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Hi there, This is Eduardo Rivero 👍🚀💻📌📊📈

I'll be working full code into 👇🏼 - plan your trip:

Big Data & Data Sciences

"The genius's brand is clarity, the failure's brand is confusion" - Brian Tracy

Did you know that? I'm New in this page! Performance & Development are my goals...

Designer

  • Driving Data Science, Machine Learning and Deep Learning with Python, R and now I will be driving Scala tech principles!!
  • Database analysis with SQL Server.
  • Scripts development and implementation in automated supervised learning models (Regressions, Classifications and time series) and unsupervised (Clustering) with Programming languages such as Python and R.
  • Modeling mean libraries: Pandas, NumPy, Matplotlib, Scikit-Learn, Seaborn, Keras, TensorFlow, dplyr, ggplot2, reader, VIM, lifecycle, lubricate, etc..
  • Data Analysis through collection, transformation, filtering and cleaning with Jupyter Notebook and Databricks.
  • Databases used: structured books, flat files, AVRO, Parquet, JSON and ORC formats.
  • Connection string with Apache Hadoop (HDFS, YARN, MapReduce).
  • Essential touch development with SCALA.
  • Learning Azure Machine Learning, with Data Factory and Synapse Analytics.
  • Curious about AI + Problem Solving.

Let's connect:


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Eduardo Rivero's Projects

azureml-examples icon azureml-examples

Official community-driven Azure Machine Learning examples, tested with GitHub Actions.

cuda-samples icon cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

handsonscala icon handsonscala

Discussion and and code examples for the book Hands-on Scala Programming

isbt icon isbt

A Jupyter Kernel for sbt

ml-basics icon ml-basics

Exercise notebooks for Machine Learning modules on Microsoft Learn

mongodb-labs icon mongodb-labs

Learn MongoDB through interactive labs and other fun stuff.

neural-network_random-forest_support-vector-machine icon neural-network_random-forest_support-vector-machine

Collect the datasets you received load it using pandas . Apply necessary pre-processing steps on it . Support Vector Machine (SVM), Neural Network (Multilayer Perceptron Classifier) and Random Forest are three very popular machine learning classifiers. Divide the dataset into 8:2 train-test split and perform Support Vector Machine, Neural Network

petspotr icon petspotr

Demo application that showcases a modern, cloud-native application

pythonnet icon pythonnet

Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.

spark icon spark

Apache Spark - A unified analytics engine for large-scale data processing

sparkmagic icon sparkmagic

Jupyter magics and kernels for working with remote Spark clusters

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