Data engineering

No account yet ?

No predefined data

Reinforcement learning

Genetic algorithm

What is the certification of a database

Data

Data lake

Open data

What web databases are available ?

Data bases

Data is the first pillar of Machine Learning.

External data sets can serve as a basis for a project, or complement those that can be used internally.

The unstructured data that is collected is raw, and it needs to be cleaned and formatted in order to be as relevant as possible for analysis.

Data becomes structured when you know what each piece of data contains and where to find it.

It is therefore possible to enrich the database with metadata that correspond to a set of specific details about the data in order to better understand them.

It is necessary to create a database representative of the reality on the ground.

The representativeness of the data is one of the key success factors in the development of an AI.

Without it, AI development will suffer significant performance losses.

The data are raw elements that have not yet been interpreted.

They are used to generate information.

Information is therefore data that has been interpreted.

Data processing and cleaning.

The raw data must be made usable and for this, it is necessary to go through several stages of manipulation in order to format them.

The first phase is that of cleaning the data to make them reliable, for this, it is necessary to check if there is missing data or to ensure that there is no aberrant, inconsistent data which will be deleted.

Unstructured data

Data is raw

Unsupervised learning

Main unsupervised learning algorithms

K-means clustering

Hierarchical clustering

Mean Shift clustering

Expectation maximisation clustering

DBSCAN Density Based Spatial Clustering of Applications with Noise

GMM Gaussian Mixture Model clustering

Affinity propagation clustering

OPTICS Ordering points to identify the clustering structure

BIRCH Balanced Iterative Reducing and Clustering using Hierarchies Clustering

Flat clustering

Centroid-based clustering

Density-based clustering

Distribution-based clustering

Connectivity-based clustering

Mini-batch K-means Clustering

Ward's hierarchical clustering

...

PCA Principal Component Analysis

SVD singular value decomposition

ICA Independent component analysis

K nearest neighbors

Distribution models

Dimensionality Reduction

Regularizations

Features

Apriori algorithm

Contexts

Clustering

Association rules

Processing large data sets

Data processing lambda architecture

Data mining

Data cleaning

DataOps

Visualize and generate functions to simplify fitting equations to data points

Data visualization

Data annotation.

Annotation is used to describe and qualify data.

Structured data

Data Annotation

Metadata

Anomaly detection

Extract discriminating characteristics called features.

Image features

Image processing

The data is processed

Semi supervised learning

Supervised learning

Underfitting and overfitting in machine learning

Main supervised learning algorithms

Random Forest

SVM Support Vector Machine

K nearest neighbors

Contexts

Classification

Regression

Characterize the problem you want to address.

Transfert learning

Deep learning

Machine learning

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