CDOSS Certificate

Machine Learning With Python

Profiles that can prepare this certification contents:

Data engineer, statistical engineer, Developer engineer, Applied Mathematics engineer and more.

Global knowledge to be acquired to pass this certification:

  • Python libraries for data science
  • Supervised learning regression
  • Supervised learning classification
  • Unsupervised learning
  • Natural language processing

Detailed plan of preparation:

  1. Python libraries for Data Science

Pandas library for DataFrame. Matplotlib library for Visualization. Numpy library for Scientific Computing.

  1. Supervised learning regression

Simple and multiple linear regression. Polynomial regression. Evaluation metrics. Mean Squared Error. Absolute Squared Error. Root Mean Squared Error. R2. Adjusted R2. Features selection. P values. Features importance. Sci-Kit Learn library.

  1. Supervised learning classification

Nearest Neighbors. Logistic regression. Support Vector Machine (linear and kernel). Decision Tree. Random Forest. Na√Įve Bayes. Artificial Neural Networks. Evaluation metrics. Accuracy. Precision. Recall. F2 score. ROC curve. CAP curve. Sci-Kit Learn library. Tensoflow and Keras libraries.

  1. Unsupervised learning

K-Means. Hierarchical Clustering. Dimensionality reduction with Principal Component. Clusters visualization. Sci-Kit Learn library.

 

 

 

CDOSS Association

(Compliance for Data Open Source Software)

ūüďĆ Association CDOSS, ZI de Franchepr√©, Centre d’activit√©s Econoliques de Franchepr√©
54240 JOEUF (FRANCE)

‚úȬ† contact@cdoss.tech