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Best Natural Language Processing Courses

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Best Natural Language Processing Courses
Best Natural Processing Courses

Natural language processing is the driving force behind machine intelligence in many modern real-world applications. It is hard from the standpoint of the child, who must spend many years acquiring a language … it is hard for the adult language learner, it is hard for the scientist who attempts to model the relevant phenomena, and it is hard for the engineer who attempts to build systems that deal with natural language input or output.

Let’s look at some of the top Best Natural Language Processing courses which you can study at home in your comfort – Online!

NLP – Natural Language Processing with Python – Udemy

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.

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, you can learn Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in the text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems.

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.

Rating: 4.6

Duration: 12 hours | Lectures: 80 | Pricing: $45

InfoVisit the website and avail an amazing offer of 80% off

Machine Learning: Natural Language Processing in Python (V2) – Udemy

This is a massive 4-in-1 course covering:

1) Vector models and text preprocessing methods

2) Probability models and Markov models

3) Machine learning methods

4) Deep learning and neural network methods

covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence,  various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and the basics of neural embedding methods like word2vec, and GloVe, most important models in all of data science and machine learning.

The study of RNNs will involve modern architectures such as the LSTM and GRU which have been widely used by Google, Amazon, Apple, Facebook, etc. for difficult tasks such as language translation, speech recognition, and text-to-speech.

Obviously, as the latest Transformers (such as BERT and GPT-3) are examples of deep neural networks, this part of the course is an essential prerequisite for understanding Transformers.

Rating: 4.9

Duration: 22 hours | Lectures: 152 | Pricing: $45

Info: Visit the website and avail of an amazing offer of 80% off

Data Science: Natural Language Processing (NLP) in Python – Udemy

In this course, you will build MULTIPLE practical systems using natural language processing.

The first thing we’ll build is a cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms.

The second project, where we begin to use more traditional “machine learning“, is to build a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.

Next we’ll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.

We’ll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.

Rating: 4.6

Duration: 12 hours | Lectures: 88 | Pricing: $45

Info: Visit the website and avail of an amazing offer of 80% off

Deep Learning: Advanced Natural Language Processing and RNNs

This course takes you to a higher systems level of thinking.

Since you know how these things work, it’s time to build systems using these components.

At the end of this course, you’ll be able to build applications for problems like:

  • text classification (examples are sentiment analysis and spam detection)
  • neural machine translation
  • question answering

This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Rating: 4.6

Duration: 9 hours | Lectures: 65 | Pricing: $45

Info: Visit the website and avail of an amazing offer of 80% off

Natural Language Processing – Coursera

This online course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well.

The final project is devoted to one of the hottest topics in today’s NLP. You will build your own conversational chatbot that will assist with search on the StackOverflow website.

The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. 

will discuss word alignment models in machine translation and see how similar it is to the attention mechanism in encoder-decoder neural networks.

Rating: 4.5

Duration: 32 hours | Pricing: $45

Info: Visit the website for more Financial Aid

Natural Language Processing in TensorFlow – Coursera

This course will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an  LSTM on existing text to create original poetry!

Rating: 4.6

Duration: 32 hours | Total Quizzes: 13

Pricing: $54

Info: Visit the website for more Financial Aid

Natural Language Processing Specialization – Coursera

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.

This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.

You will learn:

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.

Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.

Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

There are 4 courses in this specialization that you can learn with it.

  • NLP with classification and vector spaces 
  • NLP with Probabilistic Models 
  • NLP with Sequence Models 
  • NLP with attention models 

Rating: 4.6

Duration: 128 hours | Total Quizzes: 51

Pricing: $54

Info:  Visit the website for more Financial Aid.

Conclusion:

This is the best list for the Best Natural Language Processing Courses. If you are having any doubts or any other best NLP courses then feel free to comment in comment.

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