Introduction to NLP

Results-driven Machine Learning Engineer with expertise in NLP and MLOps, specializing in optimizing model performance, developing scalable data solutions, and enhancing system security. Skills: • Machine Learning & NLP: supervised, unsupervised algorithms, NN, CNNs, RNNs, LSTMs, Classical NLP, Tokenization, stemming, lemmatization, POS tagging, named entity recognition (NER), Embeddings, Word2Vec, GloVe, BERT, GPT, Attention, Transformer, Pytorch, HuggingFace, LLMs, Prompt Engineering, RAG, Vector Store, Langchain, LLM serving • MLOps & Deployment: Azure Repos - Pipelines, Git, Docker - Compose - Swarm, Mlflow, Weights & Biases, OLLAMA • API Development & Integration: Python, Flask, Flask-RESTx, Http/s, GRPC, Protocol Buffers, Swagger, Postman, SSL/TLS, Authentication, role-based access control, rate limiting, Microservices, Service Registry • Graph Database: Neo4j, Cypher language, APOC: Awesome Procedures on Cypher • Other Tools & Technologies: Numpy, Pandas, Matplotlib, Sklearn, NLTK, Redis, A/B Testing