Objectives of the training
The objective of this training is to gain an understanding of the principles of data mining and machine learning, to know how to apply and evaluate different analysis algorithms, and to put this knowledge into practice using tools such as Scikit-learn.Targeted audience
Data Analysts, Junior Data Scientists, Developers, Technical Project ManagersPrerequisite
Basic knowledge of statistics and programming (Python recommended)Trainers
Course architecture
Introduction to Data Mining
Chapter 1: Fundamentals
• Definition and challenges
• Life cycle of a data mining project
Chapter 2: Data preparation
• Cleaning, transformation, normalization
• Feature engineering
Workshop 1: Preparing a real dataset
Machine learning algorithms
Chapter 3: Supervised Learning
• Linear regression, logistic regression
• Decision trees, Random Forest
Chapter 4: Unsupervised Learning
• K-means, PCA, hierarchical clustering
Workshop 2: Classifying a dataset with Scikit-learn
Evaluation and interpretation
Chapter 5: Model evaluation
• Accuracy, precision, recall, F1-score
• ROC curves, confusion matrix
Chapter 6: Interpretability and bias
• SHAP, LIME
• Ethics and algorithmic bias
Workshop 3: Optimizing and interpreting a predictive model
Pedagogical details
Type of training
Private or personalized training
If you have more than 8 people to sign up for a particular course, it can be delivered as a private session right at your offices. Contact us for more details.
Request a quotePrivate or personalized training
If you have more than 8 people to sign up for a particular course, it can be delivered as a private session right at your offices. Contact us for more details.
Request a quote