Types of data and variable

├── Day2.Data and variable
    ├── Data and variable.ipynb
    ├── README.md
    ├── dinfo.JPG
    └── variable.JPG
├── Graphical and Analytical representation of Data
    ├── README.md
    └── part1.ipynb
├── Introduction
    ├── Day1 -Introduction to ML.ipynb
    ├── ML1.JPG
    └── README.md
├── LICENSE
└── README.md


/Day2.Data and variable/README.md:
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 1 | <img align="right" alt="GIF"  width="353px" src="https://www.completecsharptutorial.com/wp-content/uploads/2016/02/Variable-in-Java-300x172.jpg" /><br>
 2 | 
 3 | 
 4 | <h1>Types of data and variable</h1><br>
 5 | <h2> Topics Covered:</h2><br>
 6 | <h3>&nbsp >What is Data?</h3><br>
 7 | <h3>&nbsp >What is the Difference between Data and Information ?</h3><br>
 8 | <h3>&nbsp >What is Variable?</h3><br>
 9 | <h3>&nbsp >Types of variable</h3><br>
10 | <h3>&nbsp >WHAT ARE INDEPENDENT AND DEPENDENT VARIABLES?</h3><br>
11 | <h3>&nbsp >What is Structured data and unstructured data?</h3><br>
12 | 
13 | <a href="https://github.com/Kushal997-das/Machine-Learning/blob/master/Day2.Data%20and%20variable/Data%20and%20variable.ipynb">Click this for Documentation</a><br><br>
14 | <a href="https://github.com/Kushal997-das"><h4>Follow me<3.</h4></a>
15 | 
16 | 


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/Day2.Data and variable/dinfo.JPG:
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https://raw.githubusercontent.com/Kushal997-das/Machine-Learning/81c178a3a883adc8ab332e178d35b87d25e27fe3/Day2.Data and variable/dinfo.JPG


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/Day2.Data and variable/variable.JPG:
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https://raw.githubusercontent.com/Kushal997-das/Machine-Learning/81c178a3a883adc8ab332e178d35b87d25e27fe3/Day2.Data and variable/variable.JPG


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/Graphical and Analytical representation of Data/README.md:
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1 | - Graphical and Analytical representation of Data
2 | 
3 | 


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/Graphical and Analytical representation of Data/part1.ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
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  7 |       "language": "python",
  8 |       "name": "python3"
  9 |     },
 10 |     "language_info": {
 11 |       "codemirror_mode": {
 12 |         "name": "ipython",
 13 |         "version": 3
 14 |       },
 15 |       "file_extension": ".py",
 16 |       "mimetype": "text/x-python",
 17 |       "name": "python",
 18 |       "nbconvert_exporter": "python",
 19 |       "pygments_lexer": "ipython3",
 20 |       "version": "3.8.3"
 21 |     },
 22 |     "colab": {
 23 |       "name": "part1.ipynb",
 24 |       "provenance": [],
 25 |       "include_colab_link": true
 26 |     }
 27 |   },
 28 |   "cells": [
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "view-in-github",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "<a href=\"https://colab.research.google.com/github/Kushal997-das/Machine-Learning/blob/master/Graphical%20and%20Analytical%20representation%20of%20Data/part1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "markdown",
 41 |       "metadata": {
 42 |         "id": "TjV4_p3F5A7j"
 43 |       },
 44 |       "source": [
 45 |         "# Graphical and Analytical representation of Data-1\n",
 46 |         ">part-1\n"
 47 |       ]
 48 |     },
 49 |     {
 50 |       "cell_type": "markdown",
 51 |       "metadata": {
 52 |         "id": "pIE64uER5A7n"
 53 |       },
 54 |       "source": [
 55 |         "### What is data analysis?"
 56 |       ]
 57 |     },
 58 |     {
 59 |       "cell_type": "markdown",
 60 |       "metadata": {
 61 |         "id": "6BqJP0cA5A7o"
 62 |       },
 63 |       "source": [
 64 |         "- Data analysis is the process of studying the available data and drawing valuable insights or information from it with the help of any software."
 65 |       ]
 66 |     },
 67 |     {
 68 |       "cell_type": "markdown",
 69 |       "metadata": {
 70 |         "id": "UmlhfJoN5A7o"
 71 |       },
 72 |       "source": [
 73 |         "- Data analysis being used everyday and everywhere to enable the businesses to take smart and accurate decisions."
 74 |       ]
 75 |     },
 76 |     {
 77 |       "cell_type": "markdown",
 78 |       "metadata": {
 79 |         "id": "HqGeFkFT5A7p"
 80 |       },
 81 |       "source": [
 82 |         "### How machine learning is related to data analysis ? <br>OR.<br> What has machine learning got to do with data analysis ?"
 83 |       ]
 84 |     },
 85 |     {
 86 |       "cell_type": "markdown",
 87 |       "metadata": {
 88 |         "id": "OxPrrczW5A7q"
 89 |       },
 90 |       "source": [
 91 |         "- We know data analysis is all about analyzing the data and bringing out the varibales insights from it using analytical tools."
 92 |       ]
 93 |     },
 94 |     {
 95 |       "cell_type": "markdown",
 96 |       "metadata": {
 97 |         "id": "U3dysT5S5A7q"
 98 |       },
 99 |       "source": [
100 |         "- Data analysis is a part of machine learning which analyzes your data and brings out insights before applying any algorithm in it ."
101 |       ]
102 |     },
103 |     {
104 |       "cell_type": "markdown",
105 |       "metadata": {
106 |         "id": "x3OtkLga5A7r"
107 |       },
108 |       "source": [
109 |         "### What is explanatory data analysis (EDA) ?"
110 |       ]
111 |     },
112 |     {
113 |       "cell_type": "markdown",
114 |       "metadata": {
115 |         "id": "8ZABNLM45A7r"
116 |       },
117 |       "source": [
118 |         "- Explanatory data analytics focuses on all the parts of context, mainly the why and how. An outcome can be statistically calculated, modeled, or visualized to tell you the likelihood of certain events based on preconceived variables."
119 |       ]
120 |     },
121 |     {
122 |       "cell_type": "markdown",
123 |       "metadata": {
124 |         "id": "m-rDBo1h5A7s"
125 |       },
126 |       "source": [
127 |         "\n",
128 |         "<img align=\"center\" alt=\"GIF\"  width=\"450px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/ba/Data_visualization_process_v1.png/350px-Data_visualization_process_v1.png\" />\n"
129 |       ]
130 |     },
131 |     {
132 |       "cell_type": "markdown",
133 |       "metadata": {
134 |         "id": "Tfm3k4er5A7t"
135 |       },
136 |       "source": [
137 |         "## Levels of data analysis ."
138 |       ]
139 |     },
140 |     {
141 |       "cell_type": "markdown",
142 |       "metadata": {
143 |         "id": "p4Wk3_oV5A7t"
144 |       },
145 |       "source": [
146 |         "#### There are 4 types of levels namely -"
147 |       ]
148 |     },
149 |     {
150 |       "cell_type": "markdown",
151 |       "metadata": {
152 |         "id": "XeSlMhtU5A7u"
153 |       },
154 |       "source": [
155 |         "- Descriptive Analysis.\n",
156 |         "- Diagnostic Analysis.\n",
157 |         "- Predictive Analysis.\n",
158 |         "- Prescriptive Analysis."
159 |       ]
160 |     },
161 |     {
162 |       "cell_type": "markdown",
163 |       "metadata": {
164 |         "id": "_jYuaEr45A7u"
165 |       },
166 |       "source": [
167 |         "<img align='center' alt=\"GIF\" width='450px' src=\"https://media-exp1.licdn.com/dms/image/C5112AQEQRrnvOyTYsQ/article-cover_image-shrink_600_2000/0?e=1605744000&v=beta&t=B-5q5C44zofSEC4r2Gb7nGyFumXpemSq0ySDqszGfKM\">"
168 |       ]
169 |     },
170 |     {
171 |       "cell_type": "markdown",
172 |       "metadata": {
173 |         "id": "mcXeEeBm5A7u"
174 |       },
175 |       "source": [
176 |         "> Descriptive Analysis: \n",
177 |         "- Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more."
178 |       ]
179 |     },
180 |     {
181 |       "cell_type": "markdown",
182 |       "metadata": {
183 |         "id": "HWa_-WmB5A7v"
184 |       },
185 |       "source": [
186 |         "> Diagnostic Analysis :\n",
187 |         "- Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.\n"
188 |       ]
189 |     },
190 |     {
191 |       "cell_type": "markdown",
192 |       "metadata": {
193 |         "id": "urkJTWt85A7v"
194 |       },
195 |       "source": [
196 |         ">Predictive Analysis:\n",
197 |         "- Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year’s revenue, data from previous years will be analyzed. If revenue has gone up 20% every year for many years, we would predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads."
198 |       ]
199 |     },
200 |     {
201 |       "cell_type": "markdown",
202 |       "metadata": {
203 |         "id": "6kFSoymI5A7w"
204 |       },
205 |       "source": [
206 |         ">Prescriptive Analysis: \n",
207 |         "- Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data-driven choices are made."
208 |       ]
209 |     },
210 |     {
211 |       "cell_type": "markdown",
212 |       "metadata": {
213 |         "id": "aE4Qmt3T5A7w"
214 |       },
215 |       "source": [
216 |         "#### These 4 types of data analysis can be applied to any issue with data related to it.\n"
217 |       ]
218 |     }
219 |   ]
220 | }


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/Introduction/Day1 -Introduction to ML.ipynb:
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  8 |       "name": "python3"
  9 |     },
 10 |     "language_info": {
 11 |       "codemirror_mode": {
 12 |         "name": "ipython",
 13 |         "version": 3
 14 |       },
 15 |       "file_extension": ".py",
 16 |       "mimetype": "text/x-python",
 17 |       "name": "python",
 18 |       "nbconvert_exporter": "python",
 19 |       "pygments_lexer": "ipython3",
 20 |       "version": "3.7.4"
 21 |     },
 22 |     "colab": {
 23 |       "name": "Day1 -Introduction to ML.ipynb",
 24 |       "provenance": [],
 25 |       "include_colab_link": true
 26 |     }
 27 |   },
 28 |   "cells": [
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "view-in-github",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "<a href=\"https://colab.research.google.com/github/Kushal997-das/Machine-Learning/blob/master/Introduction/Day1%20-Introduction%20to%20ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "markdown",
 41 |       "metadata": {
 42 |         "id": "1sxfrkK65Q9B"
 43 |       },
 44 |       "source": [
 45 |         "# Intoduction to Machine Learning."
 46 |       ]
 47 |     },
 48 |     {
 49 |       "cell_type": "markdown",
 50 |       "metadata": {
 51 |         "id": "z2GoDEHQ5Q9F"
 52 |       },
 53 |       "source": [
 54 |         "## What is Machine Learning?"
 55 |       ]
 56 |     },
 57 |     {
 58 |       "cell_type": "markdown",
 59 |       "metadata": {
 60 |         "id": "vu5bVHbu5Q9F"
 61 |       },
 62 |       "source": [
 63 |         ">Machine Learning is the ability of machines ,that is computers to learn and improve their past experience or data, without being explicitly programmed to do so .\n",
 64 |         "\n",
 65 |         "\n",
 66 |         ">Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.\n"
 67 |       ]
 68 |     },
 69 |     {
 70 |       "cell_type": "markdown",
 71 |       "metadata": {
 72 |         "id": "d9BeqY_v5Q9G"
 73 |       },
 74 |       "source": [
 75 |         "## Why machine learning is so important/Why machine learning is the future?"
 76 |       ]
 77 |     },
 78 |     {
 79 |       "cell_type": "markdown",
 80 |       "metadata": {
 81 |         "id": "1dcxMRKF5Q9H"
 82 |       },
 83 |       "source": [
 84 |         "> Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. As humans become more addicted to machines, we're witnesses to a new revolution that's taking over the world, and that is going to be the future of **Machine** Learning. "
 85 |       ]
 86 |     },
 87 |     {
 88 |       "cell_type": "markdown",
 89 |       "metadata": {
 90 |         "id": "yGpfLHC15Q9H"
 91 |       },
 92 |       "source": [
 93 |         "## Why is it called machine learning?"
 94 |       ]
 95 |     },
 96 |     {
 97 |       "cell_type": "markdown",
 98 |       "metadata": {
 99 |         "id": "xeIAX5tu5Q9I"
100 |       },
101 |       "source": [
102 |         "> It used to be called statistical learning theory. ... Its because the way human learn different thing machine is learning different thing. we are not telling machine what to do, we are teaching machine how to do.So, machine will use this skill to solve next problem it will face without explicitly programmed."
103 |       ]
104 |     },
105 |     {
106 |       "cell_type": "markdown",
107 |       "metadata": {
108 |         "id": "cjgRU3Em5Q9I"
109 |       },
110 |       "source": [
111 |         "## Application of machine Learning. "
112 |       ]
113 |     },
114 |     {
115 |       "cell_type": "markdown",
116 |       "metadata": {
117 |         "id": "6cfdwsT45Q9I"
118 |       },
119 |       "source": [
120 |         "## Here are the few Applications of Machine Learning from Day-to-Day Life-\n",
121 |         ">*1.Virtual Personal Assistants. Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants.*<br>\n",
122 |         ">*2.Predictions while Commuting.*<br>\n",
123 |         ">*3.Videos Surveillance.*<br>\n",
124 |         ">*4.Social Media Services.*<br>\n",
125 |         ">*5.Email Spam and Malware Filtering.*<br>\n",
126 |         ">*6.Online Customer Support.*<br>\n",
127 |         ">*7.Search Engine Result Refining.*<br>\n",
128 |         ">*8.Product Recommendations.*<br>"
129 |       ]
130 |     },
131 |     {
132 |       "cell_type": "markdown",
133 |       "metadata": {
134 |         "id": "BO34hh5p5Q9J"
135 |       },
136 |       "source": [
137 |         "## Types of machine learning:"
138 |       ]
139 |     },
140 |     {
141 |       "cell_type": "markdown",
142 |       "metadata": {
143 |         "id": "PttS0kYX5Q9J"
144 |       },
145 |       "source": [
146 |         "#### There are mainly two types of machine learning-\n",
147 |         "        1.Supervised Learning.\n",
148 |         "        2.Unsupervised Learning."
149 |       ]
150 |     },
151 |     {
152 |       "cell_type": "code",
153 |       "metadata": {
154 |         "id": "LbjM-wHg5Q9K",
155 |         "outputId": "49a7b8ea-ece0-43c0-f179-b75b0f4ee1df"
156 |       },
157 |       "source": [
158 |         "from IPython.display import Image\n",
159 |         "Image(filename=\"./ML1.jpg\")"
160 |       ],
161 |       "execution_count": null,
162 |       "outputs": [
163 |         {
164 |           "output_type": "execute_result",
165 |           "data": {
166 |             "image/jpeg": 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74r9FfgX/AMkt0D/r3H8zX50x/eFfot8C/wDklugf9e4/9CNePmXwI/UuBv48z0DuaKO5or5w/aQbpXz/APth/wDJOR/19RV9AN0r5/8A2w/+Scj/AK+oq7MJ/GifLcTf8iuqfFVDdKKG6V9cfze9xtFFFMkKKKKACiiigAXr3/CvqH9j/Q7Oay1vWGjSfU4H8qIHG5VxnH418vKNzAfnXafDf4qax8L9Va7051MUnE1tLja/p+lcuIhKcLRPoMjxdHB4r2ldaHo/in4/fEnw/rV2l1CLCKOVljhuLTjAz0Y9aw/id8fJ/iR4Ws9Mm0yOG8Vw0tyrfKcf3OM167pP7UXgrxXB5PiPSGtWwN3mxCZfr0yBWF8aPgv4avvB58YeEmjSIASvHC2Y3UkA49CM5/CuGlONNqM42Z9fjaWIxVCpPCV1Ui90+iPIbP4P6xffD+fxcs1uNOhDOylmLhR14xXLeE/Ddz4u8QWekWZVLi7by08xsKGAJ5x9K+kdBUj9k3Vc9VgmBxXi3wJwfiz4dGelyT/44x/rXRGs3CUn0PDr5XQhiqFGC0mlczPG3w91HwH4iXRr2aOS5ZQyvAxxz0zx711+ofs0eLrG+0+1jSC6F6rNvhkOyNV28sT/ALw6dfzrd/aWUr8ZrLbwdkePrxz+Yruf2pfGGr+HNF8LWelXslkl0khk8ptpYKI9oyOnLZrOVapJQcep3RyvBUPrFSte1NnlniL9mbxdoOkS34+zXyQjLxWzndt9ecZryuz0641C7S1t4XlnZtojGQ2fSvoL9l34karfeKpdDvrqW/tLyNnAuJN7KyqSSM9jjH410vws8F6bD+0J4wIiU/YNphVhkKXGc/rR9YnDmU9wjlGHx8KVXDuylujzXT/2WfFl7Yw3MstnZtIM+TPK29fyFcD48+HOufDvVBa6rAQsgBjnjfKSDPY4r6B+Inw18QeJPFd5ff8ACeWtmjPiO3S4KiNR04FX/Hmiw3nwQuNM1rXLPVtWsU82K7jkBb5SCB69BinCtNuJpisowijUhFcrj+J4Z4d+B/iDxT4Nm8RaeYnto2b9wrMZDtxkAY9xWtZ/s0+Krj7Kt2bSwnuM7IJ5dzEhdxA255wDXr/wm1268Lfs2arqtntNzbebNGX6Z2qa+f8ARPitrsHjbTNb1HU7m9eC4Vz5jfLz8rfL0AwxHFEatWo5tPRGNbB5fg/YQmnzTSOa8UeHbzwnrt7pF+qx3FrJtbByDxnj8CK6nwf8GdZ8YeGbvXbeW1tLG3DEyXO7OVGTjFemftReEU1LUtD8S6dEZo9SVYWCfxEdPxIKr+Fb3xevF+FXwW0rwraSYvrpRHK68FsYLn8SQPwoeIclFLciOTUqE6s63wr4TkPDbeMYfgDqBsJNPGhRrMP3gbzlUOd3scmvOfh78J9b+JN5NDpkKxxQgh7ickRj2zXuXgnK/sp6oV5+Wc/X5zVH9n/xZoNx8PdV8K3GsDQ9RuJGdbhpArMGCgYJ+n61HtJRi3HudbweGrV6Ma705evzON1j9lnxbpenTXMctlfeWM+Xbyneee27AryfTtGvdU1JbG2t5JbyR/LEKDnIOD096+i5/gt8QvC9vPfeH/FT6ovksoVpCwdT/vHGfpUX7I+greeJfEF/fIGv7WPaN64KMTycfnVfWJRpuTepyRyWjWxdKhGlyrXrucrb/sq+LJ7VJZJ9PtmYf6uSRiw9jjivOPGnw/1fwBqYsdVt/JY/6uVASr/Q/jXv3iz4ZeJfEXiK61GTx9ZQ7pD5cUdyVjTngAetafxi0qC7+C8cep6tZ6nrmlgMlxDKCzjeAffO2lHETub4jJcNKjPkXK4/jqeBeA/hD4j+IQebTLUC2UgNPIQqj8z/ACr0jwz8AvEvgXxnoWo3KQ3tqLpRI1qzHy+DjIPv6V3PiLWp/hh+zlpZ0YiK5mSONpk6jfyx+vbNeN/B3x/4jT4haOn9s3kqXEnlSpNM8iEYPGCSM0nOpUjJrY1p4bA4LE0qLTc3ZnR/tfJjx5p/TIsweR/tN05rwevef2vsnx1p5PP+iAfQ7mrwau3D/wANHymdq2OqeoUUUV0nhBRRRQAUUUUAFX9A/wCQxY/9d0/9CFZ5rQ0H/kNWH/XxH/6EKwrfw5HZg/8AeIeqP080n/kH2v8A1zX+Qq7VLSf+Qfb/APXNf5CrtfGy3P6qp/AgoooqTUKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooARulJ60rdKT1oA8/wDjp/yTHxB/16t/Svzrbp/n2r9FPjp/yTHxB/16t/Svzrbp/n2r6fLf4bPwzjf/AH2Pp+rG0UUV6Z+cPcKKKKYhV610Xw9/5HTRv+vlP51zgroPAlxHaeLtJlmcRotwpLN061nU+Bno5c7Yunfuj6C/bM/1fh36y/8AstfMMf3hX1H+2XbPJY+H7pUYwq7xl8cbiAQPyU/lXy9bqZpFCDcScDHrXJhmvYM+izqm5ZraKvc+sPjkB/wz7oWcY8236/Rq8z8E/s7y+NvhrH4jtr0/a5GYJa8BeH2nLE9AMn8K9L+PwNn8BtDtph5c4kgyjdfut/jVbwneSWf7Jc8sErQygSgPGcEZuCDXHGU4wXK92fTYjDYeviakq0b8kE/wOf0n9lGz1C3SB/FlqdT2/Na27hgp9hnJrx3xN8PdS8NeNpPDk6LLehlWPyxhXVvusK6D4B30/wDwtjQnMzZe4CuxYksCef0rs/jxrw8MfH6z1TyvP+zW8EjR+vLg/pXRGVSFRxbvoeJUw+AxWDp4lQ5VzWfmXE/Zk03S7e1XXfFVvpl/IgLQllGCfqa8++LPwlf4cS2Uyahb6lZXj7Y5Yz85x13dvyr2TX2+G3x2uoLybXZNK1dkUbZHCe23DY5rzP4yfBzU/h9Z6fdDVH1jRGkbypNxYQ5Ixk9OamnVne0jfMMHSlQc8PTTjG2qfTzK+v8AwUXQ/hPYeMPt5ka6ijk+zbeBuz3rnfhL4DX4keL4tFkumtUeJpPMUZI2gkj/AD617r8QAR+yvoIHP+j26nHrivOf2UR/xdy0JBA+zXBzj2qlWl7KUjCpl1COZUaEY6NJs5u6+F/2b4rL4PS7LBp/KW4YcjIJ6V6LqH7KX9n600d14ghtdMVAVurplRnPcKM/zqHVP+TqIeQP9NHf0U1D+1pe3TePLS0M8ptlt1YR7jtznsKmM6k2o3toaywuCweGqV6lLm5ZNDPG/wCzLPo3h2XWdD1ePWoIVLyBME4/2SOD1z9K8r8E+CNU8d65FpmlweZOwJk3cKgB5JNe+/shaxe39n4i0m5kabTI4Y5UhblULBg35gDitr9mTTLC0uvHcqlIHin8hJQeVjAY5/P+VZuvOmmjphk+ExdWhVhHlU07r0OQf9mXQ7E+Tf8AjKK1vcYaLKDB9ME5ryr4mfC3U/hrrSWtygms5hmC8Q5En19Oteoax4G+G2oaxc3dx48uWupZiXYlSQc8jO3tWn8YPFnhTVvhRY6Lp+vLq1/YMgill/1r7cqc/n+lXCrU5jnxWBwU6E7WUo7fec5oP7Mt34k8D6drdhqKPPdAN5MowiqTgkn2GTVyz/Zk0+61X+yX8Twy3/kGVY7cghsHkHuK7HXdavNH/ZYsprCZrabyYlDxnB5cA814B8L/ABrceE/HenarJM7xiXbPuJJdGI3c/nTjKpNSlcrEUsvw9WlQlTvdLUwfEGhz+Gdev9Ouk2zWspQhu4H0r0r/AIUWlr8K28XajqT2jOu6K2KD5snCj6En8q7n41fCtvEPxO0C909fOtNbdUd0GVBUbmOf9pefwqv+1R4njsotD8H2DL9ns4laUK3OBgKp/IH8aFXlUtFGUcrw+BhVr146N2j/AMAyPEPhnXYfgHp2pzeIJptOmSJ1sSoATIIwD3HtWV8N/gCfGHhc+INY1KPRtNZgI3kAGR0zz716L4w+b9lXQQO8cI+nWsrwP8UPBfjD4b2vhDxXK+nNbr5ayISFPcMSOB+NQpT5G49z0J4TCfWlTra2imr9WYHir9nGx03w7daroniez1Q2iNLMrbc7QM8bT/OvMvh/8PdW+IusLp2m2wYj55ZHPyRj1zXs+u/s6Knh291HwV4nfUrYR5kgVwwlAGdoKnFdD+ynY2tj4C8STSSi0m3+XLMOsYCkA8/54q3XlGnvqctPJ6eIxsFVpcsWm9HuczL+zRoVm32abxrbw32drQblzux0xnI5ryL4ifDfVPhzr39nX0RaNuYbmMZjkGR09OteoT+AfhpNqD3reP7n7TJIZTL8uS+eedtbXxu8X+FPEngTRbKw1ldX1K1lRFkY/vGQ8MelTCtOLu9ScRgcJWotRtGUXZWe+v5nI/D39m2/8W+H49bv9Qi0iwm+ePcPmKevoM+9ejfDD4Ix+D/iFpup6TrNvrNpGsi3ASRS8WRgcA+pqP8Aag1K88P/AA38PaZpjNb2EmyJ2Q4BVUyFyPoD+Fea/sx3lxD8VbSMSyKjxyB03EK/ynkipk51IOTZ2UVhMJjoYSFPVJalH9pHj4paqPZK8rr1P9pJsfFTVR67f5V5ZXpUf4aPg80/32r6hRRRW55QUUUUCYsf3hX6LfAv/klugf8AXuP/AEI1+dMf3hX6LfAv/klugf8AXuP/AEI14+ZfAj9T4F/jzPQO5oo7mivnD9pBulfP/wC2H/yTkf8AX1FX0A3Svn/9sL/knI/6+oq7MJ/GifLcTf8AIrqnxVQ3SihulfXH83vcbRRRTJCiiigAooooAFxkZGR6V7h8Bj8P7/T9S0nxQoGo3TYW4m+4q44CnsRXh4pVYr3BHbj/AOtWVSmqkbXsengcb9Rqe05VJdmfT0/7K3huS6+02fjFY7YcouyNjjORl91T/Frx34a+H/wxPgfw/dLc3DJ5XyODtBbczE+5zwP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167 |             "text/plain": [
168 |               "<IPython.core.display.Image object>"
169 |             ]
170 |           },
171 |           "metadata": {
172 |             "tags": []
173 |           },
174 |           "execution_count": 2
175 |         }
176 |       ]
177 |     },
178 |     {
179 |       "cell_type": "markdown",
180 |       "metadata": {
181 |         "id": "RpvO3Bf-5Q9N"
182 |       },
183 |       "source": [
184 |         "#### before going to Supervised Learning and Unsupervised Learning we should have an idea about labeled data and unlabeled data.\n"
185 |       ]
186 |     },
187 |     {
188 |       "cell_type": "markdown",
189 |       "metadata": {
190 |         "id": "p7cwaSVe5Q9O"
191 |       },
192 |       "source": [
193 |         "### labeled data-\n",
194 |         ">1.Labeled data, used by Supervised learning add meaningful tags or labels or class to the observations (or rows).                 These tags can come from observations or asking people or specialists about the data.\n",
195 |         "\n",
196 |         ">2.Classification and Regression could be applied to labelled datasets for Supervised learning.\n",
197 |         "         \n",
198 |         ">3.Machine learning models can be applied to the labeled data so that new unlabeled data can be presented to the model             and a likely label can be guessed or predicted."
199 |       ]
200 |     },
201 |     {
202 |       "cell_type": "markdown",
203 |       "metadata": {
204 |         "id": "EwVXGH6f5Q9O"
205 |       },
206 |       "source": [
207 |         "### unlabeled data-"
208 |       ]
209 |     },
210 |     {
211 |       "cell_type": "markdown",
212 |       "metadata": {
213 |         "id": "ZknfVLZy5Q9P"
214 |       },
215 |       "source": [
216 |         ">1.Unlabeled data, used by Unsupervised learning however do not have any meaningful tags or labels associated with it.\n",
217 |         "\n",
218 |         ">2.Unsupervised learning has more difficult algorithms than supervised learning since we know little to no information about the data, or the outcomes that are to be expected.\n",
219 |         "\n",
220 |         ">3.Clustering is considered to be one of the most popular unsupervised machine learning techniques used for grouping data points, or objects that are somehow similar.\n"
221 |       ]
222 |     },
223 |     {
224 |       "cell_type": "markdown",
225 |       "metadata": {
226 |         "id": "K6xnwNM05Q9P"
227 |       },
228 |       "source": [
229 |         "## Supervised Learning:\n"
230 |       ]
231 |     },
232 |     {
233 |       "cell_type": "markdown",
234 |       "metadata": {
235 |         "id": "AFvGld885Q9P"
236 |       },
237 |       "source": [
238 |         ">1.Supervised learning is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.\n",
239 |         "\n"
240 |       ]
241 |     },
242 |     {
243 |       "cell_type": "markdown",
244 |       "metadata": {
245 |         "id": "t_abWjw95Q9Q"
246 |       },
247 |       "source": [
248 |         ">2.In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data"
249 |       ]
250 |     },
251 |     {
252 |       "cell_type": "markdown",
253 |       "metadata": {
254 |         "id": "9cbnZV7f5Q9Q"
255 |       },
256 |       "source": [
257 |         "## Unsupervised Learning:\n"
258 |       ]
259 |     },
260 |     {
261 |       "cell_type": "markdown",
262 |       "metadata": {
263 |         "id": "gFdwYoXa5Q9R"
264 |       },
265 |       "source": [
266 |         "> 1.Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program. "
267 |       ]
268 |     },
269 |     {
270 |       "cell_type": "markdown",
271 |       "metadata": {
272 |         "id": "C2V64pzl5Q9R"
273 |       },
274 |       "source": [
275 |         ">2.unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own."
276 |       ]
277 |     },
278 |     {
279 |       "cell_type": "markdown",
280 |       "metadata": {
281 |         "id": "L1pN_-015Q9R"
282 |       },
283 |       "source": [
284 |         "### This is all about the introduction to ML. "
285 |       ]
286 |     },
287 |     {
288 |       "cell_type": "markdown",
289 |       "metadata": {
290 |         "id": "vMwC3jIm5Q9S"
291 |       },
292 |       "source": [
293 |         "### Hope you liked it <3."
294 |       ]
295 |     },
296 |     {
297 |       "cell_type": "markdown",
298 |       "metadata": {
299 |         "id": "m43ZdmMa5Q9S"
300 |       },
301 |       "source": [
302 |         "#### Thank You:)"
303 |       ]
304 |     }
305 |   ]
306 | }


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/Introduction/README.md:
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 1 | <img align="right" alt="GIF"  width="353px" src="https://static.wixstatic.com/media/bb7b70_d5fde322f7914060b7d997ba9d506a50~mv2.gif" /><br>
 2 | 
 3 | 
 4 | <h1>Introduction To Machine Learning</h1><br>
 5 | <h2> Topics Covered:</h2><br>
 6 | <h3>&nbsp >What is Machine Learning?</h3><br>
 7 | <h3>&nbsp >Why machine learning is so important/Why machine learning is the future?</h3><br>
 8 | <h3>&nbsp >Why is it called machine learning?</h3><br>
 9 | <h3>&nbsp >Application of machine Learning.</h3><br>
10 | <h3>&nbsp >Types of machine learning</h3><br>
11 | 
12 | <a href="https://github.com/Kushal997-das/Machine-Learning/blob/master/Introduction/Day1%20-Introduction%20to%20ML.ipynb">Click this for Documentation</a><br><br>
13 | <a href="https://github.com/Kushal997-das"><h4>Follow me<3.</h4></a>
14 | 
15 | 
16 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 Kushal Das
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | [![](https://img.shields.io/badge/Author-KushalDas-green.svg)](https://github.com/Kushal997-das)<br>
 2 | [![Hello programmer Welcome to this repo](https://img.shields.io/badge/Hello!-Welcometothisrepo-brightgreen.svg?style=flat&logo=github)](https://github.com/kushal997-das)
 3 | ![](https://img.shields.io/badge/Programming_Language-Python-blue.svg)
 4 | [![](https://img.shields.io/github/languages/top/Kushal997-das/Machine-Learning.svg?style=plastic)](https://github.com/Kushal997-das/Machine-Learning)
 5 | ![](https://img.shields.io/badge/Status-Inprogrees-green.svg)
 6 | 
 7 | 
 8 | <br>
 9 | <img align="right" alt="GIF"  width="353px" src="https://thumbs.gfycat.com/AshamedWeightyDachshund-small.gif" /><br>
10 | 
11 | 
12 | - [Day1](Introduction)
13 |   - [Introduction to ML](https://github.com/Kushal997-das/Machine-Learning/blob/master/Introduction/Day1%20-Introduction%20to%20ML.ipynb)
14 | - [Day2](https://github.com/Kushal997-das/Machine-Learning/tree/master/Day2.Data%20and%20variable)  
15 |   - [Day2.Data and variable](https://github.com/Kushal997-das/Machine-Learning/blob/master/Day2.Data%20and%20variable/Data%20and%20variable.ipynb)
16 | 
17 | <br><br>
18 | <p align="center">
19 |   <b><i>Let's connect! Find me on the web.</i></b>
20 | 
21 | [<img height="30" src = "https://img.shields.io/badge/Youtube-%23E4405F.svg?&style=for-the-badge&logo=Youtube&logoColor=white">][Youtube] 
22 | [<img height="30" src = "https://img.shields.io/badge/gmail-c14438?&style=for-the-badge&logo=gmail&logoColor=white">][gmail] 
23 | [<img height="30" src="https://img.shields.io/badge/linkedin-blue.svg?&style=for-the-badge&logo=linkedin&logoColor=white" />][LinkedIn]
24 | [<img height="30" src="https://img.shields.io/badge/github-black.svg?&style=for-the-badge&logo=github&logoColor=white" />][Github]
25 | <br />
26 | <hr />
27 | 
28 | [youtube]: https://www.youtube.com/channel/UCIHj6mNCMnSnmWLHOxzIESw?view_as=subscriber
29 | [gmail]: mailto:daskushal980@gmail.com
30 | [linkedin]: https://www.linkedin.com/in/kushal-das-7337421a9/
31 | [github]: https://github.com/Kushal997-das/
32 | 
33 | 
34 | 
35 |   
36 | If you have any Queries or Suggestions, feel free to reach out to me.
37 | 
38 | <h3 align="center">Show some &nbsp;❤️&nbsp; by starring some of the repositories!</h3>
39 | 


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