Machine Learning on Google Cloud: Sequence and Text Models

Machine Learning on Google Cloud: Sequence and Text Models, available at $54.99, has an average rating of 4.4, with 47 lectures, based on 36 reviews, and has 254 subscribers.

You will learn about 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 This course is ideal for individuals who are People who wish to learn practical text mining and natural language processing or People who wish to derive insights from textual data or People wanting to harness the power of cloud computing via GCP It is particularly useful for People who wish to learn practical text mining and natural language processing or People who wish to derive insights from textual data or People wanting to harness the power of cloud computing via GCP.

Enroll now: Machine Learning on Google Cloud: Sequence and Text Models

Summary

Title: Machine Learning on Google Cloud: Sequence and Text Models

Price: $54.99

Average Rating: 4.4

Number of Lectures: 47

Number of Published Lectures: 47

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 🙂

Course Curriculum

Chapter 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

Chapter 2: An Overview of Google Cloud Platform (GCP)

Lecture 1: Where to Start?

Lecture 2: Lets Look at the GCP Interface (And Accessing the Free Trial)

Lecture 3: Permissions and Access

Lecture 4: Some Components of GCP Machine Learning

Lecture 5: GCP and Machine Learning APIs

Lecture 6: GCP Buckets

Lecture 7: Virtually Speaking: Virtual Machines (VMs)

Lecture 8: Nuts and Bolts of Google Big Query

Chapter 3: Python/Jupyter Notebooks and GCP

Lecture 1: Working With Jupyter Notebooks (The Vertex Way)

Lecture 2: Work With JupyterLab

Lecture 3: Quick Access

Lecture 4: Pre-Install Tensorflow

Lecture 5: Access Data From Buckets To JupyetrLab

Lecture 6: Start With Google Colaboratory Environment

Lecture 7: Google Colabs and GPU

Lecture 8: Accessing A Single CSV From GCP Buckets Into Colab

Lecture 9: Multiple PDFs

Chapter 4: Set Up Your Text Modelling Environment

Lecture 1: Get Access To the OpenAI API

Lecture 2: Sign Up For HuggingFace

Lecture 3: Introduction to LangChain

Chapter 5: Text Data Ingestion and Pre-Processing

Lecture 1: Read in a PDF

Lecture 2: Read in Multiple PDFs

Lecture 3: Basic Text Cleaning

Lecture 4: Text Cleaning With NLTK

Chapter 6: Natural Language Processing (NLP) Analysis

Lecture 1: NLP

Lecture 2: Keyword Extraction

Lecture 3: TFIDF

Lecture 4: Document Similarity

Lecture 5: Text Similarity

Lecture 6: Text Similarity With Transformers

Lecture 7: Named Entity Recognition (NER)

Lecture 8: Named Entity Linking (NEL)

Chapter 7: Text Classification

Lecture 1: LSTM Theory

Lecture 2: Preliminary Steps

Lecture 3: Text Data Formatting

Lecture 4: Of Encoding and Padding

Lecture 5: Building the LSTM Model

Lecture 6: Install DistiBERT

Lecture 7: Build a Classification Model

Chapter 8: Miscellaneous Lectures

Lecture 1: Introduction to Numpy

Lecture 2: What Is Pandas?

Lecture 3: Basic Data Cleaning With Pandas

Lecture 4: Dictionary

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 2 votes
2 stars: 2 votes
3 stars: 0 votes
4 stars: 5 votes
5 stars: 27 votes

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