Philippe ROSSIGNOL
Big Data Consultant: Architect, Data Engineer, Certified in Data Science
Communicating with an Agile Culture
Approache driven Use Cases and Data
Prepare the Data, create the Dashboards
Certifications in Data Science
Domains: Bank, Insurance
Driving License
Bordeaux (33000) France
Professional Status
Project initiator
Open to opportunities
Machine Learning Regression (6 weeks) - Certification (100%)
University of Washington (MOOC Coursera)
February 2016
to April 2016
Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed continuous-valued response.
- This course covers the major topics such as:
Simple Linear Regression.
Multiple Regression.
Assessing Performance.
Ridge Regression.
Feature Selection & Lasso.
Nearest Neighbors & Kernel Regression.
- This course teaches how to:
Describe the input and output of a regression model.
Compare and contrast bias and variance when modeling data.
Estimate model parameters using optimization algorithms.
Tune parameters with cross validation.
Analyze the performance of the model.
Describe the notion of sparsity and how LASSO leads to sparse solutions.
Deploy methods to select between models.
Exploit the model to form predictions.
Build regression models to make predictions.
Implement these techniques in Python.
Machine Learning Foundations: A Case Study Approach (6 weeks) - Certification (100%)
University of Washington (MOOC Coursera)
January 2016
to February 2016
Implementing intelligent applications using regression, classification, nearest neighbor search, clustering, collaborative filtering, and deep learning... With these ML methods, intelligent applications can perform predictions, personalized recommendations and retrieval, learn non-linear features that improve the accuracy of the solutions, and much more.
- This course covers the major topics such as:
Regression.
Classification.
Clustering & Retrieval.
Recommender Systems & Dimensionality Reduction.
ML Capstone: An Intelligent Application with Deep Learning.
- This course teaches how to:
Become a machine learning expert, ready to develop and deploy new intelligent applications.
Machine Learning (11 weeks) - Certification (100%)
Stanford University (MOOC Coursera)
November 2015
to January 2016
Linear Regression with one and multiple variables, Gradient Descent and Cost Function, Linear Algebra, Logistic Regression, Regularization, Multiclass Classification, Solving the Problem of Overfitting, Neural Networks and Backpropagation, Evaluating a Learning Algorithm, Bias vs. Variance, Support Vector Machines, Unsupervised Learning, Dimensionality Reduction, Anomaly Detection, Recommender Systems, Density Estimation, Multivariate Gaussian Distribution, Collaborative Filtering, Low Rank Matrix Factorization, Gradient Descent with Large Datasets, Photo OCR.
Big Data Science Training - Spark for Data Scientists (2 days)
Xebia Training - Paris
September 2015
“Hands-on” training focusing on Spark Machine Learning and DataFrames in order to develop Machine Learning Pipelines (e.g: with objects like Transformers, Estimators, Cross-Validators and others.).
Cloudera-Xebia three days Spark course enables participants to build complete, unified Big Data applications combining batch, streaming, and interactive analytics on all their data. With Spark, developers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.
Big Data Training - Introduction to Data Science with Hadoop (4 days)
ExitCertified - Montreal
April 2014
Introduction to Data Science and Machine Learning (Clustering, Classification and Recommenders). Creating of Hybrid Recommenders with Apache Hadoop, Apache Mahout, Hive, and R by using algorithms such as Naïve Bayes, Tanimoto coefficient, Euclidean distance, etc. Labs were based on a fictive movies company use case, similar to Netflix.
MapReduce development jobs with Hadoop (in Java and Python)
Business Intelligence SAS Trainings - The whole SAS Platform
SAS Institute
January 2011
to September 2011
SAS Administration and SAS Developer trainings:
- SAS Programming Levels I and II, Macro SAS Language, SAS Java API, SAS Enterprise Guide GUI, Architecture and components of the SAS platform, Administration of SAS platform
Master Diploma - In Computer Science and Development
ENST - Brest (FRANCE)
September 1996
to September 1997
Micro-Electronic Diploma - NVQ Level 5
I.X.L Laboratory - University of Bordeaux (FRANCE)
September 1993
to 1994
Micro-Electronics
Electronic Diploma - Higher Education
Night School - University of Bordeaux (FRANCE)
September 1992
to 1993
Electrical and Electronics Engineering
Electronic Diploma - BTEC Higher National Diploma
Night School - University of Bordeaux (FRANCE)
September 1990
to 1992
Electrical and Electronics Engineering
Industrial Electronics Technology/Technician