Nlp specialization coursera

Nlp specialization coursera DEFAULT

5 Best Natural Language Processing Courses for 2022

Natural Language Processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages.

This branch of AI contains various subfields like speech recognition, text analysis, speech synthesis, dialogue systems, etc.

All of these are extremely helpful in building a system that performs language-based tasks, notably grammar checkers, voice text messaging, chatbots, and many more.

As natural language processing becomes a crucial element of numerous software, demand for its specialists skyrockets. This fact makes NLP as an unsurprisingly lucrative career skill.

According to Ziprecruiter’s data, specialists in the US who master NLP earn as much as $123,974, which is significantly higher than most programming jobs.

If you are looking to get started with NLP, I recommend taking online courses on the topic. However, you cannot just take random natural language processing courses, as some lack quality and can do you more harm than good.

I decided to do the heavy lifting for you. This post features a curated list of the best NLP courses available in the market, along with their pricing and pros and cons for each course. You can then select the one that suits your learning style and start learning right away.

Affiliate Disclosure: This post from Victory Tale contains affiliate links. If you purchase NLP courses through them, we will receive a small commission from course providers.

Nonetheless, we always value integrity and prioritize our audience’s interests. You can then rest assured that we will present each NLP course truthfully.

Things You Should Know

Prerequisites

Natural Language Processing is an advanced concept. Thus, you need to have background knowledge in the following before taking any course.

The links below direct to lists of the best courses on that topic.

Regarding other programming languages, I have found few courses that use R. However, I don’t think their quality is sufficient. Hence, all courses that this post features use Python.

Criteria

Below are my criteria for the best natural language processing courses.

  • Credible instructors
  • User-friendly, reliable learning platform
  • Up-to-date (most of the course content is not outdated)
  • High-quality course materials
  • Provide excellent value for money
  • Positive reviews from real students
  • My personal experience with the course (if any) must be positive.

1. Become a Natural Language Processing Expert

This nanodegree program from Udacity is unarguably one of the most comprehensive natural language processing courses available online.

In collaboration with Amazon Alexa and IBM Watson, this program provides students with opportunities to learn natural language processing from experts who have had years of experience in the field.

Udacity's nanodegree program offers one of the best natural language processing courses

Course Content

This program consists of three minor courses as follows.

1. Introduction to Natural Language Processing – The first course will explain the text processing concepts, including stemming and lemmatization. Later on, you will use machine learning methods in sentiment analysis.

2. Computing with Natural Language – The second course will discuss advanced NLP techniques such as word embeddings, deep learning attention, and how to use recurrent neural networks (RNNs) to build a translation model.

3. Communicating with Natural Language – The final course will drill deep into voice user interface techniques, which can be used to build an automatic transcription tool or AI voice generator.

Each minor course has quizzes, assignments, and, most importantly, real-world projects. For example, you will build a speech tagging model in the first course and an English-French machine translation tool in the second.

Once you complete the project, you will possess three real-world tools that you can add to your Github portfolio to showcase your skills and gather tons of hands-on experience that you can develop upon in the process.

Udacity suggests spending 10-15 hours per week on the program, and you will complete it in approximately three months. As the course is 100% self-paced, you can freely set your schedule.

However, keep in mind that your tuition (see #pricing below) will increase along with the time you spend on the course. Thus, it would be optimal if you finish the program in three months or even earlier. Rushing is not recommended, though.

Student Support

Once you enroll in Udacity’s program, you will gain access to three mini-bootcamp support as follows.

Technical Support – If you get stuck, encounter any technical issues or have any questions regarding the courses, you can email your mentor 24/7.

Most likely, the personalized response from your mentor will arrive in less than an hour. Hence, you don’t have to wait forever for the instructor to reply to your inquiries again.

Project Reviews – Once you work on the projects, you can make unlimited requests for experts to review your projects. They will provide personalized responses and inform you about best practices to further strengthen your skills over time.

Best of all, you will receive a personalized review in slightly more than an hour. You can then improve or modify your project and ask experts to review it again, hence creating a healthy feedback loop that aids your learning.

Career Services – The team will review your resume, LinkedIn profile, and Github portfolio to ensure that your job application is up to professional standards. This service is highly beneficial if you aim for a lucrative career at leading tech giants like Amazon, Facebook, or Google.

From an overall perspective, this level of support differentiates Udacity from its competitors. The team will support you throughout the entire learning journey, thus permanently eliminating any frustration caused by the instructor’s inactivity, and bugs & errors on the assignments and projects.

Pricing

Udacity uses a subscription model. The monthly tuition is $399 or $339 (15% off) if you pay for three months upfront.

However, Udacity frequently offers personalized discounts that can be as high as 75%. You just need to create a free account, and you will gain access to the deals right away.

With these discounts, you can enroll in a top-notch at a low price of $100 per month. Considering all course materials, projects, and quality support, I am confident that the program is worth the price.

Pros and Cons

Pros

  • Learn NLP from experts with years of experience in the field
  • Well-structured, up-to-date curriculum
  • Self-paced learning
  • Numerous practical exercises and hands-on projects to obtain hands-on experience
  • Excellent and speedy technical support that apparently trumps other courses
  • Unlimited Project Reviews
  • Career services are a big plus.

Cons

  • Expensive compared to other natural language processing courses. However, discounts can cut the tuition down by 75%.
  • Require a high weekly commitment if you don’t want to pay extra tuition

2. Natural Language Processing Specialization

This Coursera specialization from Deeplearning.ai offers exceptional training that helps students break into the NLP space. You will learn from three experts who specialize in training AI practitioners.

Note: You will need background knowledge in calculus and linear algebra for this specialization.

NLP specialization from Deeplearning.ai is the best natural language processing course.

Course Content

The specialization comprises four courses as follows.

1. NLP with Classification and Vector Spaces – The first course in specialization will introduce you to Natural Language Processing. Later on, you will start working on the projects.

First, you will use logistic regression and Naive Bayes to perform sentiment analysis of people’s tweets. Then, you will use vector space models to find the relationship between words and visualize them.

Finally, you will build a basic machine translation algorithm that can translate between English and French.

2. NLP with Probabilistic Models – In essence, the second course will teach you how to build auto-complete and auto-correct algorithms popularly used in numerous editing software. You will use different tools and techniques, such as the N-gram language model and dynamic programming.

Subsequently, you will build a Word2Vec model that uses neural networks to compute word embeddings.

3. NLP with Sequence Models – The third course will drill deep into intermediate and advanced topics. You will train neural networks to perform sentiment analysis, generate synthetic Shakespeare text, perform NER (named entity recognition), and compare questions to identify synonyms.

4. NLP with Attention Models – The final course will cover advanced real-world usage of NLP. You will use various models and tools to perform advanced tasks, including translating English to German, summarize text, and built a chatbot.

As a project-based specialization, you will spend most of your learning time on NLP tasks while learning crucial concepts along the way. Once you complete all the courses, you will be able to built NLP systems confidently on your own.

Regarding the pace, you should spend 7 hours per week on the program, and you will finish it in 4 months.

Auditing the course is entirely free. Alternatively, the full course costs $49 per month. I suggest you start a free 7-day trial to evaluate the entire program before making decisions.

Pros and Cons

Pros

  • Learn from leading AI researchers
  • Well-structured and comprehensive curriculum
  • Project-based learning: You will work on numerous tasks and be able to build NLP systems of your own.
  • Manageable pace

Cons

  • Most reviewers complained that some parts of the program do not have adequate explanations.

Lazy Programmer’s Series

If you are looking for affordable courses to master NLP, you don’t need to look elsewhere besides Udemy.

Lazy Programmer is a knowledgeable and experienced AI engineer who creates a series of machine learning, deep learning, and AI courses on Udemy. I have taken some courses with him and appreciated the quality. Hence, I decided to recommend him to you.

Note: Course #3 to #5 on this list are parts of his series. However, unlike Udacity and Coursera, you have to purchase each separately since Udemy offers no bundle pricing.

3. Data Science: Natural Language Processing (NLP) in Python

The third course will explain basic concepts of Natural Language Processing and its widespread applications. You will then work on several projects that will help you enhance your knowledge and skill.

Best NLP course from Lazy Programmer

Course Content

Below is a summary of what you will learn from the course.

  • Machine Learning Review (Regression, Classification, Comparison of machine learning models)
  • Markov Models (Building a text classifier and a language model)
  • Write your cipher decryption algorithm
  • Build a spam detector and a sentiment analyzer
  • Latent semantic analysis
  • Build an article spinner (using the Trigram model)

The video content is 12 hours long. You need to prepare to get your hands dirty. This course does not teach you how to use the API. Instead, you will learn to build these tools from scratch and understand the entire process, which is an excellent strategy to develop your skills gradually.

Reviews: 4.6/5.0 from 10000+ ratings

4. Natural Language Processing with Deep Learning in Python

The fourth course will be more in-depth than the third. You will understand how to utilize deep learning approaches with Natural Language Processing.

Note: You will use Tensorflow or Theano in all parts of this course (you can choose either, as the instructor will provide instructions to both). Thus, you should have some experience in using either library beforehand.

Second best NLP courses

Course Content

A summary of all course content is as follows:

  • Theano and Tensorflow review
  • Introduction to Word2vec
  • Language modeling and neural networks
  • Word embeddings (using Word2vec or GloVe)
  • Use neural networks to provide solutions to NLP problems
  • Recursive neural networks

The video content is 12 hours long. However, the actual length is only 10 hours, as Lazy Programmer utilizes the same content (Python Review and learning strategies) in all of his courses.

Reviews: 4.6/5.0 from 6590+ ratings

5. Deep Learning: Advanced Natural Language Processing and RNNs

This advanced course will tackle the most challenging concepts of Natural Language Processing. You will learn to build a text classification system that can be useful in performing several tasks, such as spam detection, sentiment analysis, and many more.

Note: You will be using Tensorflow and Keras. It is thus vital that you master them before enrolling.

Deep Learning: Advanced Natural Language Processing and RNNs

Course Content

Below is a summary of the course content.

  • RNNs, CNNs, and word embeddings
  • Bidirectional RNNs
  • Sequence-to-Sequence Model (Seq2Seq)
  • Attention
  • Memory Networks

Similar to his other courses, this one is also project-based. You will create tools that are in demand (chatbots, spam detectors, etc.) utilizing advanced NLP techniques and deep learning.

This course is relatively short. By taking duplicate content off, you will be left with only 6 hours of content. However, very few online courses cover such advanced topics. This fact makes this course worth taking.

Reviews: 4.6/5.0 from 3600+ ratings

Pros and Cons of the Series

Pros

  • Learn from a highly knowledgeable AI engineer
  • Well-structured curriculum
  • Include detailed explanations of both the concepts and the how-tos.
  • Project-based learning: You will work on multiple projects and understand each concept through doing and experimenting
  • Lifetime Access + 30-day money-back guarantee

Cons

  • Some courses are math-heavy, which may not be desirable for some students.
  • Purchasing all three courses can be expensive, though Udemy frequently offers discounts (you can get it at $20 each or lower)

Other Alternatives

Natural Language Processing and Language Understanding in Educational Research – This NLP course on the edX platform aims toward educational researchers.

However, as of September 2021, this course has been archived. If you are interested, you need to wait for future start dates to be announced.

NLP – Natural Language Processing with Python – This Udemy course from Jose Portilla provides an excellent overview of natural language processing. However, Jose has not updated this course since 2019, so most of the course content is now not up-to-date.

Deep Learning and NLP A-Z™: How to create a ChatBot – This Udemy course from Hadelin de Ponteves and Kirill Eremenko covers the same content as the fifth course on the list. However, the course uses Seq2Seq models in Tensorflow 1.0, making the content outdated.

Still, both instructors are highly knowledgeable, and they explain NLP theories very well. If you still have problems with the concepts, you might want to give this course a try.

Datacamp’s Natural Language Processing in Python – Datacamp is an online school that teaches data science through interactive learning. The school offers a learning track comprising 5 NLP courses that every student can take to enhance their skills.

However, Datacamp’s courses are beginner-friendly but not very in-depth. If you already have some programming and data science experience, these courses will not be challenging at all.

Besides these options, some leading universities offer free online courses on NLP. However, most, if not all, of these courses lack updates and student support. I chose not to include those on the list as the learning experience is unlikely to be satisfying.

Sours: https://victorytale.com/best-natural-language-processing-courses/

First Impressions Of Deeplearning.ai

Opinion

This is an objective and unsolicited review. I was not asked to or paid to write this review.

I recently enrolled in Andrew Ng’s deeplearning.ai. Specifically, the Natural Language Processing (NLP) specialization. My aim was to buff up my NLP knowledge a bit because that’s something I don’t touch upon as much in my day-to-day work.

Before diving in, it might be helpful if I lay out what I was hoping to achieve from taking this course:

  • Freshen up on NLP concepts and more critically its practical applications.
  • Build some cool NLP models with some guidance from the course.
  • Get some inspiration on where I might apply NLP in my day job to produce insights or something novel.
  • Coding practice.

There are certain aspects of data science where I possess lots of experience and a fair bit of expertise, but there are many more areas where I have much to learn. NLP is one of the latter, so I was hoping this course could help fill some of the gaps.

I’ve honestly enjoyed it so far. Working a full-time job (and doing fair share of blogging on the side) means that I don’t have a ton of spare hours. Each module is pretty quick to finish — they consist of a few 5–10 minute videos where a lecturer highlights the concepts via a series of well laid out examples. For example, the module I most recently completed was on the Naive Bayes classifier.

It starts with an explanation of Bayes’ Theorem and then moves to how we can use the insights of Bayes’ Theorem and the Naive Bayes classifier to classify documents (such as message board threads or tweets) in terms of their sentiment. Each module culminates in a graded lab where we build and train a machine learning model somewhat from scratch (more on this later).

Deeplearning.ai does a good job in terms of helping you look under the hood of each machine learning model. For example, for sentiment analysis with Naive Bayes, it comprehensively walks through how we can calculate the conditional probability of each word (conditional probability here means the probability of a word appearing in a positive message vs. a negative message). Then it shows us the math behind how to combine the conditional probabilities of each word in a message into a sentiment prediction for the entire message (spoiler: we can do so by summing the log-likelihoods).

I left each module feeling like I understood the bigger picture:

  • How the model works and what its key underlying assumptions are.
  • What are some real world applications the model is used for.

But where I felt like deeplearning.ai came up short was in terms of depth and practice.

The modules are designed to be breezed through. If you come in with some familiarity with data science, it should take around three to five hours to finish each one.

So you definitely feel accomplished as you speed through it. The problem is rigor. Unless you spend extra time reading and rereading and working things out with pen and paper yourself, you won’t retain a whole lot of the course material.

I expected there to be a lot more exercises where practice and repetition would really solidify the concepts in my brain. Unfortunately, this is not the case. The intermediate labs don’t require any code writing at all — you just click through each code block and look at the output. Yes, it’s somewhat helpful to read the comments and the code itself is well-written (so I did pick up some new things from reading the instructors’ code). But for those of us who like to learn by doing, there is just not that much to do.

Even the final graded lab of each module is a bit too simple. Most of the functions and code that you need to execute are already written for you and you are just filling in a few missing chunks. The reason for this I assume is that it makes it easy to automatically grade — the labs are graded by machine not hand (otherwise the program couldn’t enroll the tens of thousands of students that it has).

Anyone hoping to increase their mastery of Python through deeplearning.ai’s labs will probably be sorely disappointed. My suggestion to the curriculum managers would be to include much more exercises, even if it means they have to be non-graded and optional (just providing solutions is fine by me). Less handholding — let students feel what it’s like to clean some data and get their hands dirty.

Also, data science is as much or more about asking the right questions as it is about building a good model. Instead of just saying here are all the applications, it would be helpful to make students think about what the practical applications of the technology are. Test the student through a case study or two about how the model he or she just learned about could be used to solve an analytics problem in the wild.

Do I regret enrolling? No. I have been able to learn some new things from deeplearning.ai. I also understand now that its goal (and the way it’s presented) is probably not to rigorously train its students into functioning data scientists; rather, I’d guess the primary goal is to let a broad set of people know more about the power and versatility of machine learning (increasing overall adoption). It’s meant more to be an appetizer that piques our curiosity and not the main course. So if you are interested in enrolling, I would bear that in mind.

You will find a broad overview of machine learning (concepts and models) presented in an attractive and easy to digest manner. But if you want to build your skills through rigorous practice and a steep learning curve, you probably won’t find that here. Disclaimer: this is my first impression and as I continue through the modules, my opinion may change. At that point I will either edit this post or write an updated one. Cheers!

Sours: https://towardsdatascience.com/first-impressions-of-deeplearning-ai-53925b012f63
  1. Sony str dn1080
  2. Zillow lee center ny
  3. Midwest transport jobs

Description

Natural Language Processing Specialization is a natural language processing course or NLP . NLP uses several algorithms to understand and modify Adam\’s language . This technology is widely used in the field of machine learning are used, and the developers of it to build models that speech and language analysis work. the template text will discover it, and the insight of the text and sound business works advantage . With the use of this course and mastering this technology, are able to the application of NLP to make your own; the instant that the questions and answers period). emotion analysis, they are able to translate and summarize the text are . This tool, along with other NLP-based tools, is the highest layer in the future era of artificial intelligence .

This course will cover a variety of topics . You learn how to use logical regression and Bayes categories to analyze emotions, complete similarities and translate words . Then you will learn the use of intelligent programming and Hidden Markov models for automatic vocabulary correction, Sentence Completion and word role recognition . The use of neural networks iterative and dense, for LSTM network and the Siamese in the library TensorFlow and Trax for the analysis of more advanced emotions, the construction of the text and detection of duplicate questions from other topics of this course are . Finally, you will get acquainted with the implementation of advanced machine translation, text summarization and FAQ to build the chat bot . It should be noted that the instructors of this course are lecturers of artificial intelligence at Stafford University and a member of the research team at Google Brain .

What things to learn

Use logical regression Bayes categorizer and an array of words to analyze emotions complete similarities and translate vocabulary
Use intelligent programming, Hidden Markov models and embedding words to auto-correct vocabulary, complete sentences and recognize the role of word in speech
Use repeatable, dense, LSTM, Grus and Siamese networks in TensorFlow and Trax libraries for more advanced emotion analysis and text building
Use encryption and decryption causal relations and dependencies between words to summarize text and FAQ to build Chatbot

Specifications of Natural Language Processing Specialization

Publisher: Coursera
Lecturer: young Bensouda Mourri, Łukasz Kaiser, Eddy Shyu
Language: English
Training level: Medium
Quantity: 4 periods
Duration of the course: with the proposed time of 5 hours per week, approximately 4 months

Courses

  1. Natural Language Processing with Classification and Vector Spaces
  2. Natural Language Processing with Probabilistic Models
  3. Natural Language Processing with Sequence Models
  4. Natural Language Processing with Attention Models

Prerequisites

  • Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. If you would like to brush up on these skills, we recommend the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng.

Images

Natural Language Processing Specialization

Sample movie

Installation guide

After the Extract with the Player your custom view.

Subtitles: English

Quality: 720p

Download link

Download Coursera – Natural Language Processing Specialization 2020-10

Password file(s): www.downloadly.ir

File size

987 MB

Sours: https://downloadly.net/2020/11/19647/09/natural-language-processing-specialization/18/
Noam Chomsky: Language, Cognition, and Deep Learning - Lex Fridman Podcast #53

By Matthew Mayo, KDnuggets.

comments

Natural language processing (NLP) is an in-demand set of skills among employers and one of the most sought-after and pursued topics among learners.

Previously, we presented 10 Free Top Notch Natural Language Processing Courses, a collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics. But with spring now upon us, what better time to have a fresh look at a topic like NLP, and do so with some new learning resources.

Figure
From CMU's Algorithms for NLP Lecture 1: Introduction slides

 

Here is a small collection of 3 curated NLP courses ready to help you get your spring learning on, and help increase your understanding and expertise of the vast field of natural language processing.

 
1. Algorithms for NLP, Carnegie Mellon University

This CMU course is taught by Emma Strubell, Yulia Tsvetkov, and Robert Frederking.

Available resources include slides, readings, projects, assignments.

This course will explore foundational statistical techniques for the automatic analysis of natural (human) language text. Towards this end the course will introduce pragmatic formalisms for representing structure in natural language, and algorithms for annotating raw text with those structures. The dominant modeling paradigm is corpus-driven statistical learning, covering both supervised and unsupervised methods. Algorithms for NLP is a lab-based course. This means that instead of homeworks and exams, you will mainly be graded based on four hands-on coding projects.

Slides, materials, and projects for this iteration of Algorithms for NLP are borrowed from Jacob Eisenstein’s course at Georgia Tech, Dan Jurafsky at Stanford, Dan Klein and David Bamman at UC Berkeley, and Nathan Schneider at Georgetown University.

 

2. Neural Networks for NLP, Carnegie Mellon University

This CMU course is tuaght by Graham Neubig, with co-instructor Pengfei Liu.

Available resources includes videos, slides, readings, projects, assignments, code.

You can find a direct link to the course lecture videos here.

Neural networks provide powerful new tools for modeling language, and have been used both to improve the state-of-the-art in a number of tasks and to tackle new problems that were not easy in the past. This class will start with a brief overview of neural networks, then spend the majority of the class demonstrating how to apply neural networks to natural language problems. Each section will introduce a particular problem or phenomenon in natural language, describe why it is difficult to model, and demonstrate several models that were designed to tackle this problem. In the process of doing so, the class will cover different techniques that are useful in creating neural network models, including handling variably sized and structured sentences, efficient handling of large data, semi-supervised and unsupervised learning, structured prediction, and multilingual modeling.

 

3. Natural Language Processing Specialization, DeepLearning.AI

This Coursera-hosted DeepLearning.AI specialization (4 courses) is taught by Younes Bensouda Mourri, Łukasz Kaiser, and Eddy Shyu.

Available resources includes videos, slides, readings, projects, assignments, code (see note below).

Note that you can pay for a certificate, which also gets you access to tutors and assignment grading, but other materials are freely-accessible for those auditing.

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. 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.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

 

These are 3 free NLP courses you can take in your spare time to ramp up your skills. Looking for more? Be sure to check out 10 Free Top Notch Natural Language Processing Courses!

 
Related:

Sours: https://www.kdnuggets.com/2021/03/3-more-free-nlp-courses.html

Specialization coursera nlp

My GAN Specialization repository

Click on the image

This repository contains my personal notes on DeepLearning.ai NLP specialization courses.

DeepLearning.ai contains four courses which can be taken on Coursera. The four courses are:

  1. Natural Language Processing with Classification and Vector Spaces
  2. Natural Language Processing with Probabilistic Models
  3. Natural Language Processing with Sequence Models
  4. Natural Language Processing with Attention Models
  • Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. 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.

  • This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words, and use locality sensitive hashing for approximate nearest neighbors.

• Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.

• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.

• Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering and to build chatbots. Models covered include T5, BERT, transformer, reformer, and more! Enjoy!

I share the assignment notebooks with my prefilled and from the contributors code structred as in the course Course/Week The assignment notebooks are subject to changes through time.

Once you enrolled to the course, you are invited to join a slack workspace for this specialization: Please join the Slack workspace by going to the following link deeplearningai-nlp.slack.com This Slack workspace includes all courses of this specialization.

Stargazers over time

Ibrahim Jelliti © 2020

Sours: https://github.com/ibrahimjelliti/Deeplearning.ai-Natural-Language-Processing-Specialization
New DeepLearning AI courses - AI For Medicine - Natural Language Processing Specialization

Natural Language Processing

I'm a "Computational Linguist" at Amazon & I would recommend that you spend some time thinking about what areas you're most interested in, what problems you're interested in solving, what burning questions about language you want answered, what kind of company you're interested in working at, etc.

Then once you narrow your scope, start just googling those keywords and look up people/papers that are doing those things currently and figure out what kinds of technology they use.

Some other suggestions:

  • Google "computational linguist jobs" and read through job descriptions. It will go a long way to let you know what the "industry standard" for a computational linguist is
  • Look at syllabi for current MA/PHD programs in CL. They might point you to books and concepts that you want to learn more about. Some of them might have slides and resources directly on their program pages
  • Look at what people are presenting at conferences and see what grabs your attention. For example, I went to NAACL in 2019 and there were really cool presentations about building ML models to find political leanings of blogs and news sources. cool applications of nlp i hadnt thought of
  • Look into coursera udemy courses for NLP. Things like: Udacity NLP Nanodegree & Coursera: NLP Specialization

For books & projects, I think the other commenter's paper list will help a lot but these are the books that I've worked through as I was developing my skills: https://web.stanford.edu/\~jurafsky/slp3/ & https://www.nltk.org/book/

Hope this helps! Ultimately you'll need to do the work to figure out what you want to work on and learn. It's gonna take years to develop the skills but there are so many interesting problems that can be solved with the skills of a CL, so i wish you luck

Sours: https://reddsera.com/specializations/natural-language-processing/

Similar news:

Natural Language Processing Specialization

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. 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. This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems. 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.

This Specialization is for students of machine learning or artificial intelligence as well as software engineers looking for a deeper understanding of how NLP models work and how to apply them. Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. If you would like to brush up on these skills, we recommend the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Course 1: Classification and Vector Spaces in NLP

This is the first course of the Natural Language Processing Specialization.

Week 1: Logistic Regression for Sentiment Analysis of Tweets

  • Use a simple method to classify positive or negative sentiment in tweets

Week 2: Naïve Bayes for Sentiment Analysis of Tweets

  • Use a more advanced model for sentiment analysis

Week 3: Vector Space Models

  • Use vector space models to discover relationships between words and use principal component analysis (PCA) to reduce the dimensionality of the vector space and visualize those relationships

Week 4: Word Embeddings and Locality Sensitive Hashing for Machine Translation

  • Write a simple English-to-French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbors search

Course 2: Probabilistic Models in NLP

This is the second course of the Natural Language Processing Specialization.

Week 1: Auto-correct using Minimum Edit Distance

  • Create a simple auto-correct algorithm using minimum edit distance and dynamic programming

Week 2: Part-of-Speech (POS) Tagging

  • Apply the Viterbi algorithm for POS tagging, which is important for computational linguistics

Week 3: N-gram Language Models

  • Write a better auto-complete algorithm using an N-gram model (similar models are used for translation, determining the author of a text, and speech recognition)

Week 4: Word2Vec and Stochastic Gradient Descent

  • Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model

Course 3: Sequence Models in NLP

This is the third course in the Natural Language Processing Specialization.

Week 1: Sentiment with Neural Nets

  • Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets

Week 2: Language Generation Models

  • Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model

Week 3: Named Entity Recognition (NER)

  • Train a recurrent neural network to perform NER using LSTMs with linear layers

Week 4: Siamese Networks

  • Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning

Course 4: Attention Models in NLP

This is the fourth course in the Natural Language Processing Specialization.

Week 1: Neural Machine Translation with Attention

  • Translate complete English sentences into French using an encoder/decoder attention model

Week 2: Summarization with Transformer Models

  • Build a transformer model to summarize text

Week 3: Question-Answering with Transformer Models

  • Use T5 and BERT models to perform question answering

Week 4: Chatbots with a Reformer Model

  • Build a chatbot using a reformer model

Certificate

Sours: https://github.com/amanjeetsahu/Natural-Language-Processing-Specialization


2160 2161 2162 2163 2164