Pablo Campos

Pablo Campos

Mentor
5.0
(14 reviews)
US$15.00
For every 15 mins
34
Sessions/Jobs
ABOUT ME
Mathematician, Economist, Data Scientist
Mathematician, Economist, Data Scientist
Spanish, English
Madrid (+01:00)
Joined December 2018
EXPERTISE
5 years experience | 1 endorsement
Used R, Python, Scala, SQL among others during the last years to perform Data manipulation, Data wrangling, Data visualization, and build...
Used R, Python, Scala, SQL among others during the last years to perform Data manipulation, Data wrangling, Data visualization, and build Machine Learning models.
RPythonScala
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8 years experience | 2 endorsements
Data manipulation, Data wrangling, Data visualization, Machine Learning models, Shiny applications
Data manipulation, Data wrangling, Data visualization, Machine Learning models, Shiny applications
1 year experience
Performed Data Analylisis and deployed Machine Learning models into production
Performed Data Analylisis and deployed Machine Learning models into production
5 years experience | 1 endorsement
Data manipulation, Data wranling, Data visualization, Machine learning models
Data manipulation, Data wranling, Data visualization, Machine learning models

REVIEWS FROM CLIENTS

5.0
(14 reviews)
Michael Osafo
Michael Osafo
August 2021
Very helpful
Removed User
Removed User
November 2019
Nice and honest person to deal with. Good at what he does. All the jobs that he has done for me so far, are all excellent, no complain.
Kyle Lesinger
Kyle Lesinger
August 2019
Pablo is very skilled with R studio and I know that I can trust his work (multi-time user with Pablo). He is prompt and attentive in his coding.
amanda Morrow
amanda Morrow
July 2019
Thank you, Pablo,​ for your help once again!
amanda Morrow
amanda Morrow
July 2019
Pablo was amazing! He is very informative and ensured he not only helped me resolve my issue, he ensured I understood. Plus he followed up and went the extra mile to help.
amanda Morrow
amanda Morrow
July 2019
Pablo was a great help! Very nice and easy to communicate with. My Problem was solved quickly. Thank you
Kyle Lesinger
Kyle Lesinger
July 2019
Excellent assistance provided with my code. Quick with starting on my work as well.
Quail
Quail
June 2019
If you need any assistance for R, I would highly recommend Pablo. He is reliable and always in contact.
Sylvie Borau
Sylvie Borau
June 2019
Great job! Very professional. Excellent expertise in RStats
Kaity Castel
Kaity Castel
January 2019
Extremely helpful and reliable. Completed everything in a timely manner and kept in contact along the way. Highly recommend Pablo!
SOCIAL PRESENCE
GitHub
MLPR_A1
UoE - MLPR Assignment 1
Jupyter Notebook
0
1
kernel_methods
R and Python codes for some Kernel Methods
R
0
2
EMPLOYMENTS
Data Scientist
BBVA Data & Analytics
2018-11-01-Present
BBVA Data & Analytics is a center of excellence in financial data analysis. https://www.bbvadata.com/
BBVA Data & Analytics is a center of excellence in financial data analysis. https://www.bbvadata.com/
Python
SQL
Scala
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Python
SQL
Scala
R
Apache Spark
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PROJECTS
Generative Adversarial Networks for Generating Novel Policies and Rewards in Reinforcement LearningView Project
2018
In the Reinforcement Learning (RL) framework, two main elements are given by the policies, which define some agent’s way of behaving, and ...
In the Reinforcement Learning (RL) framework, two main elements are given by the policies, which define some agent’s way of behaving, and the rewards, which define the goals in a RL problem. Besides, Generative Adversarial Networks (GANs) [Goodfellow et al., 2014] are a class of generative models that have been regarded as the most interesting idea in the last years in the Machine Learning (ML) field, that are capable of produce realistic data in a wide range of domains. Nevertheless, the use of GANs in the context of RL remains a little explored research area. In this project we explore the idea of applying GANs in the context of RL in order to generate policies and rewards and also the idea of accelerating new learning by using the learned generative models. Using a simple RL environment, we show that it is completely possible to train GANs in order to be able to produce realistic policies and rewards that can be used independently to eventually accelerate new learning.
Python
Reinforcement Learning
Deep Learning
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Python
Reinforcement Learning
Deep Learning
TensorFlow
Keras
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Econometric and Statistical Learning Models for Cancer Detection with Data from Computational GenomicsView Project
2017
R
Machine learning
Bioinformatics
R
Machine learning
Bioinformatics