Machine Learning Course

Block 1: introduction basic features
Python, the new unknownData typesFlow controlFunctions

Block 2: Object-oriented programming (OOP)
Object based methodologyClasses, objects, attributes and methodsPractising with classes and objectsInheritance
Block 3: OOP and applied methods
Other toolsApplied methodsErrors and exceptionsTemporary data

Block 4: data handlingExcel – CSVJSONDatabase (DB)
Specialised data libraries

Projects: putting it into practiceWork experience 1Work experience 2Final project


Artificial Intelligence Training Programme

Module 1. Introduction to Machine LearningIntroduction to Big Data and Machine LearningWorking environment: VMJupyterPython numerical librariesIntroduction to Scikit-learn

Module 2. Supervised learningLinear regressionGradient descent optimisationNormalisationRegularisationCross validationBayes theoremClassification by decision tresLogistic regression / classificationClassification by SVM (Support Vector Machines)Introduction to neural networks

Module 3. Unsupervised learningOptimisation by randomisationGrouping

Module 4. Enhanced learningAnomaly detectionRecommendation systemsGeneric algorithms


Module 5. Development of machine learning systemsFeature engineeringPrincipal Components Analysis (PCA)AssemblyML systems approachEvaluation and improvement of modelsML Operations
Final ProjectA client, an IT supply company, has asked the company we work for to develop a web application to help them manage their products and suppliers. Therefore, we must implement an application that serves as a database, but also as a management tool. Once this has been done, we will adapt it to certain requirements that will arise in order to include Artificial Intelligence in the system and thus be able to improve it.