The advanced block is more complex topics that make you already a strong professional AI developer 
 
 Writing neural networks with the Tensorflow library. Part 1 
 
 Writing neural networks with the Tensorflow library. Part 2 
 
 Writing neural networks with the PyTorch library. Part 1 
 
 Writing neural networks with the PyTorch library. Part 2 
 
 AutoML: images, tables, texts, time series 
 
 Computer vision: object detection, segmentation, OCR, OpenCV 
 
 Speech recognition and generation 
 
 Natural language processing: Bert, T5, NER, transformers, NLTK, pymorphy2, RNN, LSTM 
 
 The basics of working with chatGPT. Promt engineering. Embedding representation of texts. LangChain algorithm. Creation of knowledge bases. Creating a dialog with chatGPT. Connecting multiple chatGPT models 
 
 Working with GigaChat. Running and using SoTA (state of the art) local (contour) models 
 
 Classical machine learning, clustering and recommender systems 
 
 Publish to server, FastApi 
 
 Working with databases: SQL, MS SQL, PostgreSQL, Oracle 
 
 Fundamentals of distributed data processing: Spark, Hadoop and HDFS 
 
 Tools and frameworks for working with big data: PySpark, Hive and ClickHouse 
 
 Orchestration and workflow management: AirFlow, MLFlow and MLOps 
 
 Tools for project management and teamwork: Confluence, Jira, Git and Gitlab 
 
 Streaming and containerization: Kafka, CI/CD and Kubernetes