Sessions

- INTRODUCTION DATA SCIENCE ET PYTHON
- PYTHON BASICS
- PYTHON CONTROL STATEMENTS
- PYTHON DATA STRUCTURES
- PYTHON FUNCTIONS
- PYTHON NUMPY PACKAGE
- PYTHON PANDAS PACKAGE
- DATA SCIENCE FOUNDATION
- DATA SCIENCE
- PYTHON FOR DATA SCIENCE
- VISUALIZATION WITH PYTHON
- R LANGUAGE ESSENTIALS
- STATISTICS
- MACHINE LEARNING INTRODUCTION
- COLLABORATIVE TOOLS
- Github
- BitBucket
- Docker
- MACHINE LEARNING
- CLASSIFICATION
- RÉGRESSION
- CLUSTERING
- MACHINE LEARNING ACANCÉ
- DÉTECTION D’ANOMALIES
- RÉDUCTION DE DIMENSION
- SÉRIES TEMPORELLES
- MACHINE LEARNING APPLIQUÉ
- ÉTHIQUE & INTERPRÉTABILITÉ
- TEXT MINING
- WEBSCRAPING
- DATA SCIENCE EXPERT
- TIME SERIES FORECASTING
- FEATURE ENGINEERING
- SENTIMENT ANALYSIS
- AWS CLOUD FOR DATA SCIENCE
- REGULAR EXPRESSIONS WITH PYTHON
- ML MODEL DEPLOYMENT WITH FLASK
- ADVANCED DATA ANALYSIS WITH MS EXCEL
- AZURE FOR DATA SCIENCE
- DATABASE : SQL AND NoSQ
- DATABASE INTRODUCTION
- SQL BASICS
- DATA TYPES AND CONSTRAINT
- DATABASES AND TABLES (MySQL)
- SQL JOINS
- SQL COMMANDS AND CLAUSES
- DOCUMENT DB/NO-SQL DB
- ARTIFICIAL INTELLIGENCE FOUNDATION
- ARTIFICIAL INTELLIGENCE OVERVIEW
- DEEP LEARNING INTRODUCTION
- TENSORFLOW FOUNDATION
- COMPUTER VISION INTRODUCTION
- NATURAL LANGUAGE PROCESSING (NLP)
- AI ETHICAL ISSUES AND CONCER
- MLOps
- MLflow
- Docker