Learn Natural Language Processing: Find the Best Courses
How often do you turn to Siri, Google, or Alexa to help you complete your daily tasks? Since most of us are never without our digital devices, your answer is probably “very often.” These virtual assistants function using natural language processing and have become an integral part of the routines of many Americans.
However, many of us are not aware of how natural language processing (NLP) works. These programs enable a computer to understand your language and commands, then execute them perfectly. Below, we’ll take a closer look at how NLP is developed and used, and how you can get involved in this field.
What Is Natural Language Processing?
Natural language processing is the interaction between computers and humans using human language. It is a branch of Artificial Intelligence (AI) that blends linguistics and computer science.
Through NLP, a computer is able to read, understand, and process human languages, then carry out tasks accordingly. The programming can also be used to process and analyze large amounts of language data.
NLP is the backbone of voice recognition programs, predictive writing, autocorrect, and machine translation software such as Google Translate. Although NLP is not a new technology, it has rapidly developed in recent years. The possible applications for this type of programming are numerous.
Which Jobs Use Natural Language Processing?
As the possible uses for NLP expand, there is no shortage of jobs in this sector. NLP’s recent development is due to the availability of big data, faster computers, and better algorithms. It can be used in many fields to bridge the gap between human language and computers to make our work and personal tasks more manageable.
Besides the few positions mentioned below, there are also other roles available in this field, such as NLP architects, AI data scientists, and Python engineers who specialize in NLP.
Natural language processing engineer. An NLP engineer is responsible for creating the interaction between human language and a machine. These professionals design and develop NLP systems by using the right algorithms to guide the NLP to achieve its tasks.
An NLP engineer will need to be able to understand text representation and machine translation, and should know the programming languages Python and Java. They should also be analytically-minded problem solvers.
NLP data scientist. An NLP data scientist is responsible for selecting data sources to input into a machine and preparing and analyzing data.
NLP application developer. An NLP application developer designs and builds applications that use NLP technology. Besides vast technical knowledge in programming, analytical and problem-solving skills are also prerequisites for this job.
NLP researcher. NLP researchers mainly deal with modeling languages and developing algorithms that will work with NLP models.
Steps to Learn Natural Language Processing
NLP’s rapid ascent as an integral aspect of our daily lives means there are also many positions available in this field. If you want to try your hand at a career in NLP, follow these basic steps.
Step 1: Learn Programming and Text Data
The first step to learning NLP is to get fluent in programming languages like Python and Java. Besides this, you should also get familiar with how to clean text data and learn manual tokenization, which is the splitting of sentences, paragraphs, and phrases into individual words.
Text preprocessing techniques are also important to learn at this stage. You can see a list of text preprocessing steps here.
Step 2: Get Familiar With Computational Linguistics and Word Vectors
Computational linguistics is the modern-day study of linguistics using computer science. It involves studying computer systems to produce natural languages. Word vectors represent the meaning of words by using numbered vectors.
Computational linguistics and word vectors are both essential to human-machine communication. These practices aim to help a computer understand the context and emotion behind a word.
Step 3: Study Deep Learning for NLP
Deep learning has become an important driver behind the recent advancements in NLP. Therefore, to succeed in this field, you will need to understand how deep learning influences NLP.
Deep learning uses the mechanisms based on the human brain to enable computers to recognize and process images in speech in real-time. To do this, deep learning programs mimic neurons in the brain.
Step 4: Learn About Sequential Modeling
Sequential modeling is the technique that has allowed machine translation to reach the same level as human translation. The real-time translation machines we use today are based on sequential modeling.
Besides translation programs, the chatbots used in call centers and virtual assistants on our smartphones are also a product of sequential modeling.
Step 5: Practice Transfer Learning
Transfer learning is the process of training a model using a large scale dataset, then using the model to jumpstart a new task. This means you won’t have to start from scratch on the new NLP program. The machine learning techniques BERT and GPT-2 are the most popular transformer-based models.
The Best Courses to Learn Natural Language Processing
NLP’s numerous applications mean that it is all around us since language is the key to communication. It enables us to understand computers, and vice-versa. Because knowledge in this field is in such high demand, taking a course in NLP is a good idea if you’re interested in a tech career. Below are a few of the best NLP courses.
- Provider: Datacamp
- Level: Beginner
- Duration: 4 hours
This course by Datacamp covers all the basics of NLP. It includes topics such as tokenization, topic extraction in a text, and the use of basic libraries. The class will also touch on how deep learning libraries can be used to solve common NLP problems. This is a foundational course that will be helpful to take as you learn programming languages such as Python.
- Provider: Coursera
- Level: Advanced
- Duration: About 32 hours
The programs offered by Coursera cover a wide range of topics on both basic and advanced NLP concepts. This includes sentiment analysis, summarization, and dialogue state tracking. The course will give you real experience in completing tasks such as text classification, duplicity detection, and named entity recognition.
- Provider: EdX
- Level: Expert
- Duration: About 7 hours
This course focuses on NLP tasks through the lens of AI. The course will guide you through machine learning techniques that will help you resolve NLP issues.
The lessons will cover Deep Semantic Similarity Models and statistical machine translation. They will also incorporate natural language understanding methods like image captioning.
- Provider: Stanford University
- Level: Expert
- Duration: 10 weeks
Among the topics covered in this online course are computational properties of natural languages, neural network models, word vectors, and machine translation. This is a professional-level course in which you will be awarded a certificate upon completion issued by the Stanford Center for Professional Development.
- Provider: University Of Oxford
- Level: Advance
- Duration: 2 months
This is an applied course that focuses on recent advances in speech and text generation and analysis using recurrent neural networks. The coursework also discusses the many possible applications of neural networks in NLP, including speech-to-text transcription, translation between languages, and question answering.
Why Learn Natural Language Processing?
As it is the underlying technology behind many of the apps we use daily, learning NLP can lead to a very rewarding career. NLP is an interesting field because it has revolutionized the way we interact with the machines around us.
The applications of NLP are numerous. It has made our communication faster and easier through virtual voice assistance. But it also has other uses. NLP has played a huge part in the growth of AI, which has in turn opened up many jobs in this industry.
With such a promising future for this technology, individuals with professional NLP skills will remain in high demand for years to come.