My 5 Machine Learning projects that I've built as part of my freeCodeCamp assignment.

Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD based 3D convolutional neural networks (CNNs) for predicting elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2,170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Magpie features and ECD descriptors achieved the best 5-fold cross-validation performance. More importantly, we showed that our ECD based CNN models can achieve significantly better extrapolation performance when evaluated over non-redundant datasets where there are few neighbor training samples around test samples. As additional validation, we evaluated the predictive performance of our models on 329 materials of space group Fm-3m by comparing to DFT calculated values, which shows better prediction power of our model for bulk modulus than shear modulus. Due to the unified representation power of ECD, it is expected that our ECD based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

FCC Machine Learning: Book Recommendation Engine using KNN

FCC Machine Learning: Rock Paper Scissors

FCC Machine Learning: Cat and Dog image classifier

Projects from the 'Machine Learning with Python' course from FreeCodeCamp

Solutions for the FreeCodeCamp "Machine Learning with Python" Challenges

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This repository maintains a copy of my projects done to secure Free Code Camp's Machine Learning with Python Certification.

Repository containing course material and projects completed as part of the FreeCodeCamp "Machine Learning with Python" certification.

5 Projects I did through the FreeCodeCamp.org - Machine Learning in Python course

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FCC Machine Learning: Health Costs Calculator

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FCC Machine Learning: Neural Network SMS Text Classifier

A freeCodeCamp.org Machine Learning with Python project. Linear Regression Health Costs Calculator

Data Science Capstone : Machine Learning applied to FCC Net Neutrality comments

A Java FCC Neural Network implementation I wrote as a personal project to introduce myself to machine learning.

My Solution for the fcc_cat_dog Problem by Free Code Camp for the course Machine Learning with Tensorflow.

Objective: Build a CodePen.io app that is functionally similar to this: https://codepen.io/freeCodeCamp/full/qRZeGZ. Fulfill the below user stories and get all of the tests to pass. Give it your own personal style. You can use any mix of HTML, JavaScript, CSS, Bootstrap, SASS, React, Redux, and jQuery to complete this project. You should use a frontend framework (like React for example) because this section is about learning frontend frameworks. Additional technologies not listed above are not recommended and using them is at your own risk. We are looking at supporting other frontend frameworks like Angular and Vue, but they are not currently supported. We will accept and try to fix all issue reports that use the suggested technology stack for this project. Happy coding! User Story #1: I can see a wrapper element with a corresponding id="quote-box". User Story #2: Within #quote-box, I can see an element with a corresponding id="text". User Story #3: Within #quote-box, I can see an element with a corresponding id="author". User Story #4: Within #quote-box, I can see a clickable element with a corresponding id="new-quote". User Story #5: Within #quote-box, I can see a clickable a element with a corresponding id="tweet-quote". User Story #6: On first load, my quote machine displays a random quote in the element with id="text". User Story #7: On first load, my quote machine displays the random quote's author in the element with id="author". User Story #8: When the #new-quote button is clicked, my quote machine should fetch a new quote and display it in the #text element. User Story #9: My quote machine should fetch the new quote's author when the #new-quote button is clicked and display it in the #author element. User Story #10: I can tweet the current quote by clicking on the #tweet-quote a element. This a element should include the "twitter.com/intent/tweet" path in its href attribute to tweet the current quote. User Story #11: The #quote-box wrapper element should be horizontally centered. Please run tests with browser's zoom level at 100% and page maximized. You can build your project by using this CodePen template and clicking Save to create your own pen. Or you can use this CDN link to run the tests in any environment you like: https://cdn.freecodecamp.org/testable-projects-fcc/v1/bundle.js Once you're done, submit the URL to your working project with all its tests passing. Note: Twitter does not allow links to be loaded in an iframe. Try using the target="_blank" or target="_top" attribute on the #tweet-quote element if your tweet won't load. target="_top" will replace the current tab so make sure your work is saved.

My notes and follow along practice from freecodecamp.org/learn Machine Learning with Python.

Python Notebook from Free Code Camp

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Storage of Jupyter files from FreeCodeCamp's Machine Learning course.

Solution Code for Machine Learning certification