Christopher De Leon's repositories
Xilinx-Artix-7-PCIe-Project
Created project using a PCIe root-complex and endpoint on a Xilinx Artix-7.
Altera-DE1-VGA-Interface
Integrated and programmed a VGA Interface using the Altera DE1 to output in synchronization with a custom programmed finite-state machine.
edX-xdemo-day-Machine-Learning-Algorithmic-Trading-Bot
Machine Learning and its Implementation into Algorithmic Trading.
Exo-Remedy-Robotic-Hand-Rehabilitation-Therapy-Tool
Integrated an Arduino Uno microcontroller into a mixed-signal circuit with use of linear actuators, motor controllers, force sensors and flex sensors.
Module-10-Unsupervised-Learning-Challenge
In this assignment, I'll work primarily with the K-means algorithm, one of the most popular unsupervised learning algorithms that groups similar data into clusters. I'll build on this by speeding up the process using principal component analysis (PCA), which employs many different features.
Module-12-Supervised-Learning-Challenge
In this assignment, I’ll use various techniques to train and evaluate models with imbalanced classes. I’ll use a dataset of historical lending activity froma peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
Module-13-Neural-Networks-Challenge
In this module, I'll explore and implement neural networks using the TensorFlow platform and Keras in Python. I’ll learn about the history of computational neurons, how neural networks apply to deep learning and what the cost and benefits of neural networks are.
Module-14-Algorithmic-Trading-Challenge
In this module, I learned how to use Pandas and the JupyterLab IDE to collect, prepare and analyze financial data.
Collaboration-9-14-23
Columbia Bootcamp collaboration demo - testing GitHub features with multiple users!
Markdown-Example-9-14-23
Columbia Bootcamp demo - testing Markdown features!
Module-1-Markdown-Challenge
This assignment challenged me to polish my fintech research skills by accessing reports, publications and online resources that fintech professionals use to evaluate the industry. It also helped me situate the techniques andtechnologies that I will use in this course. Finally, it gave me practice presenting & speaking about fintech strategy/tech.
Module-11-Timed-Series-Challenge
In this assignment, I've been tasked with analyzing the company's financial and user data in clever ways to help the company grow. So, I want to find out if the ability to predict search traffic can translate into the ability to successfully trade the stock.
Module-15-Roboadvisors-Challenge
In this module, I’ll combine all my machine learning skills with conversational user interfaces (CUIs) and cloud services.
Module-19-Blockchain-Wallets-Challenge
In this module, I'll learn the basics of blockchain transactions and how to execute and secure them.
Module-2-Python-Challenge
I applied the concepts I’ve learned to complete the required PyBank Python activity and stretched my skills even further by doing the the optional PyRamen Python activity. Both activities presented a real-world situation in which my newfound Python skills will come inhandy.
Module-20-Solidity-Challenge
In this module, I'll learn about smart contracts and how I can build them by using Solidity. Solidity is a programming language for implementing smart contracts on the Ethereum blockchain. Smart contracts are agreements that special computer programs, which can run on a blockchain, create.
Module-21-Advanced-Solidity-Challenge
In this module, I’ll learn the difference between fungible and non-fungible tokens, as well as their standards as defined by the Ethereum community. I’ll use this knowledge to build smart contracts that use the same techniques blockchain companies use to offer new tokens and hold ICOs.
Module-4-Pandas-Challenge
In this assignment, I’ll use quantitative analysis techniques with Python and Pandas, and I’ll determine which portfolio is performing the best across multiple areas:volatility, returns, risk, and Sharpe ratios. In this module, I learned how to use Pandas and the JupyterLab IDE to collect, prepare, and analyze financial data.
Module-5-APIs-Challenge
In this assignment, I will use the information from the Monte Carlo simulation to answer questions about the portfolio in my Jupyternotebook. In this module, I learned how to measure and analyze long-term future performance using Application Programming Interfaces (APIs) and Monte Carlo simulations.
Module-6-PyViz-Challenge
In this assignment, my job is to use my data visualization skills, including aggregation, interactive visualizations and geospatial analysis, to find properties in the San Francisco market that are viable investment opportunities.
Module-7-SQL-Challenge
In this homework assignment, I will apply my new SQL skills to analyze historical credit card transactions and consumption patterns in order to identify possible fraudulent transactions.