Natural Language Processing (NLP) with Python and NLTK

Natural Language Processing (NLP) with Python and NLTK, available at $34.99, has an average rating of 4.55, with 26 lectures, based on 40 reviews, and has 1166 subscribers.

You will learn about Natural Language Processing using Python This course is ideal for individuals who are Data scientists, Applied Machine Learning engineers and Software engineers. It is particularly useful for Data scientists, Applied Machine Learning engineers and Software engineers.

Enroll now: Natural Language Processing (NLP) with Python and NLTK

Summary

Title: Natural Language Processing (NLP) with Python and NLTK

Price: $34.99

Average Rating: 4.55

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Natural Language Processing using Python

Who Should Attend

Data scientists, Applied Machine Learning engineers and Software engineers.

Target Audiences

Data scientists, Applied Machine Learning engineers and Software engineers.

Natural Language Processing or NLP is a very popular field and has lots of applications in our daily life. From typing a message to auto-classification of mails as Spam or not-spam NLP is everywhere.

NLP is a field concerned with the ability of a computer to understand, analyze, manipulate and potentially generate human language. In this course we study about NLP and use the NLP toolkit or NLTK in Python.

The course contains following:

Introduction to NLP and NLTK

NLP Pipeline

Reading raw data

Cleaning and Pre-processing

Tokenization

Vectorization

Feature Engineering

Training ML Algorithm for Classifying Spam and non-spam messages

This course would be very useful for Applied Machine Learning Scientists and Data Scientists who are working on NLP/NLU.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to NLP

Lecture 2: NLTK Introduction

Chapter 2: Reading and Cleaning Data

Lecture 1: Structured vs Unstructured Data

Lecture 2: Reading Text data

Lecture 3: Exploring the Data

Lecture 4: NLP Pipeline for Text Data

Lecture 5: Removing Punctuation | Cleaning | Pre-processing

Lecture 6: Tokenization

Lecture 7: Removing Stop Words

Lecture 8: Stemming

Lecture 9: Porter Stemmer in NLTK

Lecture 10: Lemmatization

Lecture 11: WordNet Lemmatizer in NLTK

Chapter 3: Vectorizing Data

Lecture 1: Vectorization

Lecture 2: Count Vectorization

Lecture 3: N-Grams Vectorization

Lecture 4: TF-IDF Vectorization (Term Frequency Inverse Document Frequency)

Chapter 4: Feature Engineering

Lecture 1: Feature Engineering – Introduction

Lecture 2: Feature Creation

Lecture 3: Feature Evaluation

Lecture 4: Power Transformations – Box Cox Transformation

Chapter 5: Building Machine Learning Classifier

Lecture 1: Evaluation Metrics – Accuracy, Precision and Recall

Lecture 2: K-Fold Cross-Validation

Lecture 3: Random Forest – Introduction

Lecture 4: Building a basic Random Forest model

Lecture 5: Random Forest with holdout test

Instructors

Abhishek Kumar
Computer Scientist at Adobe

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 17 votes

5 stars: 21 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

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