<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Appsolworld Website]]></title><description><![CDATA[This is Artificial Intelligence and Data Sceince Blog]]></description><link>http://localhost:3000</link><image><url>http://localhost:3000/favicon.ico</url><title>Appsolworld Website</title><link>http://localhost:3000</link></image><generator>NodeJS RSS Module</generator><lastBuildDate>Mon, 22 Jun 2026 17:31:47 GMT</lastBuildDate><atom:link href="http://localhost:3000/rss/posts" rel="self" type="application/rss+xml"/><item><title><![CDATA[Deep Neural Networks Application]]></title><description><![CDATA[1 - Packages]]></description><link>http://localhost:3000/blog/deep-neural-networks-applications</link><guid isPermaLink="false">tpGPajA5SFt9mqBcS</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Sat, 07 Jun 2025 07:12:35 GMT</pubDate></item><item><title><![CDATA[Building Your Deep Neural Network : Step by Step]]></title><link>http://localhost:3000/blog/building-your-deep-neural-network-step-by-step</link><guid isPermaLink="false">vbgzWvNEYqosSxAXh</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Fri, 06 Jun 2025 17:13:32 GMT</pubDate></item><item><title><![CDATA[Data Classification with one hidden layer]]></title><description><![CDATA[Taken reference from Coursera.]]></description><link>http://localhost:3000/blog/data-classification-with</link><guid isPermaLink="false">NfH9s6wdDKdfWgJ6r</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Fri, 06 Jun 2025 16:10:13 GMT</pubDate></item><item><title><![CDATA[Logistic Regression with a Neural Network mindset]]></title><description><![CDATA[import  numpy  as  np
 import  copy
 import  matplotlib.pyplot  as  plt
 import  h5py
 import  scipy
 from  PIL  import  Image
 from  scipy  import  ndimage
 from  lr_utils  import  load_dataset
 from  public_tests  import  *
%matplotlib inline
%load_ext autoreload
%autoreload  2    Overview of the Problem set]]></description><link>http://localhost:3000/blog/logistic-regression-with-a-neural-network-mindset</link><guid isPermaLink="false">DHrMz363nTh9NEKzS</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Sun, 23 Feb 2025 16:27:38 GMT</pubDate></item><item><title><![CDATA[K-Nearest Neighbors (KNN) Predicting Product Purchage.]]></title><description><![CDATA[We will use the Social Network ad dataset for product purchase prediction by using age, gender, and salary.]]></description><link>http://localhost:3000/blog/knn-</link><guid isPermaLink="false">Bn2andn9FMnWW9kQL</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Wed, 21 Aug 2024 12:42:39 GMT</pubDate></item><item><title><![CDATA[Naive Bayes - Email Spam Classification]]></title><description><![CDATA[import  pandas  as  pd
 import  numpy  as  np
 import  seaborn  as  sns
 import   matplotlib.pyplot  as  plt
 from  sklearn.feature_extraction.text  import  CountVectorizer
 from  sklearn.model_selection  import   train_test_split
 from  sklearn.naive_bayes  import  MultinomialNB
 from  sklearn.metrics  import  classification_report,confusion_matrix
 #Importing the Dataset 
spam_df = pd.read_csv( 'emails.csv' )
 print  (spam_df.head( 10 ))
print(spam_df.tail( 10 ))   0    Subject : naturally irresistible your corporate...      1 
 1    Subject : the stock trading gunslinger  fanny i...      1 
 2    Subject : unbelievable  new  homes made easy  im...      1 
 3    Subject :  4  color printing special  request add...      1 
 4    Subject :  do  not have money ,  get  software cds ...      1 
 5    Subject : great nnews  hello , welcome to medzo...      1 
 6    Subject : here ' s a hot play  in  motion  homela...      1 
 7    Subject : save your money buy getting this thin...      1 
 8    Subject : undeliverable : home based business f...      1 
 9    Subject : save your money buy getting this thin...      1 
                                                   text  spam
 5718    Subject : altos na gas model  kim , i know you ...      0 
 5719    Subject : power market research  i came across ...      0 
 5720    Subject : re : visit to houston  fyi  - - - - -...      0 
 5721    Subject : ees risk management presentations  for ...      0 
 5722    Subject : re : vacation  vince :  i just found ...      0 
 5723    Subject : re : research and development charges...      0 
 5724    Subject : re : receipts from visit  jim ,  than...      0 
 5725    Subject : re : enron  case  study update  wow ! a...      0 
 5726    Subject : re : interest  david ,  please , call...      0 
 5727    Subject : news : aurora  5  .  2  update  aurora ve...      0    print (spam_df.info())
 print (spam_df.describe())]]></description><link>http://localhost:3000/blog/naive-bayes-email-spam-classification-use-case</link><guid isPermaLink="false">A3foT5NugRFDBj8gg</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Wed, 21 Aug 2024 11:54:45 GMT</pubDate></item><item><title><![CDATA[Shallow Neural Networks]]></title><description><![CDATA[7-Steps process for practical AI.]]></description><link>http://localhost:3000/blog/shallow-neural-networks</link><guid isPermaLink="false">EHWzhPRsRwYYvDmPm</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Mon, 14 Sep 2020 02:32:58 GMT</pubDate></item><item><title><![CDATA[Machine Learning major types]]></title><description><![CDATA[Different types of Machine Learning :-]]></description><link>http://localhost:3000/blog/machine Learning</link><guid isPermaLink="false">CL6DjdBmwJicXkWdw</guid><dc:creator><![CDATA[tonu11singh@gmail.com]]></dc:creator><pubDate>Mon, 25 May 2020 10:04:50 GMT</pubDate></item><item><title><![CDATA[AI for business executives (based on McKinsey Articles)]]></title><description><![CDATA[Artificial intelligence: A definition AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.Machine learning: A definition Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data andexperiences to improve efficacy over time.]]></description><link>http://localhost:3000/blog/ai-for-business-executives-based-on-mckinsey-articles</link><guid isPermaLink="false">Y6mxhFnbt8xTpAumC</guid><dc:creator><![CDATA[admin@appsolworld.com]]></dc:creator><pubDate>Sun, 24 May 2020 18:26:23 GMT</pubDate></item></channel></rss>