### 10 Algorithms Machine Learning Engineers Need to Know

Want to know the most commonly used algorithms which can be applied to any data issue? Here's the list.

If you have an Engineering detection and filtering techniques background (Neyman Pearson detectors and Bay's Detectors), please message me.

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Project for Mohamed A. -- 18/10/10 15:33:48
20 hours left

...predictive model to predict the outcome of a Kickstarter project before it is uploaded. As it is a classification problem I am looking to implement algorithms like knn, naive **Bayes** and j48. A chunk of work has already been done (that is the algorithms have been written) and I am looking for someone to clear the road blocks and finish the project. If you

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...predictive model to predict the outcome of a Kickstarter project before it is uploaded. As it is a classification problem I am looking to implement algorithms like knn, naive **Bayes** and j48. Most of the work has already been done and I am looking for someone to clear the road blocks and finish the project.
Please reach out to me if you are interested.

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...from all the categories, group the testing file together.
Method 1: use naive **bayes** Binary classifier.
Method2: use naive **bayes** Binary classifier on each category to determine where the URI is positive (using binary approach to simulate the multiway).
Method3: use Naive **Bayes** Multiway classifier.
For each method show using tables the following:
1-(ca

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...in xls format i want to compute the conditional probabilities using **Bayes** theorem. Compute 10 cases of your choice. Results in microsoft word 2003 or microsoft word 2007 .Use Equation Editor.
look at the picture attached, to have an idea !
theory : [login to view URL]
Attribute Information:
Attribute Information:
------------------------

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My project is a java code for semi-supervised learning technique. I want to add a new classifier to WEKA for classification task using co-training and naive **bayes** classifier.

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Hi i need someone I want to add a new classifier to WEKA platform for classification task using Co-Training approach and Naive **Bayes** classifier.

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...machine learning models in R --
Dataset : "Statlog (Heart) Data Set" from [login to view URL]
4 different algorithms :
1. Multilayer Perceptron Network
2. The Naïve **Bayes** classifier
3. Classification and Regression Trees (CART).
4. k-Nearest Neighbors (kNN).
A. For every algorithm use 10-fold crossvalidation to estimate accuracy.
B. Visualize

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Sentiment Analysis
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...g)
- Sentiment analysis approaches (Machine learning and Lexicon-based. please note that I'm using Machine learning supervisor approach: SVM (Support Vector Machines), Naïve **Bayes** Classifiers and Random Forest Classifier. so you have to show also why this approach used in the work at the end) - please use charts, tables and etc to explain the approaches

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Looking for an expert in R language for a project, needed to be completed as withing 2 days, the...MIT-BIH Database available on Kaggle,
this is a small project, will take someone like you 1-2 days, I will give you the link to the dataset, U need to apply KNN, SVM, Naives **Bayes**, Random Forest, and finally a comparison of accuracy between these models

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To implement Naive **bayes**, Decision trees, svm, k-means and Gaussian mixture models algorithms on 2 datasets without using inbuilt functions in R language and finally calculate accuracy, sensitivity, precision for those prediction algorithms.

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I require a Java command line program that can perform text classification (binary) using Naive **Bayes** (maybe include TF-IDF if possible).
This is very similar to a Spam Filter (i.e. Ham or Spam). Here are several examples
[login to view URL]
[login to view URL]

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...that I have a pretty big database(~15k), and I can't just use something like regex simple algorithms(cause sometimes Cheese could be written as "Ch.")
My thoughts are going to **Bayes** or Neural algorithms, but my Knowledge of Programming isn't enough
The data mainly on Russian, sometimes some English
_____________________________________________________________________

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Machine Learning
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I would like to do SVM, naive **bayes** and KNN using some tagged data. Data set is very small. R or python preferred.

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...expert in NLP and machine learning algorithm. I need some one to develop a system which d-identifies patient names, place names, dates, gender, Age. This should be done using **Bayes** Classifier and training with N-gram statistical language model. Generation of keywords, then clustering keywords into semantically related clusters namely: Symptoms, Diagnosis

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I need you to develop some software for me. I would like this software to be developed for Windows using Java. ...developed for Windows using Java. I need a Windows application that helps companies in doing financial statement comparison and financial forecasting for the future using **Bayes** algorithm.
More details will be given to chosen freelancer.

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I have written a bit of simple python code in Jupyter Notebook to grab tweets and classify their sentiment... Have a bug in the code somewhere + my SVM Classifier is classifying all test data as positive. For an experienced person in the field, this will barely take 15mins. All I need is for you to point out where the bug, and pointers to fix it.

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classification : NN, K-NN, SVM, MLP, **Bayes**, K-mean
Clustering : K-mean , fuzzy K-mean
Feature section : PCA, LDA,ICA
or other techniques on Machine Learning

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Sort Twitter Profiles
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General Objetive
Sort twitter profiles using political ideology, with Naïve **Bayes**
Specific objectives
Obtain information from twitter profiles using the NodeJS application deployed in Azure (Web App), preventing the Twitter API from censoring the information or delivering it incomplete.
Clean information, identify profiles of Ecuador and, as much as

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Hi,
I want someone who can create Web Crawler and Do Sentiment Analysis using Naive **Bayes** Algorithm as i have very short time must be done within 12 hours and also generate report of that analysis

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You need knowledge in machine learning/k-means clustering/ Naive **Bayes**/and must know how to use Jupyter notebook. Please only apply to job if you have excellent English language skills. I will provide more details about job.

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We need a Natural Language Classifier which will work in as many different languages as possible which can be used to detect spam messages in Push n...machine learning algorithm do you feel could be a viable candidate for this project (used to weed out those who don't have Natural Language Understanding experience). Hint: Naive **Bayes** is a poor choice.

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I'm looking for someone to help work on a Machine learning algorithm using naive **bayes** to help classify a set of data float values from 0-1

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Very easy project for someone well versed in machine learning should not take more than an hour. You need to implement a simple Naive **Bayes** classifier using the Bernoulli model. No preexisting libraries are to be used except NumPy which may be used where needed. Data set is provided.
Please contact me as soon as possible if you fit the bill.

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to develop a working model of a naive **bayes** classifier and then train it with a dataset so that when the user send the input of what he wants to find the he should get a reply

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Naive **bayes** classifier in Java. More details to be provided.

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I need a freelancer to help me do this.

$10 - $30

Do some data entry
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I have a data analysis task. Analyze any dataset in r using niave **bayes**, knn, glm, lm, trees, etc and analyze which algorthm worked the best. Thats all

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to implement Naïve **Bayes** Classifier in Matlab and apply it on the aforesaid problems for classification

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Word Sense Disambiguation (WSD) is the task of id...given a context. Table 1 shows an example for the word "pen".
Naive **Bayes** (NB) classiers is one of the best methods for supervised approach for WSD.
We try to choose correct sense of a word (e.g. "pen") in this assignmen by using Naive **Bayes**
Classier. (You must implement the Nave **Bayes** Classier)

$30 - $250

Naive Bayes Classier2
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Word Sense Disambiguation (WSD) is the task of id...given a context. Table 1 shows an example for the word "pen".
Naive **Bayes** (NB) classiers is one of the best methods for supervised approach for WSD.
We try to choose correct sense of a word (e.g. "pen") in this assignmen by using Naive **Bayes**
Classier. (You must implement the Nave **Bayes** Classier)

$50
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Naive **Bayes** (NB) methods have been used for classication task. For word sense disambigua-
tion, the context in which an ambiguous word occurs is represented by a vector of feature
variables F = (f1; f2; ; fn) and the sense of the ambiguous word is represented by classi-
cation variables (s1; s2; ; sk). Choosing the right sense of the

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Word Sense Disambiguation (WSD) is the task of id...given a context. Table 1 shows an example for the word "pen".
Naive **Bayes** (NB) classiers is one of the best methods for supervised approach for WSD.
We try to choose correct sense of a word (e.g. "pen") in this assignmen by using Naive **Bayes**
Classier. (You must implement the Nave **Bayes** Classier)

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Project is evaluate the performance of the Full **Bayes** classifier.

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Word Sense Disambiguation (WSD) is the ta...identifying which sense of an ambiguous word given a context.
Naive **Bayes** (NB) classifiers is one of the best methods for supervised approach for WSD. You try to choose correct sense of a word (e.g. ”pen”) in this project by using Naive **Bayes** Classifier. (You must implement the Native **Bayes** Classifier)

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Machine Learning Task 2
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3. Generate samples from two multivariate normal densities N(μi,Σi),i = 1,2, and calculate the **Bayes**’ optimal discriminant for the four cases in table 5.1.

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Naive Bayes Classier
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**Bayes** (NB) classiers is one of the best methods for supervised approach for WSD.
We try to choose correct sense of a word (e.g. "pen") in this assignmen by using Naive **Bayes**
Classier. (You must implement the Nave **Bayes** Classier)

$50
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NLP naive bayes
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Word Sense Disambiguation (WSD) is t...identifying which sense of an ambiguous
word given a context.
Naive **Bayes** (NB) classifiers is one of the best methods for supervised approach for WSD.
We try to choose correct sense of a word (e.g. ”pen”) in this assignmen by using Naive **Bayes**
Classifier. (You must implement the Na¨Ä±ve **Bayes** Classifier)

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machine learning hw 9
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Using Matlab , screenshot for output .
1- Generate a sample from a multivariate normal density N(μ,Σ), calcul...with μ and Σ. Check how your estimates change as the sample size changes.
2. Generate samples from two multivariate normal densities N(μi,Σi),i = 1,2, and calculate the **Bayes**’ optimal discriminant for the four cases in table 5.1.

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machine learning hw 8
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Using Matlab , screenshot for the output .
1- Generate a sample from a multivariate normal density N(μ,Σ), ca...with μ and Σ. Check how your estimates change as the sample size changes.
2. Generate samples from two multivariate normal densities N(μi,Σi),i = 1,2, and calculate the **Bayes**’ optimal discriminant for the four cases in table 5.1.

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developing naive **Bayes** classification algorithm

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Word Sense Disambiguation (WSD) is t...identifying which sense of an ambiguous
word given a context.
Naive **Bayes** (NB) classifiers is one of the best methods for supervised approach for WSD.
We try to choose correct sense of a word (e.g. ”pen”) in this assignmen by using Naive **Bayes**
Classifier. (You must implement the Na¨Ä±ve **Bayes** Classifier)

$27
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Use machine learning algorithm to analyze/make prediction from a data set.
I need 4 data mining algorithms in R for: knn, naive **bayes**, logistic/linear regression, C5.0 for a data set I will provide.
Max 60 euro

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analysis data
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Use the attached train and test data set, build the classification models based on the training set, and evaluate the models based on the test set....Compare the accuracies to tell which model is the best one.
In terms of the classification techniques, you should utilize logistical regression, KNN classifier, and Naïve **Bayes** classifications. Use R

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I built a Naive **Bayes** model and I need someone just to make the Roc Curve. The code is available in github and I have 3 hours to complete.

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...the data set available, identify which Classification algorithm works best in classifying new movie in terms of accuracy. The three algorithms are J48 Decision Tree, Naive **Bayes** and Neural Networks.
2. From number 1, select the best performing algorithm and build the model.
3. The model built will be ingested in a web application or desktop application

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we need freelancer who can grasp and handle arabic content, and have ability to do reprocessing and applying multiple classification on this (SVM , navies **bayes** and decision tree)
time duration is limited in 4-5 days only
programming language not specified in one language

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...classification algorithms on this data to finally measure the accuracy and performance of each classifier:
Artificial Neural Network
SVM
Random Forest
Decision Tree
Naive **Bayes**
Logistic regression
KNN
I need the following criteria to be measured in the form of graphs and tables:
sensitivity = true positive rate
specificity = true negative rate

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Implementation of Naive **Bayes** and Random Forest classifiers.

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Implementing the naive **bayes** and random forest on mushroom dataset. Generating roc curves for poisonous and edible attributes generated.

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Want to know the most commonly used algorithms which can be applied to any data issue? Here's the list.

This guide is intended for anyone who has a text problem they want solved