Catalogue Machine Learning — Mandragore Academy

ChatGPT Claude AI Machine Learning AI Agents & Agentic Systems

Machine Learning Catalogue

From beginner to expert — buy individually or as a bundle with a certificate of completion.

Each course brings together 8 interactive lessons in a Learning Workspace designed to turn knowledge into operational skills. Guided lessons, dynamic quizzes, conversational support, practical cases, additional resources and progress tracking let you learn effectively, at your own pace. The 15 courses follow a level-by-level progression, from framing a Machine Learning problem to deploying, monitoring and defending a complete predictive solution.

🎓 15 courses • 📚 120 interactive lessons • ⏱️ 45 to 75 hours of conversational learning

Preview of a lesson in the Learning Workspace

Level 1 — 9,90 € excl. VAT Level 2 — 12,90 € excl. VAT Level 3 — 15,90 € excl. VAT Level 4 — 18,90 € excl. VAT Level 5 — 24,90 € excl. VAT

⭐ Best value

Machine Learning — Complete Pack (15 courses)

ML01 → ML15 · 1 800 credits included

🎓 Certificate : "Professional ML" 247,50 €

📚 15 courses 📖 120 lessons ⏱ 45 to 75 h ⚡ 1800 credits included

142,90 € excl. VAT

Savings : 104,60 €

Machine Learning Foundations Pack 3 courses Niveau 1

🎓 Certificate : ML — Machine Learning Foundations

📚 3 courses 📖 24 lessons ⏱ 9 to 15 h ⚡ 240 credits included

29,70 €

22,90 excl. VAT

Savings : 6,80 €

01 Level 1

Understand Machine Learning and scope a problem

turn a business need into a measurable Machine Learning problem and decide whether ML is really the right approach

Understand what Machine Learning really brings Distinguish supervised and unsupervised learning Identify classification, regression and other problem families Define features, target, observations and predictions Distinguish training, validation and inference Build a baseline before any complex model Define technical metrics and business success criteria Workshop: decide whether a problem truly requires Machine Learning

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 80 credits included

02 Level 1

Prepare and explore data for Machine Learning

build a usable dataset without introducing bias, data leakage or inconsistencies

Explore distributions, types and data quality Handle missing values and outliers Prepare numerical and categorical variables Encode categories without introducing future information Scale variables when the model requires it Spot imbalances and insufficient representativeness Build train, validation and test sets without data leakage Workshop: audit and prepare a dataset before modeling

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 80 credits included

03 Level 1

Build a first reproducible ML pipeline

chain preprocessing, training, prediction and evaluation into a reproducible workflow

Set up a reproducible ML environment Build a baseline with scikit-learn Create preprocessing transformations Assemble preprocessing and model with Pipeline Train the pipeline and generate predictions Calculate and verify first metrics Save configuration, reference data and useful artifacts Workshop: deliver a first reproducible ML pipeline

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 80 credits included

Supervised Models Pack 3 courses Niveau 2

🎓 Certificate : ML — Supervised Models

📚 3 courses 📖 24 lessons ⏱ 9 to 15 h ⚡ 300 credits included

38,70 €

28,90 excl. VAT

Savings : 9,80 €

04 Level 2

Solve regression problems

design, compare and evaluate models intended to predict a numerical value

Understand a continuous target and build a baseline Build a linear regression Understand regularization and its benefits Use trees and ensembles for regression Choose between MAE, MSE and RMSE Interpret R² without overestimating it Analyze residuals and errors by segment Workshop: compare several regression models on a business need

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 100 credits included

05 Level 2

Solve classification problems

build classification models and choose metrics and thresholds suited to the real cost of errors

Understand classes and build a baseline Build a logistic regression Use trees and Random Forest Discover boosting methods Read a confusion matrix Compare precision, recall and F1 Use ROC-AUC, PR-AUC and adjust the decision threshold Workshop: choose a classifier based on the cost of errors

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 100 credits included

06 Level 2

Evaluate correctly and compare models

get a reliable performance estimate and avoid misleading conclusions when selecting a model

Understand the risk of an overly optimistic evaluation Use a hold-out set correctly Evaluate with cross-validation Stratify data when necessary Respect temporal order with a time split Compare several models on consistent metrics Calibrate probabilities and reserve the final test set Workshop: build a defensible evaluation protocol

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 100 credits included

Advanced Modeling Pack 3 courses Niveau 3

🎓 Certificate : ML — Advanced Modeling

📚 3 courses 📖 24 lessons ⏱ 9 to 15 h ⚡ 360 credits included

47,70 €

35,90 excl. VAT

Savings : 11,80 €

07 Level 3

Design and select features

create informative variables without data leakage and reduce complexity when it improves the model

Understand the role of feature engineering Create useful numerical variables and transformations Create time-based features without information leakage Build interactions and aggregates Detect redundancy and multicollinearity Use feature selection methods Encapsulate feature engineering and selection in the pipeline Workshop: improve a model with justified features

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 120 credits included

08 Level 3

Optimize models and hyperparameters

reduce underfitting and overfitting and search hyperparameters without contaminating the final evaluation

Understand underfitting, overfitting, bias and variance Identify learned parameters and hyperparameters Build a Grid Search with cross-validation Use Randomized Search on a larger space Choose the metric actually being optimized Control compute time and search space Compare the optimized model without touching the final test set Workshop: conduct and document a hyperparameter optimization

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 120 credits included

09 Level 3

Explore unlabeled data

use clustering, dimensionality reduction and anomaly detection to understand structures without a supervised target

Understand the goals of unsupervised learning Prepare data for clustering Segment with K-means Understand hierarchical clustering Discover DBSCAN and irregularly shaped clusters Reduce dimensionality with PCA Detect anomalies and challenge their meaning Workshop: build and interpret an exploratory segmentation

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 120 credits included

Reliability and ML Lifecycle Pack 3 courses Niveau 4

🎓 Certificate : ML — Reliability and ML Lifecycle

📚 3 courses 📖 24 lessons ⏱ 9 to 15 h ⚡ 420 credits included

56,70 €

42,90 excl. VAT

Savings : 13,80 €

10 Level 4

Interpret, explain and challenge models

understand the factors that influence predictions and identify situations where an explanation can be misleading

Distinguish predictive performance and explainability Interpret coefficients and simple parameters Analyze feature importance with caution Measure importance with permutation Understand partial dependence plots Discover SHAP-style local explanations Analyze errors and explanations by subgroup Workshop: produce an explainability analysis with its limits

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 140 credits included

11 Level 4

Manage imbalance, bias, robustness and reliability

assess a model's quality beyond an average metric and control its risks on populations and hard cases

Identify the consequences of strong class imbalance Use weighting and resampling methodically Choose metrics and thresholds for rare cases Evaluate and improve calibration Compare performance across subgroups Test robustness and sensitivity to perturbed data Identify out-of-distribution data and usage limits Workshop: challenge a model's reliability before a decision

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 140 credits included

12 Level 4

Track experiments and manage model versions

make experiments comparable, auditable and reproducible, then manage the model lifecycle

Understand experiment, run and ML artifact Log parameters and metrics with MLflow Tracking Track models, figures and artifacts Compare several runs and select a candidate Register a model in the Model Registry Manage versions, aliases, tags and metadata Document data, code, environment and reproducibility Workshop: build the full traceability of a candidate model

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 140 credits included

Professional Machine Learning Mastery Pack 3 courses Niveau 5

🎓 Certificate : ML — Professional Machine Learning Mastery

📚 3 courses 📖 24 lessons ⏱ 9 to 15 h ⚡ 480 credits included

74,70 €

48,90 excl. VAT

Savings : 25,80 €

13 Level 5

Understand and practice Deep Learning

understand the essential mechanisms of neural networks and train a simple model without diluting the path in advanced Deep Learning

Understand tensors, layers and neural networks Choose activation functions and a simple architecture Understand the loss function and optimization Understand gradient and backpropagation Use batch, epoch and DataLoader Build and train a simple network with PyTorch Control overfitting, validation and saving Workshop: compare a simple network to a classic baseline

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 160 credits included

14 Level 5

Deploy, monitor and maintain an ML model

move from a validated model to a usable, versioned, observable and maintainable production service

Prepare a reliable inference artifact Distinguish batch prediction and online inference Expose a model through an inference API Containerize and configure the service Manage registry, versions, promotion and rollback Monitor latency, errors and input data quality Detect data drift, concept drift and retraining conditions Workshop: deploy and monitor a prediction service

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 160 credits included

15 Level 5

Full professional Machine Learning project

design, evaluate, explain, version, deploy and defend an end-to-end ML solution

Scope the churn problem and the cost of errors Prepare data and build the baseline Build pipelines, features and candidate models Compare, optimize and evaluate without leakage Analyze segments, errors and explainability Track experiments and version the final model Deploy the API, define monitoring, model card and runbook Final workshop: present and defend the ML solution and its limits

📚 8 lessons ⏱ 3 to 5 h 🎯 Quiz included ⚡ 160 credits included