machine learning: natural language processing in Python (V2) How to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe . how to implement a document retrieval system / search engine / similarity search / vector similarity Probability models, language models and Markov models . cipher decryption algorithm using genetic algorithms and language modeling How to implement spam detection and sentiment analysis . what you’ll learn in machine learning is a.. …-.–.. .

What you’ll discover in Artificial intelligence: All-natural Language Handling in Python (V2)

  1. How to transform message right into vectors making use of CountVectorizer, TF-IDF, word2vec, and also Handwear cover
  2. Exactly how to execute a record retrieval system/ internet search engine/ resemblance search/ vector resemblance
  3. Likelihood designs, language versions and also Markov versions (requirement for Transformers, BERT, as well as GPT-3)
  4. How to carry out a cipher decryption formula making use of hereditary algorithms and also language modeling
  5. Just how to apply spam discovery
  6. Exactly how to carry out view analysis
  7. Just how to execute an article spinner
  8. Just how to apply text summarization
  9. Just how to apply concealed semantic indexing
  10. How to implement topic modeling with LDA, NMF, and SVD
  11. Machine learning (Ignorant Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation)
  12. Deep discovering (ANNs, CNNs, RNNs, LSTM, GRU) (more important prerequisites for BERT as well as GPT-3)
  13. Hugging Face Transformers (VIP only)
  14. How to use Python, Scikit-Learn, Tensorflow, +A Lot More for NLP
  15. Text preprocessing, tokenization, stopwords, lemmatization, and stemming
  16. Parts-of-speech (POS) tagging and also called entity acknowledgment (NER)

Description

Hi pals!

Invite to Machine Learning: Natural Language Handling in Python (Version 2).

This is a large 4-in-1 program covering:

1) Vector versions and message preprocessing techniques

2) Likelihood models and also Markov designs

3) Machine learning methods

4) Deep discovering and also neural network methods

In part 1, which covers vector designs and also message preprocessing methods, you will find out about why vectors are so essential in information science as well as expert system. You will certainly learn more about numerous methods for converting message into vectors, such as the CountVectorizer as well as TF-IDF, and you’ll find out the basics of neural embedding approaches like word2vec, as well as Handwear cover.

You’ll then use what you discovered for numerous jobs, such as:

In the process, you’ll likewise learn important text preprocessing actions, such as tokenization, stemming, as well as lemmatization.

You’ll be introduced briefly to timeless NLP jobs such as parts-of-speech tagging.

Partially 2, which covers likelihood designs as well as Markov versions, you’ll discover among one of the most vital designs in all of data scientific research and artificial intelligence in the previous 100 years. It has actually been applied in lots of locations in addition to NLP, such as finance, bioinformatics, and also support discovering.

In this program, you’ll see how such chance designs can be made use of in different ways, such as:

Notably, these approaches are a crucial requirement for comprehending exactly how the most recent Transformer (attention) designs such as BERT and also GPT-3 job. Particularly, we’ll find out about 2 essential tasks which refer the pre-training goals for BERT as well as GPT.

Partly 3, which covers artificial intelligence techniques, you’ll find out about even more of the classic NLP tasks, such as:

This area will certainly be application-focused instead of theory-focused, indicating that rather than investing most of our initiative discovering the information of different ML formulas, you’ll be concentrating on just how they can be put on the above jobs.

Certainly, you’ll still require to learn something about those formulas in order to understand what’s going on. The adhering to formulas will certainly be utilized:

These are not just “any kind of” artificial intelligence/ expert system algorithms but instead, ones that have actually been staples in NLP and are therefore a crucial part of any kind of NLP course.

Partially 4, which covers deep understanding approaches, you’ll discover modern-day neural network designs that can be related to fix NLP jobs. Many thanks to their world power and also adaptability, neural networks can be made use of to resolve any of the aforementioned tasks in the training course.

You’ll find out about:

The research study of RNNs will certainly entail modern-day designs such as the LSTM and GRU which have actually been commonly utilized by Google, Amazon, Apple, Facebook, etc for uphill struggles such as language translation, speech acknowledgment, and also text-to-speech.

Undoubtedly, as the current Transformers (such as BERT as well as GPT-3) are examples of deep neural networks, this part of the course is an important requirement for recognizing Transformers.

Thank you for reading and also I hope to see you soon!

Who this course is for:

  • Anyone who wants to learn natural language processing (NLP)
  • Anyone interested in artificial intelligence, machine learning, deep learning, or data science
  • Anyone who wants to go beyond typical beginner-only courses on Udemy
File Name :Machine Learning: Natural Language Processing in Python (V2) free download
Content Source:udemy
Genre / Category:Development
File Size :4.16 gb
Publisher :Lazy Programmer Inc.
Updated and Published:08 Aug,2022

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File name: Machine-Learning-Natural-Language-Processing-in-Python-V2.rar
File Size:4.16 gb
Course duration:8 hours
Instructor Name:Lazy Programmer Inc. , Lazy Programmer Team
Language:English
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