Machine Learning on Google Cloud: Sequence and Text Models

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Minerva Singh

3:28:55

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1 - Introduction To the Course

1 - Welcome To the Course.mp4

02:44

2 - Data and Code.html

3 - Python Installation.mp4

05:44

4 - Installing Packages In Google Colab.mp4

04:27

2 - An Overview of Google Cloud Platform GCP

5 - Where to Start.mp4

02:29

6 - Lets Look at the GCP Interface And Accessing the Free Trial.mp4

02:28

7 - Permissions and Access.mp4

03:16

8 - Some Components of GCP Machine Learning.mp4

01:34

9 - GCP and Machine Learning APIs.mp4

01:34

10 - GCP Buckets.mp4

04:55

11 - Virtually Speaking Virtual Machines VMs.mp4

05:08

12 - Nuts and Bolts of Google Big Query.mp4

05:51

3 - PythonJupyter Notebooks and GCP

13 - Working With Jupyter Notebooks The Vertex Way.mp4

06:53

14 - Work With JupyterLab.mp4

04:04

15 - Quick Access.mp4

00:40

16 - PreInstall Tensorflow.mp4

02:07

17 - Access Data From Buckets To JupyetrLab.mp4

04:24

18 - Start With Google Colaboratory Environment.mp4

07:13

19 - Google Colabs and GPU.mp4

05:50

20 - Accessing A Single CSV From GCP Buckets Into Colab.mp4

04:28

21 - Multiple PDFs.mp4

04:05

4 - Set Up Your Text Modelling Environment

22 - Get Access To the OpenAI API.mp4

04:02

23 - Sign Up For HuggingFace.mp4

02:43

24 - Introduction to LangChain.mp4

04:30

5 - Text Data Ingestion and PreProcessing

25 - Read in a PDF.mp4

07:39

26 - Read in Multiple PDFs.mp4

06:09

27 - Basic Text Cleaning.mp4

07:05

28 - Text Cleaning With NLTK.mp4

03:14

6 - Natural Language Processing NLP Analysis

29 - NLP.mp4

04:02

30 - Keyword Extraction.mp4

03:00

31 - TFIDF.mp4

01:53

32 - Document Similarity.mp4

03:36

33 - Text Similarity.mp4

04:40

34 - Text Similarity With Transformers.mp4

02:49

35 - Named Entity Recognition NER.mp4

04:26

36 - Named Entity Linking NEL.mp4

02:45

7 - Text Classification

37 - LSTM Theory.mp4

05:40

38 - Preliminary Steps.mp4

06:37

39 - Text Data Formatting.mp4

03:04

40 - Of Encoding and Padding.mp4

02:02

41 - Building the LSTM Model.mp4

07:17

42 - Install DistiBERT.mp4

04:32

43 - Build a Classification Model.mp4

06:21

8 - Miscellaneous Lectures

44 - Introduction to Numpy.mp4

03:46

45 - What Is Pandas.mp4

12:06

46 - Basic Data Cleaning With Pandas.mp4

04:30

47 - Dictionary.mp4

10:33

Description

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

Who is this 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

What You Need to Know?

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

More details

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.Â 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 conductingÂ 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 anÂ 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 sourcesÂ and producingÂ 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 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, youâ€™ll haveÂ MY CONTINUOUS SUPPORTÂ 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

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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).

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