Machine Learning On Google Cloud: Sequence And Text Models

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Advanced Machine Learning on Google Cloud: Sequence Models & NLP (Natural Language Processing) on Google Cloud

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) to enable computers to comprehend spoken and written human language. NLP has several applications, including text-to-voice and speech-to-text conversion, chatbots, automatic question-and-answer systems (Q&A), automatic image description creation, and video subtitles. With the introduction of ChatGPT, both NLP and Large Language Models (LLMs) will become increasingly popular, potentially leading to increased employment opportunities in this branch of AI. Google Cloud Processing (GCP) offers the potential to harness the power of cloud computing for larger text corpora and develop scalable text analysis models.

My course provides a foundation for conducting PRACTICAL, real-life NLP and LLM-based text analysis using GCP. By taking this course, you are taking a significant step forward in your data science journey to become an expert in harnessing the power of text data for deriving insights and identifying trends.

Why Should You Take My Course?

I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience analyzing real-life data from different sources and producing publications for international peer-reviewed journals.

This course will help you gain fluency in GCP text analysis using NLP techniques, OpenAI, and LLM analysis. Specifically, you will

• Gain proficiency in setting up and using Google Cloud Processing (GCP) for Python Data Science tasks
• Carry out standard text extraction techniques.
• Process the extracted textual information in a usable form via preprocessing techniques implemented via powerful Python packages such as NTLK.
• A thorough grounding in text analysis and NLP-related Python packages such as NTLK, Gensim among others
• Use deep learning models to carry out everyday text analytics tasks such as text classification.
• Introduction to common LLM frameworks such as OpenAI and Hugging Face.

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