Showing posts with label Natural Language Processing. Show all posts
Showing posts with label Natural Language Processing. Show all posts
Natural Language Processing: Machine Learning NLP In Python
Sunday, March 6, 2022
Natural Language Processing: Machine Learning NLP In Python - Fundamental Theory & Beginner Python Projects: Sentiment, Scrape Tweets, RNN/LSTM, Chatbot, Audio To Text, Deep Learning
- Bestseller
- Created by Nidia Sahjara, Rajeev D. Ratan
- English [Auto]
What you'll learn
- Libraries: Hugging Face, NLTK, SpaCy, Keras, Sci-kit Learn, Tensorflow, Pytorch, Twint
- Linguistics Foundation To Help Learn NLP Concepts
- Deep Learning: Neural Networks, RNN, LSTM Theory & Practical Projects
- Scrape Unlimited Tweets Using An Open Source Intelligence Tool
- 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
- 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
SQuAD
SQuAD - Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset.
Libraries:
NLTK
Sci-kit Learn
Hugging Face
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.
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NLP - Natural Language Processing with Python
Saturday, March 5, 2022
Free Coupon Discount - NLP - Natural Language Processing with Python, Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing | Created by Jose Portilla
Preview this Udemy Course - GET COUPON CODE
Description
Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.
In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python.
We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files.
Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.
We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more!
Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems.
We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information.
Through state of the art visualization libraries we will be able view these relationships in real time.
Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages.
We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files.
This course even covers advanced topics, such as sentiment analysis of text with the NLTK library, and creating semantic word vectors with the Word2Vec algorithm.
Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots!
Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.
All of this comes with a 30 day money back garuantee, so you can try the course risk free.
What are you waiting for? Become an expert in natural language processing today!
I will see you inside the course,
Jose
Who this course is for:
Python developers interested in learning how to use Natural Language Processing.
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Data Science:Data Mining & Natural Language Processing in R
Tuesday, March 10, 2020
Free Coupon Discount - Data Science:Data Mining & Natural Language Processing in R, Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples | Created by Minerva Singh
Preview this Udemy Course - GET COUPON CODE
Description
MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:
Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:
My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.
This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.
NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.
I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!
The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.
HERE IS WHAT YOU WILL GET:
(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.
(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.
(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.
(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.
More Specifically, here's what's covered in the course:
Getting started with R, R Studio and Rattle for implementing different data science techniques
Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.
How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc
Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE
Statistical analysis, statistical inference, and the relationships between variables.
Machine Learning, Supervised Learning, & Unsupervised Learning in R
Neural Networks for Classification and Regression
Web-Scraping using R
Extracting text data from Twitter and Facebook using APIs
Text mining
Common Natural Language Processing techniques such as sentiment analysis and topic modelling
We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.
After each video you will learn a new concept or technique which you may apply to your own projects.
All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.
JOIN THE COURSE NOW!
Who this course is for:
Students wishing to learn practical data science and machine learning in R
Students wishing to learn the underlying theory and application of data mining in R
Students interested in obtaining/mining data from sources such as Twiter
Students interested in pre-processing and visualizing real life data
Students wishing to analyze and derive insights from text data
Students interested in learning basic text mining and Natural Language Processing (NLP) in R
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