Taking a few natural language processing (NLP) courses designed for beginners is the ideal approach to getting started in anything related to computer science.
Natural language processing programs will educate you on the fundamentals of natural language processing and may even prepare you for advanced NLP certification training.
Because the need for those skilled in NLP is growing, many NLP courses online are popping up that offer to teach you these skills. With so many options out there, choosing the best NLP courses for your needs can be a bit difficult.
That’s why we’ve made this list of the top 5 best Coursera Natural Language Processing Courses offered by the highly esteemed online learning platform Coursera.
But before we get into that…
What Is Natural Language Processing?
Within the field of computer science lies a specialized and intricate subfield known as natural language processing.
Natural language processing is the ability of a computer program to understand human language in its spoken and written forms.
This type of processing makes up a part of what we now refer to as artificial intelligence (AI).
Natural language programming has many different applications in the real world, including medical research, search engines, and business intelligence, to name a few.
With the demand for those with NLP skills going up and not enough people to fill the roles, the average salary for NLP professionals is around $124,000.
Additionally, it’s a skill that’s in extremely high demand in the fields of computer science, data science, and even marketing.
The processing of natural language presents a one-of-a-kind set of challenges. People use languages that are difficult for computers to understand by default.
NLP is used in user interfaces, artificially intelligent algorithms, and the mining of large amounts of data in today’s world.
If the computer can process natural language, then it will be possible to extract more insights and patterns from the data.
If you want to learn natural language processing, then try out some of the programs offered by Coursera below!
What Is A Specialization?
Before we tell you all about what Coursera has to offer, we have to explain how the course will be taught. This program is what Coursera calls a Specialization.
A Coursera Specialization is a collection of NLP online courses that are all related to one another and are aimed to help you become an expert on a certain subject.
Some of the most condensed Specializations consist of as little as three courses and can be completed in a matter of a few months.
Longer Specializations might consist of ten or more individual courses and take anything from six months to a year to complete.
You can get started with a Specialization by signing up for the full Specialization or just one of the courses that make up the Specialization.
You are awarded a Specialization Certificate if you have completed a Specialization. You will also receive Course Certificates once you have finished each course that is required for a Specialization.
About This Specialization
Enter the field of NLP through this program. Learn cutting-edge skills in NLP through a total of four different hands-on classes that Include all of the most recent improvements and updates in this field.
Taking around 4 months to complete if you work 8 hours a week, this specialization will allow you t become an expert in this field.
Natural Language Processing is a subfield of computer science, linguistics, and artificial intelligence that makes use of algorithms to read and modify human language.
As the field of artificial intelligence (AI) continues to develop, there will be an increased demand for individuals who are adept in the construction of models that can analyze speech and language, discover contextual patterns, and provide insights from text and audio.
After completing this Specialization, you will have the knowledge and skills necessary to design natural language processing applications that perform tasks such as sentiment analysis and question-answering, develop programs that translate languages and summarize text, and even construct chatbots.
These and other applications that make use of natural language processing are going to be at the front of the impending transition to an AI-driven era.
This Specialization was developed and is being taught by two professionals who are experts in natural language processing, machine learning (ML), and deep learning (DL).
Younes Bensouda Mourri is a member of the Artificial Intelligence faculty at Stanford University, where he also contributed to the development of the Deep Learning Specialization.
Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer study are all co-authored by Lukasz Kaiser, who is also a Staff Research Scientist at Google Brain and the creator of the Transformer paper.
You Will Learn
- How to carry out sentiment analysis, finish analogies, and translate words, logistic regression, naive Bayes, and word vectors.
- Implementing autocorrect, autocomplete, and identifying part-of-speech tags for words will be accomplished with the use of dynamic programming, hidden Markov models, and word embeddings.
- Trax will be used to perform sentiment analysis, text production, and named entity recognition with the use of recurrent neural networks, LSTMs, GRUs, and Siamese networks.
- Encoder-decoder, causal, and self-attention are three machine learning techniques that will be used to translate whole phrases, summarize text, construct chatbots, and answer questions.
Interested in this natural language processing course and want to know more about the content? Then keep reading as some of the following courses are the ones that make up this Natural language Program Specialization.
You are still able to take these courses individually if you only want to learn something specific or you don’t have the time to commit to the full specialization.
You will learn the following in the first class of the Natural Language Processing Specialization:
- How to conduct a sentiment analysis of tweets by first utilizing logistic regression, and then moving on to naive Bayes
- Utilize PCA to lower the dimensionality of the vector space and show those relationships, and then use vector space models to uncover relationships between words.
If you are a software engineer who wants to create scalable algorithms that are powered by AI, you need to learn how to use the tools that are used to create them.
TensorFlow is a widely used open-source framework for machine learning, and basic neural language processing and you will study best practices for utilizing TensorFlow as part of this Specialization.
TensorFlow will be utilized throughout the third and fourth courses of the deeplearning.ai TensorFlow Specialization, in which you will construct natural language processing systems.
In this course, you will learn how to process text to feed it into a neural network.
This will include the processes of tokenizing and modeling phrases as vectors. In addition to this, you will learn all about RNNs, LSTMs, and GRUsin TensorFlow.
At the end, you will have the opportunity to train an LSTM on pre-existing text to generate original poetry!
Both the Machine Learning course and the Deep Learning Specialization offered by Andrew Ng are designed to educate students on the fundamental and fundamentally significant concepts that underpin the fields of machine learning and deep learning frameworks.
This new TensorFlow Specialization from deeplearning.ai teaches you how to use TensorFlow to implement those concepts so that you can begin constructing scalable models and applying them to real-world situations.
We advise that you participate in the Deep Learning Specialization to acquire a more in-depth knowledge of the operation of neural networks.
On This Course, You Will
- Create systems for natural language processing with the help of TensorFlow.
- Perform operations on the text such as tokenization and the representation of sentences as vectors.
- TensorFlow may be used to train RNNs, GRUs, and LSTMs.
- Train LSTMs on pre-existing text to generate new poetry and other creative works.
You will learn the following in the fourth free online training course of the Natural Language Processing Specialization:
- Use an encoder-decoder attention model, to translate entire English sentences into German.
- Construct a model of a Transformer to summarize the material.
- Perform question answering by utilizing the T5 and BERT models, and d) Construct a chatbot by making use of the Reformer model.
Learners should be proficient in calculus, linear algebra, and statistics, as well as have a working knowledge of machine learning, intermediate Python programming language, and experience with a deep learning framework (for example, TensorFlow or Keras) to do this course.
Before beginning this class, you will need to ensure that you have successfully finished course 3, which is titled “Natural Language Processing using Sequence Models.”
This course is part of the Guided Projects that Coursera offers.
Coursera Guided Projects
You can acquire skills that are relevant to your profession in less than two hours by participating in Guided Projects provided by Coursera and receiving step-by-step direction from an instructor.
You can build the work skills you need, right when you need them because they involve a lower time commitment and provide practice using technologies in real-world circumstances.
The projects span a wide range of topics, including the development of fundamental abilities in business, technology, and data, as well as the study of methods for developing transformational algorithms, such as neural networks and Markowitz models.
Guided Projects provide targeted and time-efficient approaches to learning skills that are in high demand, regardless of the subject matter that interests you.
About This Course
The goal of this hands-on project is to train a Naive Bayes classifier to predict the sentiment of thousands of tweets from T