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marcoi

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Apr 6, 2013
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might be nice to have if you could put together a newbie post on how to get started for those who want to tinker. Especially if there is a cheap way for the home lab user to play, I think that would get a few more people interested in it.
 
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marcoi

Well-Known Member
Apr 6, 2013
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Gotha Florida
@Patrick - great articles on hardware. I got enough to play with using my laptop with my external 1080 card. Next questions is how do i start after i get the hardware? Are there software packages? Do I need to code something? What would be a good way to get my feet wet to learn about the tech and what it can do and how i can use it? Maybe after doing that i might decide it's not something i would be interested in but ATM i don't have enough exposure/experience to decide that. And if this isn't the right place to learn that, no worries, I figured it wouldn't hurt to ask.
 
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JackieAI

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Aug 9, 2017
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Regiane Folter

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Mar 28, 2018
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might be nice to have if you could put together a newbie post on how to get started for those who want to tinker. Especially if there is a cheap way for the home lab user to play, I think that would get a few more people interested in it.
Hey there! That's a good idea. Can I give you a suggestion? It's a post+tutorial for creating a ML application based on soccer results. For ML and soccer fans haha! And it's focused on beginners, so I think you will find it useful :)
 

vishabhgoel

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Aug 27, 2018
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Hi All,

I have a Dataset which explains the quality of wines based on the factors like acid contents, density, pH, etc. I am attaching the dataset.csv file which will show you the various data. According to the dataset we need to use the Multi Class Classification Algorithm to Analyze this dataset using Training and test data. Please correct me if I am wrong?

Also I have used Principal Component Analysis Algorithm to work with this dataset. Below is the code I have used :-

# -*- coding: utf-8 -*-
"""
Created on Sun Aug 26 14:14:44 2018

@author: 1022316
"""

# Wine Quality testing
#Multiclass classification - PCA

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

#importing the Dataset
dataset = pd.read_csv('C:\Machine learning\winequality-red_1.csv')
X = dataset.iloc[:, 0:11].values
y = dataset.iloc[:, 11].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#Applying the PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = 2 )
X_train = pca.fit_transform(X_train)
X_test = pca.fit_transform(X_test)
explained_variance = pca.explained_variance_ratio_

# Fitting Logistic Regression to the Training set
#from sklearn.tree import DecisionTreeClassifier
#classifier = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train)
#y_pred = classifier.predict(X_test)

#classifier = LogisticRegression(random_state = 0)
#classifier.fit(X_train, y_train)

#Fiiting the Logistic Regression model to the training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)


#Predicting thr Test set results
y_pred = classifier.predict(X_test)


# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)


Please let me know if I am using the correct algorithm of this dataset. Also, as I can see we have 9 classes in which this dataset will be divided. Please also let me know how will I visualize and plot the data accordingly in different classes.

I will be attentive to the reply.

Regards,
Vishabh Goel
 

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PetePeterson

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Nov 6, 2019
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It would be great to know more about Machine Learning as I think this area is going to be one of the most demanded in the nearest future, even now more and more people become interested in it. I also can recommend guys who also know all about ml and have done really interesting projects.
 

nthu9280

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Feb 3, 2016
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San Antonio, TX
Spam!
It would be great to know more about Machine Learning as I think this area is going to be one of the most demanded in the nearest future, even now more and more people become interested in it. I also can recommend guys who also know all about ml and have done really interesting projects.