Sessions

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