Miguel Angel Nieto's Projects
Created an AI that beats human opponents in the game of Isolation using Minimax, Alpha-Beta Search, and Iterative Deepening.
Final project of the DevOps Udacity Nanodegree
Created an AI to solve Diagonal Sudokus using constraint propagation and search techniques. Additionally, taught the agent to use the Naked Twins advanced Sudoku strategy.
Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.
Designed an A/B test, including which metrics to measure and how long the test should be run. I also analyzed the results of an A/B test that was run by Udacity, recommended a decision, and proposed a follow-up experiment.
Built a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline.
Built an algorithm to identify canine breed given an image of a dog. If given image of a human, the algorithm identifies a resembling dog breed.
Investigated a dataset using R and exploratory data analysis techniques, exploring both single variables and relationships between variables.
Used generative adversarial networks to generate new images of faces.
Built an end-to-end facial keypoint recognition system. Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition. The completed project takes in any image containing faces and identifies the location of each face and their facial keypoints.
Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.
Built a neural network from scratch to carry out a prediction problem on a real dataset.
🧞♀️ Grants 3 wishes. As long as those wishes are to generate load 🧞♂️
Identified which Enron employees are more likely to have committed fraud using machine learning and public Enron financial and email data.
Classified images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset was preprocessed, then trained a convolutional neural network on all the samples. I normalized the images, one-hot encoded the labels, built a convolutional layer, max pool layer, and fully connected layer.
Used logic and planning techniques to create an AI that finds the most efficient route to route cargo around the world to their respective destinations. This project used a combination of propositional logic and search along with A* heuristics to find optimal planning solutions.
Posed a question about a dataset, then used NumPy and Pandas to answer that question based on the data and created a report to share the results.
Trained a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
Identified a relevant real-world problem that can be solved using machine learning, and modeled it using techniques learned throughout the Nanodegree. Presented the best solution achieved, discussed its strengths and weaknesses, and scope for future work.
Built a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline accepts English text as input and returns the French translation.
Created a polished data visualization that tells a story, allowing a reader to explore trends or patterns.
Preprocessor for Markdown files to generate a table of contents and other documentation needs
Implemented a convolutional neural network to classify images from the CIFAR-10 dataset.
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
Deploy a Housing price predictor using Kubernetes
A marriage proposal in Quantum Computing
Built a system that can recognize words communicated using the American Sign Language (ASL). Trained a set of Hidden Markov Models (HMMs) using part of a preprocessed dataset of tracked hand and nose positions extracted from video to try and identify individual words from test sequences. Experimented with model selection techniques including BIC, DIC, and K-fold Cross Validation.
Analyzed the Stroop effect using descriptive statistics to provide an intuition about the data, and inferential statistics to draw a conclusion based on the results.
Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
React
Typescript
Django
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Facebook
Microsoft
Google
Alibaba
D3
Tencent