Artificial Intelligence

  • Bias & Variance… dilemma or compromise? - As soon as you begin to create machine learning models, you will be faced with the delicate problem of balance in the adjustment of bias and variance. In this article I try to simply explain how understand these two very important concepts.
  • Understanding Neural Networks with Tensorflow Playground - In this article, I suggest you discover a brilliant tool that allows you to better understand how a neural network works and the real impact of its main settings.
  • Markov chains - If this method of "prediction" based on probabilities and states / transitions had its heyday, it now seems less fashionable. In this article we will come back to the fundamentals of Markov chains and their application in Python.
  • Sentiment analysis on movie reviews - The purpose of this article is to show through a concrete and French case the method to perform a sentiment analysis with Python.
  • dataprep.eda: a newcomer in data analysis - In this article I show you how to use the new arrival of data analysis with Python: datapre.eda
  • NLP with Python NLTK - Find out in this article how to do NLP simply with Python and NLTK.
  • Explore your data with DataExplore - Discover in this article how to use the Open Source DataExplore tool to visualize and even manipulate your data.
  • Getting started in Auto-ML with AutoGluon - Discover Auto-ML with AutoGluon. Simple accelerator or real revolution in the way of creating machine learning models? get an idea by doing ...
  • Analyze your data with Pandas-profiling - Analyze your data effortlessly with the pandas_profiling Python library.
  • Strings Comparison - Find out in this article how to use distance algorithms and the Fuzzywuzzy library to compare strings.

Data Preparation

  • dataprep.eda: a newcomer in data analysis - In this article I show you how to use the new arrival of data analysis with Python: datapre.eda
  • Explore your data with DataExplore - Discover in this article how to use the Open Source DataExplore tool to visualize and even manipulate your data.
  • Analyze your data with Pandas-profiling - Analyze your data effortlessly with the pandas_profiling Python library.
  • Strings Comparison - Find out in this article how to use distance algorithms and the Fuzzywuzzy library to compare strings.
  • Get started with Tesseract - 122/5000 Interested in OCRs? learn how to use Tesseract (Open Source OCR) from the command line but also via Python.
  • Preparing the datasets - preparing the datasets in a Machine Learning project is a very important step that should not be neglected, otherwise you risk over evaluating your model (over-fitting) or quite simply the opposite (under fitting). In this article we will go through the essential steps for this delicate operation.
  • Variables correlation - This article shows you how to detect links between observation variables.
  • Orange Data Science Tool - Discover in this article in the form of a tutorial how this small Open-Source Data-science tool can save you a lot of time!
  • Bag of Words - To follow up on my article on the management of character strings, here is a first part which will allow us to have a progressive approach to the processing of this type of data. Far from any semantic approach (which will be the subject of a later post) we will discuss here the technique of bags of words
  • Managing string character - If you want to have an analytical approach to your data, you have of course been faced with the difficulty of using character strings. So much so that very often you have certainly had to put some aside. Lack of tools, complexity of managing complex semantics ... In this article (first in a series) we will tackle these problems and especially see how to solve them.

Modeling

  • Bias & Variance… dilemma or compromise? - As soon as you begin to create machine learning models, you will be faced with the delicate problem of balance in the adjustment of bias and variance. In this article I try to simply explain how understand these two very important concepts.
  • Markov chains - If this method of "prediction" based on probabilities and states / transitions had its heyday, it now seems less fashionable. In this article we will come back to the fundamentals of Markov chains and their application in Python.
  • Sentiment analysis on movie reviews - The purpose of this article is to show through a concrete and French case the method to perform a sentiment analysis with Python.
  • Getting started in Auto-ML with AutoGluon - Discover Auto-ML with AutoGluon. Simple accelerator or real revolution in the way of creating machine learning models? get an idea by doing ...
  • Keras to the rescue of the Titanic? - In this article we will see by practice if deep learning via keras can help us find Titanic survivors more efficiently than traditional algorithms.
  • Publish your Machine Learning models with Flask! - In this article we will see how to install and especially use the Python Flask micro-framework. very useful for publishing your Machine learning models in REST!
  • The persistence of machine learning models - In this short article you will see how in a few lines of Python code you can save your model and recall it.
  • CatBoost ! - Discover in this article how to use the latest open-source gradient boosting algorithms: the CatBoost!
  • XGBoost: The super star of algorithms in ML competition - Find out in this article why XGBoost is the star of Machine Learning competitions ... and especially how to use it!
  • Feature Scaling - This article explains in practice why and how to scale (Feature Scaling) the characteristics of a Machine Learning model implemented with Scikit-Learn.

Machine Learning

Deep Learning

NLP

Tools


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