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42 changes: 20 additions & 22 deletions 01_introduction.ipynb
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Expand Up @@ -167,21 +167,21 @@
"\n",
"Now Ask yourself the following questions:\n",
"\n",
"- What needs to be done: Predict the weather conditions\n",
"- **What needs to be done**: Predict the weather conditions\n",
" - What do you mean predict the weather conditions? Do you want to predict the temperature, the humidity, the wind speed, the precipitation, the cloud cover, the visibility, the air pressure, the weather type, etc?\n",
" - In what form do you want the prediction? Do you want the prediction in the form of a class label, a probability, a value, etc?\n",
" - Does the business need predictions at day level, hour level, minute level, etc?\n",
" - How is the data going to be used? Is the data going to be used for decision making, reporting, analysis, etc?\n",
"- How it should be done: Predict the weather conditions\n",
"- **How it should be done**: Predict the weather conditions\n",
" - What kind of data do you have? Do you have historical data, real-time data, forecast data, etc? Do you have structured data, unstructured data, semi-structured data, etc?\n",
" <!-- - What kind of model do you want to build? Do you want to build a regression model, a classification model, a clustering model, etc? `More on this later!` -->\n",
" <!-- - What kind of learning do you want to apply? Do you want to apply supervised learning, unsupervised learning, reinforcement learning, etc? Which all would be applicable to the problem statement? -->\n",
"\n",
"When it comes to types of Machine Learning, the following questions are most relevant to ask:\n",
"\n",
"> - What needs to be done:\n",
"> - **What needs to be done**:\n",
"> - What do you mean predict the weather conditions? Do you want to predict the temperature, the humidity, the wind speed, the precipitation, the cloud cover, the visibility, the air pressure, the weather type, etc?\n",
"> - How it should be done:\n",
"> - **How it should be done**:\n",
"> - What kind of data do you have? Do you have historical data, real-time data, forecast data, etc? Do you have structured data, unstructured data, semi-structured data, etc?\n",
"\n",
"The above questions formulate what I call the `Matrix of Problems`.\n",
Expand All @@ -192,33 +192,31 @@
"\n",
"starting statement: \"Based on the historical data, ...\"\n",
"\n",
"| Objective/Type Of Learning | Supervised | Unsupervised |\n",
"| :------------------------: | :----------------------------------------------------------: | :--------------------: |\n",
"| Regression | predict the temperature and the humidity values | estimate the |\n",
"| Classification | predict if the day is going to be pleasent/rainy/sunny/windy | |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Objective/Type Of Learning | Supervised | Unsupervised |\n",
"| :------------------------: | :----------------------------------------------------------: | :--------------------------------------------------: |\n",
"| `Regression` | predict the temperature and the humidity values | estimate the temperature based on similar days |\n",
"| `Classification` | predict if the day is going to be pleasent/rainy/sunny/windy | estimate the day class based on similar days in past |\n",
"| `Discovery` | Not Applicable | discovering data patterns, identifying behaviors |\n",
"\n",
"The above table does not cover all the use cases like combinatorics problems, optimization problems, Anomaly detection, etc. But the above table gives a good idea of the types of problems that can be solved using `Machine Learning`.\n",
"\n",
"![map](assets/map_of_ml.png)"
"Below is a representative map for scikit learn library which provides a good set of algorithms which are used to solve ML problems. It can be used as a reference to understand the types of problems that can be solved using `Machine Learning`.\n",
"\n",
"\n",
"|![map](assets/map_of_ml.png)|\n",
"|:-:|\n",
"|Source: scikit-learn|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to Project Oriented Approach: `Stock Market`\n",
"\n",
"\n",
"![problem](assets/problem0.png)"
"| ![problem](assets/problem0.png) |\n",
"| :-----------------------------: |\n",
"| Stock Market Problem Statement |\n"
]
}
],
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44 changes: 34 additions & 10 deletions _build/html/01_introduction.html
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Expand Up @@ -512,15 +512,15 @@ <h2>Types of Machine Learning<a class="headerlink" href="#types-of-machine-learn
<p>Business Question: “Predict the weather conditions of the next upcoming weekend.”</p>
<p>Now Ask yourself the following questions:</p>
<ul>
<li><p>What needs to be done: Predict the weather conditions</p>
<li><p><strong>What needs to be done</strong>: Predict the weather conditions</p>
<ul class="simple">
<li><p>What do you mean predict the weather conditions? Do you want to predict the temperature, the humidity, the wind speed, the precipitation, the cloud cover, the visibility, the air pressure, the weather type, etc?</p></li>
<li><p>In what form do you want the prediction? Do you want the prediction in the form of a class label, a probability, a value, etc?</p></li>
<li><p>Does the business need predictions at day level, hour level, minute level, etc?</p></li>
<li><p>How is the data going to be used? Is the data going to be used for decision making, reporting, analysis, etc?</p></li>
</ul>
</li>
<li><p>How it should be done: Predict the weather conditions</p>
<li><p><strong>How it should be done</strong>: Predict the weather conditions</p>
<ul>
<li><p>What kind of data do you have? Do you have historical data, real-time data, forecast data, etc? Do you have structured data, unstructured data, semi-structured data, etc?</p>
<!-- - What kind of model do you want to build? Do you want to build a regression model, a classification model, a clustering model, etc? `More on this later!` -->
Expand All @@ -532,12 +532,12 @@ <h2>Types of Machine Learning<a class="headerlink" href="#types-of-machine-learn
<p>When it comes to types of Machine Learning, the following questions are most relevant to ask:</p>
<blockquote>
<div><ul class="simple">
<li><p>What needs to be done:</p>
<li><p><strong>What needs to be done</strong>:</p>
<ul>
<li><p>What do you mean predict the weather conditions? Do you want to predict the temperature, the humidity, the wind speed, the precipitation, the cloud cover, the visibility, the air pressure, the weather type, etc?</p></li>
</ul>
</li>
<li><p>How it should be done:</p>
<li><p><strong>How it should be done</strong>:</p>
<ul>
<li><p>What kind of data do you have? Do you have historical data, real-time data, forecast data, etc? Do you have structured data, unstructured data, semi-structured data, etc?</p></li>
</ul>
Expand All @@ -557,22 +557,46 @@ <h3>Matrix of problems: <code class="docutils literal notranslate"><span class="
</tr>
</thead>
<tbody>
<tr class="row-even"><td class="text-center"><p>Regression</p></td>
<tr class="row-even"><td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">Regression</span></code></p></td>
<td class="text-center"><p>predict the temperature and the humidity values</p></td>
<td class="text-center"><p>estimate the</p></td>
<td class="text-center"><p>estimate the temperature based on similar days</p></td>
</tr>
<tr class="row-odd"><td class="text-center"><p>Classification</p></td>
<tr class="row-odd"><td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">Classification</span></code></p></td>
<td class="text-center"><p>predict if the day is going to be pleasent/rainy/sunny/windy</p></td>
<td class="text-center"><p></p></td>
<td class="text-center"><p>estimate the day class based on similar days in past</p></td>
</tr>
<tr class="row-even"><td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">Discovery</span></code></p></td>
<td class="text-center"><p>Not Applicable</p></td>
<td class="text-center"><p>discovering data patterns, identifying behaviors</p></td>
</tr>
</tbody>
</table>
<p>The above table does not cover all the use cases like combinatorics problems, optimization problems, Anomaly detection, etc. But the above table gives a good idea of the types of problems that can be solved using <code class="docutils literal notranslate"><span class="pre">Machine</span> <span class="pre">Learning</span></code>.</p>
<p>Below is a representative map for scikit learn library which provides a good set of algorithms which are used to solve ML problems. It can be used as a reference to understand the types of problems that can be solved using <code class="docutils literal notranslate"><span class="pre">Machine</span> <span class="pre">Learning</span></code>.</p>
<table class="table">
<thead>
<tr class="row-odd"><th class="head text-center"><p><img alt="map" src="_images/map_of_ml.png" /></p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td class="text-center"><p>Source: scikit-learn</p></td>
</tr>
</tbody>
</table>
<p><img alt="map" src="_images/map_of_ml.png" /></p>
</section>
</section>
<section id="introduction-to-project-oriented-approach-stock-market">
<h2>Introduction to Project Oriented Approach: <code class="docutils literal notranslate"><span class="pre">Stock</span> <span class="pre">Market</span></code><a class="headerlink" href="#introduction-to-project-oriented-approach-stock-market" title="Link to this heading">#</a></h2>
<p><img alt="problem" src="_images/problem0.png" /></p>
<table class="table">
<thead>
<tr class="row-odd"><th class="head text-center"><p><img alt="problem" src="_images/problem0.png" /></p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td class="text-center"><p>Stock Market Problem Statement</p></td>
</tr>
</tbody>
</table>
</section>
</section>

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42 changes: 20 additions & 22 deletions _build/html/_sources/01_introduction.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -167,21 +167,21 @@
"\n",
"Now Ask yourself the following questions:\n",
"\n",
"- What needs to be done: Predict the weather conditions\n",
"- **What needs to be done**: Predict the weather conditions\n",
" - What do you mean predict the weather conditions? Do you want to predict the temperature, the humidity, the wind speed, the precipitation, the cloud cover, the visibility, the air pressure, the weather type, etc?\n",
" - In what form do you want the prediction? Do you want the prediction in the form of a class label, a probability, a value, etc?\n",
" - Does the business need predictions at day level, hour level, minute level, etc?\n",
" - How is the data going to be used? Is the data going to be used for decision making, reporting, analysis, etc?\n",
"- How it should be done: Predict the weather conditions\n",
"- **How it should be done**: Predict the weather conditions\n",
" - What kind of data do you have? Do you have historical data, real-time data, forecast data, etc? Do you have structured data, unstructured data, semi-structured data, etc?\n",
" <!-- - What kind of model do you want to build? Do you want to build a regression model, a classification model, a clustering model, etc? `More on this later!` -->\n",
" <!-- - What kind of learning do you want to apply? Do you want to apply supervised learning, unsupervised learning, reinforcement learning, etc? Which all would be applicable to the problem statement? -->\n",
"\n",
"When it comes to types of Machine Learning, the following questions are most relevant to ask:\n",
"\n",
"> - What needs to be done:\n",
"> - **What needs to be done**:\n",
"> - What do you mean predict the weather conditions? Do you want to predict the temperature, the humidity, the wind speed, the precipitation, the cloud cover, the visibility, the air pressure, the weather type, etc?\n",
"> - How it should be done:\n",
"> - **How it should be done**:\n",
"> - What kind of data do you have? Do you have historical data, real-time data, forecast data, etc? Do you have structured data, unstructured data, semi-structured data, etc?\n",
"\n",
"The above questions formulate what I call the `Matrix of Problems`.\n",
Expand All @@ -192,33 +192,31 @@
"\n",
"starting statement: \"Based on the historical data, ...\"\n",
"\n",
"| Objective/Type Of Learning | Supervised | Unsupervised |\n",
"| :------------------------: | :----------------------------------------------------------: | :--------------------: |\n",
"| Regression | predict the temperature and the humidity values | estimate the |\n",
"| Classification | predict if the day is going to be pleasent/rainy/sunny/windy | |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Objective/Type Of Learning | Supervised | Unsupervised |\n",
"| :------------------------: | :----------------------------------------------------------: | :--------------------------------------------------: |\n",
"| `Regression` | predict the temperature and the humidity values | estimate the temperature based on similar days |\n",
"| `Classification` | predict if the day is going to be pleasent/rainy/sunny/windy | estimate the day class based on similar days in past |\n",
"| `Discovery` | Not Applicable | discovering data patterns, identifying behaviors |\n",
"\n",
"The above table does not cover all the use cases like combinatorics problems, optimization problems, Anomaly detection, etc. But the above table gives a good idea of the types of problems that can be solved using `Machine Learning`.\n",
"\n",
"![map](assets/map_of_ml.png)"
"Below is a representative map for scikit learn library which provides a good set of algorithms which are used to solve ML problems. It can be used as a reference to understand the types of problems that can be solved using `Machine Learning`.\n",
"\n",
"\n",
"|![map](assets/map_of_ml.png)|\n",
"|:-:|\n",
"|Source: scikit-learn|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to Project Oriented Approach: `Stock Market`\n",
"\n",
"\n",
"![problem](assets/problem0.png)"
"| ![problem](assets/problem0.png) |\n",
"| :-----------------------------: |\n",
"| Stock Market Problem Statement |\n"
]
}
],
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