Showing posts with label Machine Learnig. Show all posts
Showing posts with label Machine Learnig. Show all posts

## Friday, 11 March 2016

### Quick Dive to data handeling and machine learning in Python

This model show a quick way to apply different classification machine learning algorithm on data strored in CSV formate.
Python provide very powerful tools like pandas, numpy, and sklearn. we exploit them a lot.

# Source Identification using 2 Class of image¶

In [72]:
```from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import normalize
import numpy as np
import pandas as pd
# matplotlib.pyplot as plt
```

## Understanding Data¶

In [2]:
```sony=pd.read_csv("Sony_NONIQM.csv")
```
73

In [3]:
```nikon=pd.read_csv("Nikons_NONIQM.csv")
```
Out[3]:

## Saturday, 7 November 2015

### Using SVM Classifier

Support vector Machine (SVM) is one of most famous machine learning tool for classification problem. This is supervised learning technique .Read More
 SVM Margin
we are going to see how to use SVM classifier in python.

Our Demonstration uses digit dataset . This dataset uses 64 feature vector to identify handwritten digit [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] . It means this 64 feature extracted from Handwritten digit used to classify handwritten digit in 9 classes .

So Again we gonna use skilearn Python Package .

## Friday, 6 November 2015

### Using Naive Bayes classifier

This is most basic classifier, It uses concept of probability in order to predict the class of input feature data . Read More

We gonna see how to use Naive Bayes Classifier very easily using python.
 Bayes rule

### Prerequisite:

• Python 2
• sklearn [Python Module]
• Numpy  [Python Module]

We are going to apply this algorithm on Iris dataset .

Blogger Widgets