21.06.2021, 21:06
![[Bild: loainzihhhepfjnnvapn76jjov.jpg]](https://abload.de/img/loainzihhhepfjnnvapn76jjov.jpg)
Natural Language Processing & Deep Learning: Zero to Hero
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | VTT | Size: 8.43 GB | Duration: 15h 29m
Linguistics & Machine Learning: Grammar Syntax, Sentiment, ScrapeTweets, RNN/LSTM,Chatbot, SQuAD, Summary, Audio To Text
What you'll learn
Libraries: Tensorflow, Pytorch, NLTK, SpaCy, Sci-kit Learn, Twint
Linguistics Foundation To Help Learn NLP Concepts
Deep Learning: Neural Networks, RNN, LSTM Theory & Practical Projects
Machine Reading Comprehension: Create A Question Answering System with SQuAD
No Tedious Anaconda or Jupyter Installs: Use Modern Google Colab Cloud-Based Notebooks for using Python
How To Build Generative AI Chatbots
Create A Netflix Recommendation System With Word2Vec
Perform Sentiment Analysis on Steam Game Reviews
Convert Speech To Text
Machine Learning Modelling Techniques
Markov Property - Theory & Practical
Optional Python For Beginners Section
Cosine-Similarity & Vectors
Word Embeddings: My Favourite Topic Taught In Depth
Scrape Unlimited Tweets Using An Open Source Intelligence Tool
Speech Recognition
LSTM Fake News Detector
Context-Free Grammar Syntax
Scrape Wikipedia & Create An Article Summarizer
Description
This course takes you from a beginner level to being able to understand NLP concepts, linguistic theory, and then practice these basic theories using Python - with very simple examples as you code along with me.
Get experience doing a full real-world workflow from Collecting your own Data to NLP Sentiment Analysis using Big Datasets of over 50,000 Tweets.
Data collection: Scrape Twitter using: OSINT - Open Source Intelligence Tools: Gather text data using real-world techniques. In the real world, in many instances you would have to create your own data set; i.e source your data instead of downloading a clean, ready-made file online
Use Python to search relevant tweets for your study and NLP to analyze sentiment.
Language Syntax: Most NLP courses ignore the core domain of Linguistics. This course explains the fundamentals of Language Syntax & Parse trees - the foundation of how a machine can interpret the structure of s sentence.
New to Python: If you are new to Python or any computer programming, the course instructions make it easy for you to code together with me. I explain code line by line.
No Installs, we go straight to coding - Code using Google Colab - to be up-to-date with what's being used in the Data Science world 2021!
The gentle pace takes you gradually from these basics of NLP foundation to being able to understand Mathematical & Linguistic (English-Language-based, Non-Mathematical) theories of Deep Learning.
Natural Language Processing Foundation
Linguistics & Semantics - study the background theory on natural language to better understand the Computer Science applications
Pre-processing Data (cleaning)
Regex, Tokenization, Stemming, Lemmatization
Name Entity Recognition (NER)
Part-of-Speech Tagging
Libraries:
NLTK
Sci-kit Learn
Tensorflow
Pytorch
SpaCy
DeepPavlov
Twint
The topics outlined below are taught using practical Python projects!
Parse Tree
Markov Chain
Text Classification & Sentiment Analysis
Company Name Generator
Unsupervised Sentiment Analysis
Topic Modelling
Word Embedding with Deep Learning Models
Open Domain Question Answering (like asking Google)
Closed Domain Question Answering (Like asking a Restaurant-Finder bot)
LSTM using TensorFlow, Keras Sequence Model
Speech Recognition
Convert Speech to Text
Neural Networks
This is taught from first principles - comparing Biological Neurons in the Human Brain to Artificial Neurons.
Practical project: Sentiment Analysis of Steam Reviews
Word Embedding: This topic is covered in detail, similar to an undergraduate course structure that includes the theory & practical examples of:
TF-IDF
Word2Vec
One Hot Encoding
gloVe
Deep Learning
Recurrent Neural Networks
LSTMs
Get introduced to Long short-term memory and the recurrent neural network architecture used in the field of deep learning.
Build models using LSTMs
Who this course is for:
Anyone who is curious about data science & NLP
Those who are in the Business & Marketing world - learn use NLP to gain insight into customers & products. Can help at interviews & job promotions.
If you intend to enrol in an NLP/Data Science course but are a total newbie, complete this course before to avoid being lost in class since it can seem overwhelming if classmates already have a foundation in Python or Datascience.
Homepage
Code:
https://anonymz.com/?https://www.udemy.com/course/nidia-natural-language-processing-deep-learning-zero-to-hero/![[Bild: 002open.ipynbfilesing43j57.jpg]](https://abload.de/img/002open.ipynbfilesing43j57.jpg)
