What you would learn in Natural Language Processing (NLP) in Python with 8 Projects course?
Version 1.0: 1.0 :
Fast text Library for Text classification section added.
Hi Data Lovers!
Do you have any ideas about what Artificial Intelligence (AI) field is expected to become significant next year?
According to Statista dot com, which area of AI is projected to reach $43 billion in 2025?
If your answer is "Natural language processing' then you are at the right spot.
Do you wish to learn more?
How does Google News classify millions of news articles into hundreds of categories?
How can Android voice recognition recognizes the voice of your with this precision.
How does Google Translate convert hundreds of pairs of diverse languages?
If the answer"Yes "Yes," You are on the right path.
and to assist you and to help others, me and my friend Vijay have designed a complete course for Professionals and students to help you learn about Natural Language Processing starting from the beginning
NLP"Natural Language Processing "Natural Language Processing" has made its way into each aspect of everyday life.
The internet and cell phones are vital elements in our daily lives. In every application, you'll see the usage of NLP methods, from the search engine from Google to the recommendation system of Amazon and Netflix.
Google Now, Apple Siri, Amazon Alexa, Google Now
Analysis of sentiment
SpeechRecognition and many more.
Welcome to my class on NLP.
Natural Language Processing (NLP) in Python with eight projects
This course contains 10+ hours in the high-definition quality video and the subsequent content.
1: Hello! in this part, we will have an idea of what we'll learn during the entire course, as well as the concepts related to the natural processing of language.
2: Installation and Installation and the next section, we will install the online experience Google Colab setup.
3 . The Fundamentals in NLPIn this section, we will look at the most basic NLP tasks like tokenization Lemmatization, Stop Word removal name entity recognition, speech taggage, and learn how to use the various functions available in the Spacy or NLTK library.
4, 5 6, Spam Classification of Messages and restaurant Review prediction (Good and bad), IMDB, Amazon, and Yelp review classification
The following three sections will look at an actual dataset for text classification and spam detection and restaurant review classification Amazon IMDb reviews. We will look at how to process your data in Pre-Processing to ensure that your data is suitable for machine learning algorithms and use various Machine Learning estimator (Logistic Regression, SVM, Decision Tree) to classify the text.
7 8: Automated Summarization of Text and Twitter sentiment analysis in this two-part section, we will focus on practical applications of NLP.
Automatic text summarization that will reduce your text to get the summaries of extensive articles
Another thing we'll be working on is to find the sentiments from the most recent tweets regarding a particular keyword by using Twitter API - - tweepy library
9: Deep learning basics In this section, we will understand deep learning concepts, such as the activation of artificial neural networks and how ANN operates.
10: Word embedding In this Section, we will look at how to implement Word2vec on our custom datasets in addition to using a pre-trained Google Model.
11 12: Text Classification by using CNN & RNNIn this section, we will explore applying advanced deep-learning models such as convolution neural networks and recurrent neural networks to text classification.
13 . Automated Text Generation with TensorFlow, Keras, and LSTMIn this section, we apply neural networks-based LSTM model to generate text.
14 15 16, 17: Numpy, Pandas, Matplotlib + File ProcessingIn this section, we will provide anyone who wants to refresh ideas on analysis of data with Numpy along with Pandas library Data Visualization using the Matplotlib library as well as Text File processing as well as the PDF File processing.
The result is most comprehensive training courses on processing natural language,
And I expect that you have the basic knowledge of Python and the desire to explore different techniques in the NLP world.
The complete Understanding of Natural Language Processing
Implement NLP similar tasks using Scikit-learn NLTK and SpaCy
Use the Machine Learning Model to Classify Text Data
Text Classification (Spam Detection, Amazon product Review Classification)
Text Summary (Turn an article of 5000 words into 200 words)
Calculate Sentiment Score based on Recent Tweets (Tweeter API)
Learn to refresh your Deep Learning concepts (ANN, CNN & RNN)
Create the model of your choice Word Embedding (Word2vec) Model using Keras
Word Embeddings application with Google Pretrained Model
Spam Message Detection using Neural Network-Based CNN along with RNN Model
Automatic Text Generation by using TensorFlow, Keras, and LSTM
Utilizing Text Files and PDF using Python (PyPDF2 module)
The tokenization process, Stemming and Lemmatization
Stop Words and Parts of Speech (POS) Tagging using NLTK
Vocabulary Matching, Vocabulary Recognition (NER)
Data Analysis with Numpy as well as Pandas
Data Visualization using Matplotlib library
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