Training Machine Learning

The aim of this course is to provide trainees with in-depth knowledge and proficiency in the use of Data Analysis and Machine Learning tools in an industrial context.
Machine Learning
Data Analysis & Machine Learning
e-learning, stats

Duration 50 Hours

1330 excluding VAT / person

E-Learning has a high drop-out rate, so why do our courses have a success rate of 90 %? The key? Our content! No handouts to put you to sleep.
Kayla is a Green Belt at the B&B factory in Baumé, and is facing some real problems. We need to help her find solutions.
Still need support? We offer tutoring sessions with our Master Black Belts trainers to support you!

Solving industrial problems

The Data Analysis & Machine Learning course is lively and interactive. The resources provided will enable you to master the subject perfectly.

Programme

Understanding the scope of Machine Learning and data analysis

  • Understanding the objectives of Machine Learning
  • Put Machine Learning in the context of Big Data, Artificial Intelligence...
  • Know how to map the different tools: regression, dimension reduction, clustering, supervised (S) and unsupervised (NS) classification.
  • Understanding what can and cannot be done with Machine Learning

Preparing data for proper analysis

  • Preparing a data collection plan
  • How to draw up a sampling plan
  • Know how to apply the principles of descriptive statistics to data (type of distribution, calculation of mean statistics, median standard deviation, kurtosis, skewness, etc.).
  • Assessing the presence of outliers

Knowledge of the principle: dimension reduction tools (NS)

  • Know the principle of dimension reduction
  • Understanding and using ACP, UMAP and TSNE tools
  • Principal component analysis
  • Correspondence factor analysis
  • Multiple correspondence analysis
  • Understanding and using a T2 card

Unsupervised classification

  • Knowledge of the principle: unsupervised classification (NS) tools
  • Hierarchical classification: Dendrogram, Variables, Individuals
  • Know the principles of the K means, DBSCAN and Mean Shift algorithms

Supervised learning, continuous Y

  • Know the principle and know how to use the tools: linear regression, multiple linear regression
  • Understand the principle of neural networks and know how to apply them to simple cases

Supervised learning, discrete Y

  • Logistic regression
  • Ordinal logistic regression
  • Supervised classification SVM
  • KNN and decision tree

Metric of a classifier

  • ROC curve and confusion control
  • Simple classifier metrics
  • Combined metric
  • Confidence interval for metrics

Putting Machine Learning tools into practice

  • Mastering the use of the Ellistat Data Analysis module to implement all the points in the programme

Objectives

  • Familiarity with the principles and use of Data Analysis and Machine Learning tools in an industrial context.
  • The training is based on the tools available in the Ellistat Data Analysis module.

For whom

This e-learning course is designed for managers and engineers who need to analyse production data in order to gain a new understanding or develop a predictive model of behaviour.

Prerequisites

  • Basic use of the Internet and a web browser.
  • A level II qualification and/or 5 years' initial professional experience
  • A basic understanding of quality and process management
  • You don't need to be a Six Sigma Green Belt or Six Sigma Black Belt.

Duration

50 hours of lessons and exercises. E-Learning is available 24/7 for 3 months for this course.

Access times

Entries are permanent. So you can start this course at any time!

Accessibility

This training course is accessible to people with disabilities. Please contact us for details of special arrangements. We will do our utmost to accommodate you.

Solving industrial problems

The Data Analysis & Machine Learning course is lively and interactive. The resources provided will enable you to master the subject perfectly.
Your feedback

Our courses the most popular

See all our courses

Scroll to Top