Erickramirez

Pinned Repositories

• CarND-Capstone

  Language:Python0100

• CarND-Extended-Kalman-Filter-Project-master

  Implementation of extended Kalman filter in C++ for Lidar and Radar measurements.

  Language:C++00

• CarND-Kidnapped-Vehicle-Project

  Implementation of Particle-Filter in C++ for localization.

  Language:C++0100

• CarND-MPC-Project

  Implementation of MCP (Model-Predictive-Control ) in C++ to race around the lake track.

  Language:C++0100

• CarND-Path-Planning-Project

  In this project your goal is to safely navigate around a virtual highway with other traffic that is driving +-10 MPH of the 50 MPH speed limit.

  Language:C++0100

• CarND-PID-Control-Project

  Implementation of PID control (Proportional-Integral-Derivative controller ) in C++ to race araound the lake track.

  Language:C++0100

• RoboND-DeepRL-Project

  This project is based on the Nvidia open source project "jetson-reinforcement" developed by Dustin Franklin. The goal of the project is to create a DQN agent and define reward functions to teach a robotic arm to carry out two primary objectives: Have any part of the robot arm touch the object of interest, with at least a 90% accuracy. Have only the gripper base of the robot arm touch the object, with at least a 80% accuracy.

  Language:C++1100

• Sparkify-Data-Lake-with-Apache-Spark

  This project has as output a Data Lake solution. It building an ETL pipeline that extracts their data from S3, processes them using Spark, and loads the data back into S3 as a set of dimensional tables. This will allow their analytics team to continue finding insights in what songs their users are listening to.

  Language:Python1200

• Sparkify-Data-Pipelines-with-Airflow-S3-and-Redshift

  This project has to output a Dataware house solution and create high-grade data pipelines that are dynamic and built from reusable tasks, monitored, and allow easy backfills. They have also noted that the data quality plays a big part when analyses are executed on top of the data warehouse and want to run tests against their datasets after the ETL steps have been executed to catch any discrepancies in the datasets.

  Language:Python2200

• Understanding-Customer-Churning-using-Spark-and-ML

  Spark MLlib implementation to build machine learning models with large datasets for predicting churn rates

  Language:HTML11

Erickramirez's Repositories