Miguel Angel Nieto's repositories
Cloud-DevOps-Engineer-Capstone-Project
Final project of the DevOps Udacity Nanodegree
Finding-Donors-for-CharityML
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.
First-Neural-Network
Built a neural network from scratch to carry out a prediction problem on a real dataset.
Train-a-Smartcab-to-Drive
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.
Build-a-Game-Playing-Agent
Created an AI that beats human opponents in the game of Isolation using Minimax, Alpha-Beta Search, and Iterative Deepening.
Creating-an-AI-Agent-to-solve-Sudoku
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.
Creating-Customer-Segments
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.
Design-an-A-B-test
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.
Explore-and-Summarize-Data
Investigated a dataset using R and exploratory data analysis techniques, exploring both single variables and relationships between variables.
Face-Generation
Used generative adversarial networks to generate new images of faces.
genny
🧞♀️ Grants 3 wishes. As long as those wishes are to generate load 🧞♂️
Identify-Fraud-from-Enron-Email
Identified which Enron employees are more likely to have committed fraud using machine learning and public Enron financial and email data.
Image-Classification
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.
Implement-a-Planning-Search
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.
Investigate-a-Dataset
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.
Language-Translation
Trained a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
Machine-Learning-Capstone-Project
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.
Machine-Translation
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.
Make-Effective-Data-Visualization
Created a polished data visualization that tells a story, allowing a reader to explore trends or patterns.
markdown-pp
Preprocessor for Markdown files to generate a table of contents and other documentation needs
Object-Classification
Implemented a convolutional neural network to classify images from the CIFAR-10 dataset.
Predicting-Boston-Housing-Prices
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.
Project-ML-Microservice-Kubernetes
Deploy a Housing price predictor using Kubernetes
Quantum-Marriage-Proposal
A marriage proposal in Quantum Computing
Test-a-Perceptual-Phenomenon
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.
TV-Script-Generation
Generated own Simpsons TV scripts using RNNs.
Udagram-CloudFormation-Deployment
Udacity's DevOps Nanodegree CloudFormation Project
Wrangle-OpenStreetMap-Data
Chose a region and used data munging techniques to assess the quality of the data for validity, accuracy, completeness, consistency and uniformity.
YCSB
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