Jaime Gonzalez
Garcia-Bernardo

Forest cover classification

This project is a machine learning project that uses deep learning to predict forest cover type based on cartographic variables. The forest cover type is determined by data from the US Forest Service, and the independent variables are derived from data obtained from the US Geological Survey and USFS. The goal of the project is to develop a classifier for this multi-class classification problem using TensorFlow with Keras. The project also aims to use hyperparameter tuning to improve the performance of the model, and to create clean and modular code. Technologies used in the project include Python, Jupyter notebooks, pandas, numpy, TensorFlow, scikit-learn, seaborn, matplotlib, and git.

Map data scraper

In this project I created a scraper that accesses a map provided by the spanish ministry of telecoms and saves all of the useful data in it on mobile towers in Spain in an SQL database table and in a CSV file. The map is located here. In this project I created a program that allows users to access and scrape a map provided by the Spanish Ministry of Telecoms and save the useful data on mobile towers in Spain in an SQL database table and in a CSV file. The program is written in Python and uses Jupyter Notebooks for computing, SQL for managing the database, and Git for file version control.

Regulator data downloader

In this project, I developed a computer program using Python, Pandas, and Jupyter notebooks that automates the process of downloading data from the Spanish telecoms regulator. The program creates a folder to store the downloaded data, allowing users to save time and effort by avoiding the need to manually download the data. This program has the potential to save a telecoms strategy consulting firms significant amounts of time and effort. The program can be accessed by forking the repository and running the provided Jupyter notebook file. Additionally, the repository includes notebook files for creating useful plots from the downloaded data.

ML model Google Cloud deployment

In this project, I deployed a machine learning web application called "Food Vision" on Google Cloud that classifies images of up 12 different types of food. The web application uses Streamlit and uses Google Cloud's App Engine and AI Platform to classify images of food. By doing this project I learned how to deploy a Streamlit-powered web application using Google Cloud, how to use App Engine and AI Platform, and how to debug Streamlit deployments. I also learned about the cost of running machine learning apps on Google Cloud. The project uses technologies such as Google App Engine, AI Platform, Storage, Colab notebooks, Git Bash, and Git for version control.

Habit tracker web app

In this project, I created a web page that hosts a habit tracker for its users. The habit tracker is based on the one proposed by James Clear in his book 'Atomic Habits'. I used HTML, CSS, and JavaScript to create the web page, and it has a table layout with interactive onclick events. The goal of the habit tracker is to help users develop positive habits and achieve their goals by providing visibility and accountability for their actions. Users can track a wide range of habits, and by regularly monitoring their habits, they can gain insights into their behavior and identify areas for improvement. I use it regularly to track the sports and exercise I do.

Responsive web design projects

In these projects, hosted on CodePen, I created responsive websites using HTML and CSS:

• JS technical documentation page
• Dr. Norman Borlaug tribute page
• FreeCodeCamp survey form
• Habit tracker web app

Javascript algorithms and data structures projects

In these projects, hosted on freeCodeCamp, I implemented solutions to algorithmic problems using JavaScript:

• Palindrome checker
• Roman numeral converter
• Caesar's cipher
• Telephone number validator
• Cash register

Front end libraries projects

In these projects, hosted on CodePen, I created websites using React.js, a JavaScript library used to build user interfaces for web and mobile applications:

• Random quote machine
• Markdown previewer with React.js
• Drum machine with React.js
• Javascript calculator with React.js
• Pomodoro clock with React.js

Data visualization projects

In these projects, hosted on CodePen, I created interactive visualizations using D3.js, an open-source JavaScript library for web browsers.:

• Kickstarter pledges tree map
• US Educational Attainment choropleth map
• Monthly Global Land-Surface Temperature heat map
• Doping in Professional Bicycle Racing scatter plot
• US GDP (1947-2015) bar chart

Back End Development and APIs projects

In these projects, hosted on Replit, I created APIs using Node.js and Express.js. Node.js is a JavaScript runtime, and Express.js is a web framework for Node.js:

• File metadata
• Exercise tracker
• URL shortener
• Header parser
• Timestamp

Information security projects

In these projects, hosted on Replit and Boilerplate, I applied the principles of information security to create secure applications:

• Stock price checker
• Anonymous message board
• Port scanner
• SHA-1 password cracker
• Secure real time multiplayer game

Scientific computing projects with Python

In these projects, hosted on Replit, I used Python to perform scientific computing tasks such as data analysis, arithmetic operations, and probability calculations:

• Arithmetic Formatter
• Time Calculator
• Budget App
• Polygon Area Calculator
• Probability Calculator

Quality Assurance projects

In these projects, hosted on Replit, I created web applications with tests using Jest. Jest is a JavaScript testing framework developed by Facebook for testing JavaScript code:

• American to British translator
• Sudoku solver
• Library
• Issue tracker
• Metric to imperial converter

Data analysis with Python projects

In these projects, hosted on Replit, I used Python to analyze and visualize data:

• Mean-Variance-Standard Deviation Calculator
• Demographic Data Analyzer
• Medical Data Visualizer
• Page View Time Series Visualizer
• Sea Level Predictor

Machine learning specialization projects

In this specialization delivered by Stanford's famous Andrew Ng via his company Deeplearning.AI I built and trained machine learning models in Octave and Python using NumPy and scikit-learn. The projects covered supervised learning (regression, binary and multi-class classification) unsupervised learning (clustering, anomaly detection, recommender systems with collaborative filtering and content-based deep learning, deep reinforcement learning)

• Supervised Machine Learning: Regression and Classification
• Advanced Learning Algorithms
• Unsupervised Learning, Recommenders, Reinforcement Learning

Deep learning specialization projects

In these projects I built and trained deep neural networks from their most basic elements using Python and applied them to various tasks, such as visual detection, recognition and natural language processing:

• Neural Networks and Deep Learning
• Hyperparameter tuning, Regularization and Optimization
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

TensorFlow developer certificate projects

In these projects I used TensorFlow to build different types of machine learning models suited to different types of applications, such as computer vision, classification, regression, natural language processing or time series prediction:

• Intro to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
• Convolutional Neural Networks in TensorFlow
• Natural Language Processing in TensorFlow
• Sequences, Time Series and Prediction

Tensorflow data and deployment certificate projects

In these projects, I deployed machine learning models in browsers and mobile applications, gained experience with TensorFlow data services and APIs, as well as with techniques for processing unstructured data and maintaining data privacy, using tools such as TensorFlow Serving, TensorFlow Hub, and TensorBoard.

• Browser-based Models with TensorFlow.js
• Adanced deployment scenarios
• Data Pipelines with TensorFlow Data Services
• Device-based Models with TensorFlow Lite

Google Cloud Platform pro machine learning engineer projects

Click on each line to expand it and see the labs associated with each. You can find all labs here:

• Google Cloud Big Data and Machine Learning Fundamentals

  Exploring a BigQuery Public Dataset

  Creating a streaming data pipeline for a Real-Time dashboard with Dataflow

  Predicting Visitor Purchases Using BigQuery

  Predicting loan risk with AutoML
• How Google does Machine Learning

  Using an image dataset to train an AutoML model

  Training an AutoML video classification model

  Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model
• Launching into Machine Learning

  Improve the quality of your data

  Explore the data using Python and BigQuery

  Introduction to linear regression

  Training an AutoML classification model (Structured data)

  Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)

  Using the BigQuery ML hyperparameter tuning to improve model performance
• TensorFlow on Google Cloud

  TensorFlow Dataset API

  Classifying structured data using Keras preprocessing layers

  Introducing the Keras Sequential API on Vertex AI Platform

  Build a DNN using the Keras Functional API on Vertex AI Platform

  Making new layers and models via subclassing

  Training at scale with the Vertex AI Training Service

Machine Learning Engineering for Production (MLOps) Specialization projects

Click on each line to expand it and see the labs associated with each. You can find all labs here:

• Deploying Machine Learning Models in Production

  Intro to Docker and installation

  First look at Tensorflow Serving with Docker

  Serve a model with TensorFlow Serving

  Deploy a ML model with fastAPI and Docker

  Practice Kubernetes in your Local Environment

  Load testing servers with Docker Compose and Locust

  Building ML Pipelines with Kubeflow

  Model versioning with Tensorflow Serving and Docker

  Intro to CI/CD pipelines with GitHub Actions

  Developing Custom TFX Components

Forest cover classification

This project is a data science and machine learning project that uses deep learning to predict forest cover type based on cartographic variables. The forest cover type is determined by data from the US Forest Service, and the independent variables are derived from data obtained from the US Geological Survey and USFS. The goal of the project is to develop a classifier for this multi-class classification problem using TensorFlow with Keras. The project also aims to use hyperparameter tuning to improve the performance of the model, and to create clean and modular code. Technologies used in the project include Python, Jupyter notebooks, pandas, numpy, TensorFlow, scikit-learn, seaborn, matplotlib, and git.

Analyzing-Wikipedia-Clickstreams-with-PySpark

In this project I clean and analyze a sample of the English language Wikipedias Clickstream data from January 2018 using PySpark SQL.

Analyzing-Common-Crawl-Data-with-PySpark

In this project, I analyze a small portion of a dataset published by the Common Crawl using PySpark

stackoverflow-survey-data-analysis

In this repo I handle missing data and perform data wrangling, cleaning and tidying techniques for a stack overflow survey and then analyse it

Impact-of-Cover-Crops-on-Wheat-Crop-Yields

In this project I use inverse probability of treatment weighting (IPTW) to determine whether the use of cover crops causes an increase in crop yields.

Cleaning-US-Census-Data

In this repo I agregate data from multiple files from the US census and I create histograms and scatterplots of the data to obtain insights from it.