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) 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 tasksCarry 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 othersUse 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.In addition to all the above, you'll have MY CONTINUOUS SUPPORT to ensure you get the most value from your investment!ENROLL NOW
Overview
Section 1: Introduction To the Course
Lecture 1 Welcome To the Course
Lecture 2 Data and Code
Lecture 3 Python Installation
Lecture 4 Installing Packages In Google Colab
Section 2: An Overview of Google Cloud Platform (GCP)
Lecture 5 Where to Start?
Lecture 6 Lets Look at the GCP Interface (And Accessing the Free Trial)
Lecture 7 Permissions and Access
Lecture 8 Some Components of GCP Machine Learning
Lecture 9 GCP and Machine Learning APIs
Lecture 10 GCP Buckets
Lecture 11 Virtually Speaking: Virtual Machines (VMs)
Lecture 12 Nuts and Bolts of Google Big Query
Section 3: Python/Jupyter Notebooks and GCP
Lecture 13 Working With Jupyter Notebooks (The Vertex Way)
Lecture 14 Work With JupyterLab
Lecture 15 Quick Access
Lecture 16 Pre-Install Tensorflow
Lecture 17 Access Data From Buckets To JupyetrLab
Lecture 18 Start With Google Colaboratory Environment
Lecture 19 Google Colabs and GPU
Lecture 20 Accessing A Single CSV From GCP Buckets Into Colab
Lecture 21 Multiple PDFs
Section 4: Set Up Your Text Modelling Environment
Lecture 22 Get Access To the OpenAI API
Lecture 23 Sign Up For HuggingFace
Lecture 24 Introduction to LangChain
Section 5: Text Data Ingestion and Pre-Processing
Lecture 25 Read in a PDF
Lecture 26 Read in Multiple PDFs
Lecture 27 Basic Text Cleaning
Lecture 28 Text Cleaning With NLTK
Section 6: Natural Language Processing (NLP) Analysis
Lecture 29 NLP
Lecture 30 Keyword Extraction
Lecture 31 TFIDF
Lecture 32 Document Similarity
Lecture 33 Text Similarity
Lecture 34 Text Similarity With Transformers
Lecture 35 Named Entity Recognition (NER)
Lecture 36 Named Entity Linking (NEL)
Section 7: Text Classification
Lecture 37 LSTM Theory
Lecture 38 Preliminary Steps
Lecture 39 Text Data Formatting
Lecture 40 Of Encoding and Padding
Lecture 41 Building the LSTM Model
Lecture 42 Install DistiBERT
Lecture 43 Build a Classification Model
Section 8: Miscellaneous Lectures
Lecture 44 Introduction to Numpy
Lecture 45 What Is Pandas?
Lecture 46 Basic Data Cleaning With Pandas
Lecture 47 Dictionary
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

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