Basics: intensive upskilling of the cohort on maths, CS and programing
> Linux, Bash, Git & Gitlab application, Advanced Python, Linear Algebra, Probabilities, Bayesian statistics, SQL & Postgre application, Intro to architecture
Data Science: mastering of fundamentals in Data Science, from classic ML algorithms to state-of-the-art Deep Learning and NLP and also focused on models interpretability.
> Machine Learning: Scikit Learn, Pipelines, SHAP
> Deep Learning: TensorFlow, PyTorch
> NLP & NLU: NLTK, Gensim, Dialogflow, SpaCy, Transformers, etc.
Engineering: extensive overview and practice of tools, skills and best practices in developing that any MLE should master
> Devops for Data Science: Gitlab CI/CD, Pytest, Docker, Kubernetes
> Cloud Computing: GCP & AWS applications
> Big Data Processing: Spark, ElasticSearch, DASK, NoSQL and NewSQL
> MLOPs & Model Lifecycle management: MLflow, Model drifting detection
Soft Skills: understand the role of a MLE in a company, be aware of what's at stakes in an AI ecosystem, acquire the necessary skills to navigate efficiently in project development
> Data & companies: ethics, GDPR, Factory Data
> Agile training
+ Individual and group projects sessions and case studies
Linux, Git, Github, PostgreSQL, Bash, Python, Machine Learning, SQL, Tensorflow, Data Science, Big Data