Category Archives: Machine Learning

# Naive Bayes

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#importing the dataset
dataset=pd.read_csv('clusterincluster.csv');
columns=dataset.iloc[:, [0,1]].values; # here first colon means selecting all rows then after comma means selecting feature/column so manually we can enter 0 and 1 and we can also put -1 to select all rows except the last one as we are making the last one as class #independent variables
label=dataset.iloc[:, [2]].values; # dependent variables index in python starts from 0


#splitting the dataset into the traiing set and test set
from sklearn.cross_validation import train_test_split
columns_train, columns_test, label_train, label_test=train_test_split(columns,label, test_size=0.2, random_state=0) #if 10 observations/sample then 0.2 means two observations in a test set and 8 observations in test set

# Naive Bayes

# Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

#importing the dataset

dataset=pd.read_csv('clusterincluster.csv');

columns=dataset.iloc[:, [0,1]].values; # here first colon means selecting all rows then after comma means selecting feature/column so manually we can enter 0 and 1 and we can also put -1 to select all rows except the last one as we are making the last one as class #independent variables

label=dataset.iloc[:, [2]].values; # dependent variables index in python starts from 0

#splitting the dataset into the traiing set and test set

from sklearn.cross_validation import train_test_split

columns_train, columns_test, label_train, label_test=train_test_split(columns,label, test_size=0.2, random_state=0) #if 10 observations/sample then 0.2 means two observations in a test set and 8 observations in test set

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Posted in Machine Learning

Natural Language Processing, Text Summarization,

Posted on November 13, 2017 | Leave a comment

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Posted in Machine Learning, Natural Language Processing(NLP)

Neural Network: Machine Learning

Posted on November 10, 2017 | Leave a comment

www.polygon.com

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Posted in Data Science, Machine Learning

Machine Learning Mastery

Posted on November 9, 2017 | Leave a comment

Naive Bayes Classifier From Scratch in Python

The Naive Bayes algorithm is simple and effective and should be one of the first methods you try on a classification problem. In this tutorial you are going to learn about the Naive Bayes algorithm including how it works and how to implement it from scratch in Python. Update: Check out the follow-up on tips for …

machinelearningmastery.com

www.researchgate.net

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Posted in Machine Learning

Machine Learning in MATLAB: KNN

Posted on November 8, 2017 | Leave a comment

dataset=csvread('F:\Machine Learning Training\OneDrive-2017-10-28\Datasets\clusterincluster.csv',1,0);
label = dataset(:,3);
dataset = dataset(:, 1:2);
rng(2);
Mdl =fitcnb(dataset, label, 'CrossVal', 'on','kfold',10);

Mdl =fitcdiscr(dataset, label, 'CrossVal', 'on','kfold',10);
yfit=kfoldPredict(Mdl);
conf=confusionmat(label,yfit);
gscatter(meas(:1),meas(:,2),species,'')

dataset=csvread('F:\Machine Learning Training\OneDrive-2017-10-28\Datasets\clusterincluster.csv',1,0);

label = dataset(:,3);

dataset = dataset(:, 1:2);

rng(2);

Mdl =fitcnb(dataset, label, 'CrossVal', 'on','kfold',10);

Mdl =fitcdiscr(dataset, label, 'CrossVal', 'on','kfold',10);

yfit=kfoldPredict(Mdl);

conf=confusionmat(label,yfit);

gscatter(meas(:1),meas(:,2),species,'')

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Posted in Machine Learning

Tagged K nearest neighbors, Ma

Statistics: For Machine Learning: Random Variables : Discrete and Continuous

Posted on October 21, 2017 | Leave a comment

https://www.youtube.com/watch?v=rifK8BtHaYI

Must See:

How to Calculate Probability

Probability is the measure of how likely an event is to occur out of the number of possible outcomes. This wikiHow will show you how to calculate different types of probabilities. Define your events and outcomes. Probability is the…

www.wikihow.com

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Posted in Machine Learning, Research, Statistics

Tagged Machine Learning, Statistics

Machine Learning is Solving Real Life Problems

Posted on October 21, 2017 | Leave a comment

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Posted in Data Science, Machine Learning

Tagged data, Machine Learning, patterns

Training Set, Validation Set, Test Set

Posted on May 28, 2017 | Leave a comment

Training Set is a subset of the dataset used to build predictive models.
Validation Set is a subset of the dataset used to assess the performance of model built in the training phase
– It provides a test platform for fine-tuning model’s parameters and selecting the best performing model
– Not all modeling algorithms need a validation set
Test set or unseen examples is a subset of the dataset to assess the likely future performance of a model.
– If a model fits the training set much better than it fits the test set. Overfitting is probably the cause

Binary Classification(two class classification)

true|false, 1|0, -1|+1, male|female

Multi-class classification problems can be seen as binary classification problems.

Model Evaluation:
https://www.youtube.com/watch?v=zrr4eu29uww&list=PLea0WJq13cnCZZ3sXVEZ2OE5CLeZUlCmm&index=2

https://en.wikipedia.org/wiki/Confusion_matrix

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Posted in Data Mining, Machine Learning

Tagged Model Evaluation

Machine Learning in Easy Way

Posted on May 28, 2017 | Leave a comment

https://www.youtube.com/watch?v=IpGxLWOIZy4
Deep Learning, Deep Neural Network:
https://www.youtube.com/watch?v=BR9h47Jtqyw

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Posted in Data Science, Machine Learning

WEKA

Posted on January 1, 2017 | Leave a comment

@relation weather

@attribute outlook {sunny, overcast, rainy}
@attribute temperature numeirc
@attribute humidity numeric
@attribute windy {TRUE,FALSE}
@attribute play {yes,no}

@data
sunny, 90, 77, TRUE, no
overcast, 88, 90, FALSE, no

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Posted in Data Mining, Machine Learning, WEKA

Tagged arff, weka