Training Data Analysis & Machine e-learning
Master Machine Learning tools in an industrial context.
Order
Data Analysis & Machine Learning training
- e-learning
- statistics
Duration
50h
Rate
1330 €Ht/pers
Become an expert in industrial problem-solving
The Data Analysis & Machine Learning training course will enable you to master the use of Data Analysis and Machine Learning tools in an industrial setting. You will learn to situate Machine Learning in the context of AI and Big Data, and to understand the different algorithms (regression, dimension reduction, supervised and unsupervised classification).
At the end of the Data Analusis & Machine Learning training course, you'll be able to prepare data correctly, apply descriptive statistics methods, use dimension reduction tools such as PCA and UMAP and use modeling algorithms to extract all the information from your data and make the right decisions.
At the end of the Data Analusis & Machine Learning training course, you'll be able to prepare data correctly, apply descriptive statistics methods, use dimension reduction tools such as PCA and UMAP and use modeling algorithms to extract all the information from your data and make the right decisions.
Competitive benefits
Data Analysis & Machine Learning training is lively and interactive. Numerous exercises on Ellistat's Data Analysis software will enable you to put theory into practice. The resources and help provided by our teams will make it easier for you to assimilate complex concepts.
100%
From a satisfied Ellistat trainee
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Training available in e-learning, mix-learning or face-to-face.
99%
Trainees recommend our training courses.
*September 2021 to September 2024
Objectives
- Know the principles and master the use of Data Analysis and Machine Learning tools in an industrial setting.
- Training is based on the tools available in Ellistat's Data Analysis module.
For whom
This e-learning course is designed for managers and engineers who need to analyze production data in order to derive a new understanding or a predictive model of behavior.
Prerequisites
- Basic use of the Internet and a web browser
- A level II qualification and/or 5 years' initial professional experience
- Basic notions of quality and process management
- You don't need to be a Six Sigma Green Belt or Six Sigma Black Belt to take this course.
Duration
50 hours of lessons and exercises. E-Learning is available 24/7 for 3 months for this course.
Teaching and technical resources
- 100% E-Learning training on a dedicated platform
- Our teaching methods are mediatized (voice, text, exercises), fun and multimodal, with tests at every lesson.
- Theoretical presentations
- Case studies
- Online availability of PDF and Excel support documents
Teaching team
With over 30 years' experience, rich in teaching and practical experience, our industrial quality training organization provides you with training and consulting services to develop and improve your performance and know-how. All our consultants are Master Black Belt Lean Six Sigma and have at least 10 years' experience in applying Lean and Six Sigma tools in industrial environments.
Accessibility
This training course is accessible to people with disabilities. Please contact us for specific accommodation options. We will do our utmost to accommodate you.
Evaluation methods
- Attendance sheets.
- Oral or written questions (MCQs).
- Case studies.
- Practical work
Program
Understanding the scope of Machine Learning and data analysis
- Understanding the objectives of machine learning
- How Machine Learning fits in with Big Datas, Artificial Intelligence...
- Know how to map the different tools: regression, dimension reduction, clustering, supervised (S) and unsupervised (NS) classification.
- Understanding what you can and can't do with Machine Learning
Preparing data for proper analysis
- Prepare a data collection plan
- How to draw up a sampling plan
- 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
- Knowing 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 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 principles of neural networks and 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 intervals on metrics
Putting Machine Learning tools to work
- Master the use of Ellistat's Data Analysis module to implement all program points
Your feedback
A mode learning
- E-learningSelf-paced learning1330€
- 50 hours of e-learning courses
- Online support