Machine Learning: basicsHome Training Catalog Machine Learning: basics Analysis Data Science Open Source On-site courses Face-to-face Remote/Virtual English French Objectives Understand the basics of machine learning and machine learning on structured data, apply standard dimension reduction and clustering methods, know how to implement a regression model by controlling overfitting and validating model predictions, understand the basics of text mining. At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models. Prérequis none Pedagogical and technical material and resources Dedicated digital training platform (LMS). Sessions with the trainer, training material in digital format, balance between theoretical and practice, concrete cases. Assessment Practical application and exercises Results & skills expected at the end of the training At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models. Program DAY 1 Introduction to unsupervised methods: Principal component analysis (PCA) Automatic classification (k-means), Association rules (apriori, eclat) Introduction to supervised methods: Linear and logistic regression models with regularization (ridge regression), Decision trees (regression and classification) . DAY 2 Standard methods for implementing predictive models: Feature engineering: learning to reduce the complexity of a problem, Variable selection, Cross-validation, Calibration of a predictive model Text mining and web scraping Prerequisites None Instructional and supervisory resources In-class training sessions, Instructional materials in digital format, Concrete case studies, Theoretical presentations, Use of data provided by participants, In-depth work on the data, Paper-board, video projector, internet connection Download the full program Duration 14 hours Level Beginner Audience Anyone who wants to discover the basic principles of Machine Learning. Participants 8 people maximum Please contact us for a personnalized offer. Are you looking for information about a training course? You want to set up a customized training session? Contact our pedagogical team! Notice: JavaScript is required for this content. Upcoming trainings Analysis Modular training course - NVivo basics Objectives Understand the role of NVivo in the qualitative analysis process. Understand and master the NVivo environment. Source management Case management Source coding Crossing matrices Documenting your analysis; memos, annotations and links to. 06, 07, 09 and 10 March Register Ask for a quote Upcoming trainings Analysis Modular training course - NVivo Advanced Objectives Discussing with participants on their NVivo practices and deepen the following knowledge and skills: Mastering the NVivo environment. Source management Case management Source coding Queries and matrices Documenting your analysis; memos, annotations and links to. Viewing and exporting Importing and using data from social networks and the web Collaborative work and coding comparison. Please contact us if you would like to have this training course held in English. 13, 14, 16 and 17 March Register Ask for a quote Upcoming trainings Analysis Scientific graphs with PRISM Objectives Discover all the possibilities offered by PRISM to get the graph you want, automated plotting of fit curves included. 12 and 13 June Register Ask for a quote Previous Next