo.gl/ht3WGe

Views: 4588
TensorFlow

Includes an example with,
- brief definition of what is svm?
- svm classification model
- svm classification plot
- interpretation
- tuning or hyperparameter optimization
- best model selection
- confusion matrix
- misclassification rate
Machine Learning videos: https://goo.gl/WHHqWP
svm is an important machine learning tool related to analyzing big data or working in data science field.
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: 28881
Bharatendra Rai

This tutorial shows how to construct a predictive model using IBM SPSS Modeler. We use the Boston Housing dataset for our illustration. In addition, we also discuss how to evaluate the performance of the model using different nodes such as Graph Evaluation and Data Analysis Node. I hope you enjoy it and please let me know if you have any questions. Thanks for watching.

Views: 15313
IT_CHANNEL

With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool!
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification, is the focus of our series.
Classification involves taking a set of objects and some data features that describe them, and placing them into categories. This is done by a classifier which takes the data features as input and assigns a value (typically between 0 and 1) to each object; this is called firing or activation; a high score means one class and a low score means another. There are many different types of classifiers such as Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes. If you have used any of these tools before, which one is your favorite? Please comment.
Neural nets are highly structured networks, and have three kinds of layers - an input, an output, and so called hidden layers, which refer to any layers between the input and the output layers. Each node (also called a neuron) in the hidden and output layers has a classifier. The input neurons first receive the data features of the object. After processing the data, they send their output to the first hidden layer. The hidden layer processes this output and sends the results to the next hidden layer. This continues until the data reaches the final output layer, where the output value determines the object's classification. This entire process is known as Forward Propagation, or Forward prop. The scores at the output layer determine which class a set of inputs belongs to.
Links:
Michael Nielsen's book - http://neuralnetworksanddeeplearning.com/
Andrew Ng Machine Learning - https://www.coursera.org/learn/machine-learning
Andrew Ng Deep Learning - https://www.coursera.org/specializations/deep-learning
Have you worked with neural nets before? If not, is this clear so far? Please comment.
Neural nets are sometimes called a Multilayer Perceptron or MLP. This is a little confusing since the perceptron refers to one of the original neural networks, which had limited activation capabilities. However, the term has stuck - your typical vanilla neural net is referred to as an MLP.
Before a neuron fires its output to the next neuron in the network, it must first process the input. To do so, it performs a basic calculation with the input and two other numbers, referred to as the weight and the bias. These two numbers are changed as the neural network is trained on a set of test samples. If the accuracy is low, the weight and bias numbers are tweaked slightly until the accuracy slowly improves. Once the neural network is properly trained, its accuracy can be as high as 95%.
Credits:
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Views: 364755
DeepLearning.TV

The main concept of this Data Mining project is to forecast the Closing prices of the stock market based on the past data sets.
Note: Watch with Sub-titles :)

Views: 677
Dvs Teja

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Views: 39193
DeepLearning.TV

We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power.
Coding challenge for this video:
https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo
Vishal's winning code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier
Jie's runner up code:
https://github.com/jiexunsee/Simple-Inception-Transfer-Learning
More Learning Resources:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
http://deeplearning.net/tutorial/lstm.html
https://deeplearning4j.org/lstm.html
https://www.tensorflow.org/tutorials/recurrent
http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/
Please subscribe! And like. And comment. That's what keeps me going.
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Views: 408640
Siraj Raval

In this tutorial of “Matlab implementation of disease prediction using data mining techniques.” I have shown that how a disease can be predicted by artificial intelligence system.
To make system able to predict disease we first create the database with all accurate report from ratio of level of different substance in the body by which any disease spread in whole body. When we give input data to the system then find how much ratio is in that individual body and for which disease it matches. Then system predict that which disease you have and if no match found with database then it shows you do not have any disease.

Views: 4417
Fly High with AI

Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R
First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class.
The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself?
What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers!
Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response.
In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation.
Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression!
Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function `lm()` does this for you in R.
Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades.
All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression.
Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar.
You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are.
Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters.
You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.

Views: 32046
DataCamp

In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set.
The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share

Views: 453748
Thales Sehn Körting

Can we predict the price of Microsoft stock using Machine Learning? We'll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out!
Code for this video:
https://github.com/llSourcell/Stock_Market_Prediction
Please Subscribe! And like. And comment. That's what keeps me going.
Follow me:
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More learning resources:
https://www.quantinsti.com/blog/machine-learning-trading-predict-stock-prices-regression/
https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02
https://iknowfirst.com/rsar-machine-learning-trading-stock-market-and-chaos
https://www.udacity.com/course/machine-learning-for-trading--ud501
https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets
https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets
http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.linkedin.com/pulse/deep-learning-stock-price-prediction-explained-joe-ellsworth
If you're wondering why my voice sounds weird, it's because i was down with Traveler's Diarrhea from my recent trip to India. It's such a debilitating sickness, but the show must go on. And yes, thankfully I'm better now :)
Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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Views: 69947
Siraj Raval

Support Vector Machines Video (Part 1): http://youtu.be/LXGaYVXkGtg
Support Vector Machine (SVM) Part 2: Non Linear SVM http://youtu.be/6cJoCCn4wuU
Other Videos on Neural Networks:
http://scholastic.teachable.com/p/pattern-classification
Part 2: http://youtu.be/K5HWN5oF4lQ (Multi-layer Perceptrons)
Part 3: http://youtu.be/I2I5ztVfUSE (Backpropagation)
More video Books at: http://scholastictutors.webs.com/
Here we explain how to train a single layer perceptron model using some given parameters and then use the model to classify an unknown input (two class liner classification using Neural Networks)

Views: 137490
homevideotutor

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

Views: 130410
Well Academy

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).
2. Participate in coding contests(https://goo.gl/plOmbn)
3. Participate in hackathons(https://goo.gl/btD3D2)
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Views: 51326
HackerEarth

Source: neuralnet: Training of Neural Network by Frauke Gunther and Stefan Fritsch - The R Journal Vol. 2/1, June 2010

Views: 76449
Hamed Hasheminia

Training on Neural Networks for Classification Data by Vamsidhar Ambatipudi

Views: 57
Vamsidhar Ambatipudi

Provides steps for applying Image classification & recognition using CNN with easy to follow example. CNN is considered 'gold standard' for large scale image classification.
R file: https://goo.gl/trgsuH
Data: https://goo.gl/JmEjmc
Machine Learning videos: https://goo.gl/WHHqWP
Uses TensorFlow (by Google) as backend for CNN and includes,
- Advantages
- layers
- parameter calculations
- load keras and EBImage packages
- read images
- explore images and image data
- resize and reshape images
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
large scale Image Classification & Recognition using cnn with Keras is an important tool related to analyzing big data or working in data science field.
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: 5945
Bharatendra Rai

Description
This presentation will demonstrate Matthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build Deep Neural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep Learning Library.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Views: 3409
PyData

In this video we create and explore a variety of predictive models, including classification and regression trees, Bootstrap Forests, Boosted Trees, neural networks, and penalized regression models (Lasso and Elastic Net). We discuss modeling for unbalanced responses, and show how to change the cutoff for model classification. We also see how to use the Formula Depot to compare competing models, select the best model, and write scoring code (C, Python, SAS,…) for model deployment. A quick introduction to mining unstructured text data with the Text Explorer platform is also provided.

Views: 1637
Mia Stephens

This work build a model from 5 years data. Data is divided into classes based on general weathers like "Begining of Summer", Summer, Start of Rainfall, Mansoon, End of Rainfall, Begining of Winter, Winter Rainfall and so on. This is performed automatically using Fuzzy clustering technique ( not part of Video).
It then trains NN and SVM with weather properties like Temperature, Humidity, Rainfall etc with the classes.
When a specific year and day of the year is given as input for weather prediction, the system finds out the exact class and then aggregates the value of that class as predictive value.
It also builds a decision support system to check if on specific week, there is a possibility of plant disease or not which can be used as precautionary measure for pesticides and so on.

Views: 21793
rupam rupam

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.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
https://twitter.com/bigdatau
https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 49034
Cognitive Class

Provides steps for applying Image classification & recognition with easy to follow example.
R file: https://goo.gl/fCYm19
Data: https://goo.gl/To15db
Machine Learning videos: https://goo.gl/WHHqWP
Uses TensorFlow (by Google) as backend. Includes,
- load keras and EBImage packages
- read images
- explore images and image data
- resize and reshape images
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field.
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: 10022
Bharatendra Rai

What is a neural network, neural network terminology, and setting up a network for time series forecasting
This video supports the textbook Practical Time Series Forecasting.
http://www.forecastingbook.com
http://www.galitshmueli.com

Views: 11530
Galit Shmueli

Linear regression for predictive modeling. Part 2 of 2
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining.
It is part of a series of 37 videos, all of which are available on YouTube.
For more information:
http://www.dataminingbook.com
https://www.twitter.com/gshmueli
https://www.facebook.com/dataminingbook
Here is the complete list of the videos:
• Welcome to Business Analytics Using Data Mining (BADM)
• BADM 1.1: Data Mining Applications
• BADM 1.2: Data Mining in a Nutshell
• BADM 1.3: The Holdout Set
• BADM 2.1: Data Visualization
• BADM 2.2: Data Preparation
• BADM 3.1: PCA Part 1
• BADM 3.2: PCA Part 2
• BADM 3.3: Dimension Reduction Approaches
• BADM 4.1: Linear Regression for Descriptive Modeling Part 1
• BADM 4.2 Linear Regression for Descriptive Modeling Part 2
• BADM 4.3 Linear Regression for Prediction Part 1
• BADM 4.4 Linear Regression for Prediction Part 2
• BADM 5.1 Clustering Examples
• BADM 5.2 Hierarchical Clustering Part 1
• BADM 5.3 Hierarchical Clustering Part 2
• BADM 5.4 K-Means Clustering
• BADM 6.1 Classification Goals
• BADM 6.2 Classification Performance Part 1: The Naive Rule
• BADM 6.3 Classification Performance Part 2
• BADM 6.4 Classification Performance Part 3
• BADM 7.1 K-Nearest Neighbors
• BADM 7.2 Naive Bayes
• BADM 8.1 Classification and Regression Trees Part 1
• BADM 8.2 Classification and Regression Trees Part 2
• BADM 8.3 Classification and Regression Trees Part 3
• BADM 9.1 Logistic Regression for Profiling
• BADM 9.2 Logistic Regression for Classification
• BADM 10 Multi-Class Classification
• BADM 11 Ensembles
• BADM 12.1 Association Rules Part 1
• BADM 12.2 Association Rules Part 2
• Neural Networks: Part I
• Neural Networks: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

Views: 288
Galit Shmueli

Linear Regression - Machine Learning Fun and Easy
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
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
To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out
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Views: 82634
Augmented Startups

© 2018 It62 cryptography and network security question bank

Not the Internet. Also quite handy if you get a bit thirsty at night. Existential Dread. BCC Bitcoin Cash. GAME Game Credits. BTG Bitcoin Gold. XIN Infinity Economics. BAT Basic Attention Token. BitBay - Bitcoin Embassy Amsterdam, NL Zeedijk 43A, 1012 AR. BitBay - Katowice, PL ul. Kepowa 45, 40-583. BitBay - Malta, MLT 35 Strait Street, Valletta VLT 1434. BitBay - New Delhi, IN Off-405, DLF Tower A, Jasola. Table of Contents. Introduction to Bitcoin as an Investment. To know whether Bitcoin is a good investment, you need to understand a little about what Bitcoin is. Investing in something you know nothing about is always a bad idea. So, always do plenty of research and talk to a financial advisor before investing. The Bitcoin Investment. As I mentioned above, Bitcoin has proved to be one of the best investments in the past 10 years. Although many people have doubted Bitcoin, it has continued