MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .srt | Duration: 71 lectures (5 hour, 49 mins) | Size: 1.2 GB

Become an NLP Engineer by creating real projects using Python, semantic search, text mining and search engines!

What you'll learn

Understand every detail and build real stuff in NLP

(NEW)Learn how some plugins use semantic search to generate source code

(NEW)Building your vocabulary for any NLP model

(NEW)Reducing Dimensions of your Vocabulary for Machine Learning Models

(NEW)Feature Engineering and convert text to numerical values for machine learning models

(NEW) Keyword search VS Semantic search

(NEW)Similarity between documents

(NEW)Dealing with WordNet

(NEW)Search engines under the hood

Tokenizing text data

Converting words to their base forms using stemming

Converting words to their base forms using lemmatization

Dividing text data into chunks

Dealing with corpuses

Extracting document term matrix using the Bag of Words model

Building a category predictor

Constructing a gender identifier

Building a sentiment analyzer

Topic modeling using Latent Dirichlet Allocation

Requirements

A little bit of python

Description

-- UPDATED -- (NEW LESSONS ARE NOT IN THE PROMO VIDEO)

Learn about everything in AI by understanding concepts and building real stuff!

This course is a part of a series of courses specialized in artificial intelligence :

Understand and Practice AI - (NLP)

This course is focusing on the NLP:

Learn key NLP concepts and intuition training to get you quickly up to speed with all things NLP.

I will give you the information in an optimal way, I will explain in the first video for example what is the concept, and why is it important, what is the problem that led to thinking about this concept and how can I use it (Understand the concept). In the next video, you will go to practice in a real-world project or in a simple problem using python (Practice).

The first thing you will see in the video is the input and the output of the practical section so you can understand everything and you can get a clear picture!

You will have all the resources at the end of this course, the full code, and some other useful links and articles.

In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyze the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document.we will start with simple problems in NLP such as Tokenization Text, Stemming, Lemmatization, Chunks, Bag of Words model. and we will build some real stuff such as :

Learning How to Represent the Meaning of Natural Language Text

Building a category predictor to predict the category of a given text document.

Constructing a gender identifier based on the name.

Building a sentiment analyzer used to determine whether a movie review is positive or negative.

Topic modeling using Latent Dirichlet Allocation

Feature Engineering

Dealing with corpora and WordNet

Dealing With your Vocabulary for any NLP and ML model

TIPS (for getting through the course):

Take handwritten notes. This will drastically increase your ability to retain the information.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don’t just sit there and look at my code.

You don't know anything about NLP? let's break it down!

I am always available to answer your questions and help you along your data science journey. See you in class!

NOTICE that This course will be modified and I will add new content and new concepts from one time to another, so stay informed! 🙂

Who this course is for:

Anyone who wants to understand NLP concepts and build some projects

Beginner python developers curios about NLP, this course is not for experienced data scientists