Applied ML- Build NLP text embeddings using python

Applied ML: Build NLP text embeddings using python, available at $44.99, has an average rating of 4, with 8 lectures, 1 quizzes, based on 34 reviews, and has 1486 subscribers.

You will learn about Semantics and context in text data Why its important to embed texts Embedding libraries in python How to find text similarity This course is ideal for individuals who are Developers, Data Scientists, Machine Learning Engineers starting out in NLP It is particularly useful for Developers, Data Scientists, Machine Learning Engineers starting out in NLP.

Enroll now: Applied ML: Build NLP text embeddings using python

Summary

Title: Applied ML: Build NLP text embeddings using python

Price: $44.99

Average Rating: 4

Number of Lectures: 8

Number of Quizzes: 1

Number of Published Lectures: 8

Number of Published Quizzes: 1

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Semantics and context in text data

Why its important to embed texts

Embedding libraries in python

How to find text similarity

Who Should Attend

Developers, Data Scientists, Machine Learning Engineers starting out in NLP

Target Audiences

Developers, Data Scientists, Machine Learning Engineers starting out in NLP

Natural Language Processing (NLP) is a subfield of Artificial Intelligence and Machine Learning where we work with unstructured text data – human or machine generated. If you are new to AI and ML space and would like to know where exactly NLP fits in the bigger picture, I would like to suggest the course “Applied ML: The Big Picture”

But once you’ve arrived here with the interest in NLP, I’d like to say you’ve taken the right step of knowing more about this interesting and challenging field. The language we speak is rich in information across several dimensions and to even realize these dimensions is a research exercise in itself. For this reason, NLP data is one of the most exciting data one can work with, while developing ML models.

Embeddings are just techniques that attempt to decipher some of these dimensions and put them into numerical format. It’s the first and most important step before getting into advanced NLP algorithms and tasks such as machine translation, chatbot development etc.

This course provides the learner the foundational concepts along with two coding exercises, with attached jupyter notebooks, to provide a practical experience on the purpose and usefulness of text embeddings. Hopefully this inspires and prepares the learner to explore more topics in the interesting field of NLP.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course

Chapter 2: Embedding algorithms

Lecture 1: Why we need word embeddings

Lecture 2: TFIDF, Word2Vec and GloVe

Lecture 3: ELMo and BERT

Chapter 3: Embedding implementations

Lecture 1: Setup Python Development Environment

Lecture 2: Sklearn for TFIDF

Lecture 3: FastText for Word2Vec

Lecture 4: More embedding libraries

Chapter 4: Test your understanding

Instructors

Your Data HQ
Senior Data Scientist, ML Engineer, ML Researcher

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 1 votes

4 stars: 6 votes

5 stars: 22 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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