Machine Learning On Google Cloud: Sequence And Text Models

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

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