
Explore Google Cloud sequence and text models for language processing, NLP and LLM workflows, including chatbots, speech to text, and subtitles, with NLTK, Gensim, OpenAI, and Hugging Face.
Discover how Google Colab ships with pre-installed deep learning packages like Keras and TensorFlow, plus text processing tools, and learn to install additional packages with !pip.
Begin with a Gmail account and log into Google Cloud Platform. Learn about free credits, pricing tools, and core services like Compute Engine, BigQuery, Cloud SQL, Dataflow, Kubernetes, and AutoML.
Learn how to set up a Google Cloud Compute Engine virtual machine, choose machine types and disks, and explore templates, images, and marketplace options for ML workloads.
Explore the nuts and bolts of Google BigQuery, manage datasets, run SQL queries on big data, and work with Covid and Italy datasets, open source marketplace data, and transfers.
Use Jupyter notebooks on Vertex AI to prepare data, access cloud data sources, and build text and sequence models with pre-trained options like palm and embeddings.
Access a single CSV from a GCP bucket into Colab by authenticating, installing Google Cloud Storage, creating a storage client, downloading the CSV, and loading it with pandas.
Identify textual similarity between two text chunks using tf-idf vectors, cosine similarity, and euclidean distances. Clean documents by removing stop words and special characters, then vectorize and compute pairwise similarities.
Learn NumPy, the numerical Python library for multi-dimensional arrays and matrices, and use ndarray objects with import numpy as np for functions like np.array and np.zeros.
Explore Python dictionaries as key-value data structures that store labeled data, demonstrating creation with curly brackets, access via keys, and operations like copy, delete, add, and sort.
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 :)