Machine Learning on Google Cloud: Sequence and Text Models
What you'll learn
- Introduction to getting started with Google Cloud Platform (GCP)
- Reading in and processing text data within GCP
- Implement common natural language processing (NLP) techniques such as entity analysis and keyword detection on text data
- Carry out text classification using deep leaning models
- Getting started with OpenAI for Large Language Model (LLM) based text analysis
Requirements
- Should have prior experience of Python data science
- Prior experience of statistical and machine learning techniques will be beneficial
- Should have an interest in extracting insights from text analysis
- Should have an interest in applying machine learning models on text data
Description
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that enables 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 (Tropical Ecology and Conservation) at Cambridge University.
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 perform everyday text analytics tasks such as text classification.
Introduction to common LLM frameworks such as OpenAI and Hugging Face.
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to ensure you get the most value from your investment!
ENROLL NOW :)
Who this course is for:
- People who wish to learn practical text mining and natural language processing
- People who wish to derive insights from textual data
- People wanting to harness the power of cloud computing via GCP
Instructor
- 4.4 Instructor Rating
- 27,671 Reviews
- 143,230 Students
- 60 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).