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<!DOCTYPE html>
<html lang="zxx" class="no-js">
<head>
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<li><a href="index.html">Home</a></li>
<li class="menu-active"><a href="lessons.html">Lessons</a></li>
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<h1 class="text-white">
Lessons
</h1>
<p>View our video lessons in the free course here.</p>
<div class="link-nav">
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<a href="index.html">Home </a>
<i class="lnr lnr-arrow-right"></i>
<a href="lessons.html">Lessons</a>
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<h1>Theory Notebooks</h1>
<p>
These are our theory notebooks for the course. These GitHub notebooks discuss the conceptual knowledge behind several essential machine learning concepts. To interact with
the notebooks, click the "Open in Colab" button.
</p>
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<h4>Lesson 1 - Overview of AI/ML</h4>
<br>
<div class="wow fadeIn" data-wow-duration="1s" data-wow-delay=".1s">
<p>
In this lesson, we go over a brief introduction to AI and ML as a whole, the course overview, and getting set up for the next few weeks of coding.
<br><br>
<a href="https://docs.google.com/presentation/d/1KBm104jjbzx_Gi9gJN_NclTDgygxyPQIRZlMA8botlE/edit">Link to Slides</a>
<br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials">Link to Github Repository with our Lesson Materials</a>
</p>
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<iframe width=100% height="315" src="https://www.youtube.com/embed/RHcKxr0cbj4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<h4>Lesson 2 - Machine Learning Theory</h4>
<br>
<div class="wow fadeIn" data-wow-duration="1s" data-wow-delay=".1s">
<p>
In this lesson, we go over the fundamentals of machine learning theory and do a small machine learning project in Python to show how we execute these ideas in code.
<br><br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/2_ConceptsML.ipynb">Theory Notebook</a>
<br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/projects/1_LinearRegressionProject.ipynb">Coding Project Notebook</a>
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<iframe width=100% height="315" src="https://www.youtube.com/embed/8oROUOisDzI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<h4>Lesson 3 - Deep Learning Theory and Concepts</h4>
<div class="wow fadeIn" data-wow-duration="1s" data-wow-delay=".1s">
<p>
In this lesson, we go over the fundamental concepts of deep learning, especially on how neural networks work. We discuss the theory behind these ideas and implement them in code for you to follow along with.
<br><br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/3_DeepLearning.ipynb">Deep Learning Notebook</a>
</p>
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<h4>Lesson 4 - CNN Theory</h4>
<br>
<div class="wow fadeIn" data-wow-duration="1s" data-wow-delay=".1s">
<p>
In this lesson, we discover the theory behind Convolutional Neural Networks (CNNs), a very common type of neural network often used for image classification. We discuss their advantages compared to traditional neural networks and how we implement them in machine learning.
<br><br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/4_CNNTheory.ipynb">CNN Theory Notebook</a>
<br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/projects/7_CatDogCNN.ipynb">CNN Project Notebook</a>
</p>
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<iframe width=100% height="315" src="https://www.youtube.com/embed/pGWwDAiB3Ic" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<h4>Lesson 5 - CNN and NN Examples in Keras</h4>
<br>
<div class="wow fadeIn" data-wow-duration="1s" data-wow-delay=".1s">
<p>
In this video, we go into the code behind many of the concepts we've discussed, specifically with dense neural networks and CNNs. We code a bare-bones neural network with 3 layers and a CNN that classifies pictures of cats and dogs.
<br><br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/5_KerasNueralNetwork.ipynb">NN Project Notebook</a>
<br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/projects/7_CatDogCNN.ipynb">CNN Project Notebook</a>
</p>
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<h4>Lesson 6 - Natural Language Processing Theory and Examples</h4>
<div class="wow fadeIn" data-wow-duration="1s" data-wow-delay=".1s">
<p>
In this video, we go into how Natural Language Processing (NLP) works, which is responsible for applying machine learning to communication and human text (e.g. Siri, Google Search, etc). We talk about the theory behind NLP, followed up by a short coding project to classify emails as spam or not-spam
<br><br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/6_Intro_to_NLP.ipynb">NLP Theory Notebook</a>
<br>
<a href="https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/projects/8_NLPModel.ipynb">NLP Project Notebook</a>
</p>
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<h1>Lesson Videos</h1>
<p>
These are our videos for the course. If you enjoyed the videos, please like and comment!
<br>
<a href="https://youtube.com/playlist?list=PLWj-3LXfs4r0pD_fzYXUa2PFJ5JHKwaaW">Here is the link to the YouTube playlist containing all the videos.</a>
</p>
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<h4>Machine Learning Theory</h4>
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<h4>Deep Learning Theory</h4>
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<br>
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<h4>CNN Theory</h4>
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</div>
</div>
</div>
<br>
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<h4>NLP Theory</h4>
<iframe width=100% height="500" src="files/theory-notebooks/6_Intro_to_NLP.html"></iframe>
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<h4>Quick Links</h4>
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<h4>Resources</h4>
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<li><a href="https://github.com/Intro-Course-AI-ML/LessonMaterials">Github</a></li>
<li><a href="https://www.youtube.com/playlist?list=PLWj-3LXfs4r0pD_fzYXUa2PFJ5JHKwaaW">Lectures</a></li>
<li><a href="https://medium.com/better-programming/how-to-teach-ai-and-ml-to-middle-schoolers-34bf59262ea8">Article</a></li>
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