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SMS SPAM DETECTION USING DEEP LEARNING.

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    SPAM SMS DETECTION USING DEEP LEARNING   PROBLEM STATEMENT: The growth of mobile phones user has led to the dramatic increase in spam SMS through in the most of the parts of the world SMS mobile messaging is considered as clean the problem of the span messaging is it romantically increasing year by year in Middle East and Asia as many people fall for the spam messages and provide personal as well as the financial information which might lead to the financial loss due to the online fraud however it has become much important that we could analyze the content in the mobile messages and predict or segment the SMS to spam or not in this report we will analyze the data and propose a model or system that will classify the type of SMS whether it is spam or ham   Fig. 1: Visualization of model working.  INTRODUCTION: SMS that is short message service which is commonly referred as text message is the service of sending short messages of around in 160 characters to various electro
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Machine Learning Terminologies.

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Machine Learning Terminologies and Processes. Introduction:     Hi and welcome all to my blog in which we are going to discuss about machine learning terminology and processes. We'll see end to end machine learning modelling process, The process begins with business problems and then lead to machine learning problem we'll discuss how that happens and how data goes through preprocessing and modelling process followed by predictioning the output.      Before we proceed with machine learning process lets have a look at various commonly used machine learning terminology. coming to the basic terminology i.e. Training, Model and Prediction. Training is the process where we train our model based on the historical data the 'Model' then analyzes various patterns in the data and be self sufficient to make future predictions on the unseen data.  Typically to train the model we split our data into 2 parts which is Training set and other is Testing set. The training dataset is shown
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Gradient Descent.

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 Gradient Descent Optimizing Technique for Machine Learning  Have you ever applied any Machine Learning algorithm on a data and found that the output is not as expected and have thought over that even applying the correct algorithm with correct syntax and logic. Here is what can help you to come over that and get the expected output i.e. optimization . There are various optimizing techniques of which one is Gradient Descent and we'll focus on that in this blog, So lets gets started. Optimization: an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible Introduction to Gradient Descent: Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent. These algorithms, however, are oft
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Performing Analysis of Meteorological Data

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Performing Analysis of Meteorological Data By: Hrishikesh Dherange  Introduction: Hello Folks, Welcome to my blog where we will perform analysis on the meteorological Data i.e. the Weather Data and the Data is solely taken from Krackin.com and the link for the same is: "https://www.kaggle.com/muthuj7/weather-dataset" all the operations will be done using the standard python libraries like Numpy, Pandas to perform analysis and matplotlib and seaborn library for the visualization.  Methodology: So, Lets start by importing the dataset in the Google Collaboratory mainly known as 'Colab'  we can use any interpreter, depends on the personal preference. As Google colab is an cloud based interpreter importing the dataset includes one extra step like uploading the data first to the colab and then reading that data in our traditional method. Here's how, from  google.colab  import  files upload = files.upload () This will prompt you to select the dataset from your local comp
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