Natural Language Processing (NLP) is a field of computer science and artificial intelligence that deals with the interaction between computers and human language. It involves teaching computers to understand, interpret and generate human language, allowing them to perform tasks such as speech recognition, language translation, sentiment analysis, and more. With the rise of advanced technologies like machine learning and deep learning, NLP has become an increasingly powerful tool in a wide range of industries, from healthcare to finance to customer service. In this article, we'll explore what NLP is, how it works, and some of the most common applications of this exciting technology.
NLP stands for Natural Language Processing, which is a field of computer science and artificial intelligence that focuses on the interaction between computers and human language. NLP is used to analyze, understand, and generate human language, and it has a wide range of applications, including sentiment analysis, language translation, text summarization, and more. NLP algorithms can be used to process and analyze large amounts of text data, such as social media posts, customer reviews, and news articles, to extract insights and identify patterns. Overall, NLP is a powerful tool that enables computers to understand and interpret human language, and it has many practical applications in various industries.
Understanding Natural Language Processing (NLP)
Definition of NLP and its significance
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It is an interdisciplinary field that combines computer science, linguistics, and psychology to enable machines to process, analyze, and understand human language. The significance of NLP lies in its ability to facilitate communication between humans and machines, making it possible to automate tasks that involve natural language, such as speech recognition, text analysis, and sentiment analysis.
Historical background and development of NLP
The concept of NLP has its roots in the study of linguistics in the 1950s. However, it was not until the 1990s that NLP gained prominence with the emergence of the internet and the need for machines to process large amounts of unstructured text data. Since then, NLP has experienced significant development, driven by advancements in machine learning, deep learning, and big data analytics. Today, NLP is used in a wide range of applications, including chatbots, virtual assistants, sentiment analysis, and text classification.
NLP in the context of AI and machine learning
NLP is a critical component of AI and machine learning, enabling machines to understand and process human language. Machine learning algorithms are used to train NLP models to recognize patterns in language data, allowing them to make predictions and classify text based on its content. This makes NLP an essential tool for developing intelligent systems that can understand and respond to human language, making it possible to automate tasks such as customer service, sentiment analysis, and content classification.
Core Concepts of NLP
Syntax and Grammar Analysis
Role of Syntax in NLP
Syntax is the set of rules governing the structure of a language, including the arrangement of words, phrases, and clauses. In NLP, syntax plays a crucial role in analyzing and understanding the structure of natural language. By identifying the syntactic structure of a sentence, NLP algorithms can extract the meaning of individual words and their relationships to one another.
Parsing Techniques for Grammar Analysis
Parsing is the process of analyzing a sentence to determine its grammatical structure. NLP algorithms use parsing techniques to identify the grammatical structure of a sentence by identifying the relationships between words and phrases. One common parsing technique is dependency parsing, which involves identifying the grammatical relationships between words in a sentence, such as subject-verb or object-verb relationships.
Another parsing technique is constituency parsing, which involves identifying the syntactic structure of a sentence by dividing it into smaller units called constituents. Each constituent represents a single grammatical unit, such as a verb phrase or noun phrase.
Importance of Grammar in Understanding Language
Grammar is the set of rules governing the structure of language, including the arrangement of words, phrases, and clauses. In NLP, understanding the grammar of a language is essential for analyzing and understanding natural language. By identifying the grammatical structure of a sentence, NLP algorithms can extract the meaning of individual words and their relationships to one another.
Additionally, understanding grammar is crucial for developing NLP applications that require a deep understanding of language, such as machine translation, question answering, and text summarization. By analyzing the grammatical structure of a sentence, NLP algorithms can identify the meaning of individual words and their relationships to one another, which is essential for accurately translating text or summarizing lengthy documents.
Semantic Analysis and Understanding
Semantic representation of language
Semantic analysis is a key component of NLP that focuses on understanding the meaning of language. This process involves representing the meaning of words and phrases in a way that can be processed by a computer. One of the most popular methods for semantic representation is the use of Semantic Networks, which are graphical representations of the relationships between words and concepts.
Techniques for understanding meaning in NLP
There are several techniques used in NLP to understand the meaning of language. These include:
- Part-of-speech (POS) tagging: This involves identifying the part of speech of each word in a sentence, such as noun, verb, adjective, etc.
- Named Entity Recognition (NER): This involves identifying and categorizing entities in text, such as people, organizations, and locations.
- Sentiment Analysis: This involves determining the sentiment expressed in a piece of text, such as positive, negative, or neutral.
- Coreference Resolution: This involves identifying and linking mentions of the same entity throughout a text.
Challenges and limitations in semantic analysis
Despite the advances in semantic analysis, there are still several challenges and limitations in this area. These include:
- Ambiguity: Language is often ambiguous, and it can be difficult for a computer to determine the correct meaning of a word or phrase.
- Context: The meaning of a word or phrase can depend on the context in which it is used, and it can be difficult for a computer to understand the context of a sentence.
- Lack of common sense: NLP models often lack common sense and fail to understand the real-world implications of language.
- Cultural and linguistic differences: NLP models may not be able to understand the nuances of language across different cultures and languages.
Overall, semantic analysis is a critical component of NLP that allows computers to understand the meaning of language. However, there are still several challenges and limitations that need to be addressed in order to improve the accuracy and effectiveness of semantic analysis in NLP.
Named Entity Recognition (NER)
Definition and purpose of NER
Named Entity Recognition (NER) is a subtask of Information Extraction (IE) that focuses on identifying and classifying named entities in text. Named entities are words or phrases that represent real-world objects, concepts, or entities, such as people, organizations, locations, and events. The purpose of NER is to automatically extract and classify these named entities, making it easier to analyze and understand the content of text.
Techniques used for NER in NLP
There are several techniques used for NER in NLP, including rule-based, machine learning, and deep learning approaches.
- Rule-based NER: This approach relies on a set of predefined rules and patterns to identify and classify named entities. The rules are typically based on regular expressions or dictionary lookup.
- Machine learning-based NER: This approach uses machine learning algorithms, such as conditional random fields (CRFs) or support vector machines (SVMs), to learn the patterns and features of named entities from labeled training data.
- Deep learning-based NER: This approach leverages deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to learn and extract named entities from text.
Applications of NER in various domains
NER has a wide range of applications in various domains, including:
- Information retrieval: NER can be used to filter and retrieve relevant information from large text collections.
- Sentiment analysis: NER can be used to identify named entities related to opinions, emotions, or sentiment in text.
- Biomedical research: NER can be used to extract and classify named entities related to diseases, drugs, genes, and proteins in biomedical text.
- Financial analysis: NER can be used to extract and classify named entities related to financial instruments, companies, and events in financial text.
- Social media analysis: NER can be used to extract and classify named entities related to people, organizations, and events in social media text.
Sentiment analysis is a widely used technique in NLP that involves determining the sentiment or emotion behind a piece of text. This can be used to gauge the opinion of customers towards a product or service, understand the mood of the audience in social media posts, and more.
Approaches and Methods for Sentiment Analysis
There are several approaches and methods that can be used for sentiment analysis, including:
- Rule-based methods: These involve using pre-defined rules to classify text as positive, negative, or neutral.
- Lexicon-based methods: These involve using a pre-defined list of words and their associated sentiment scores to classify text.
- Machine learning-based methods: These involve training a model on a large dataset of labeled text to predict the sentiment of new text.
Real-world Applications of Sentiment Analysis
Sentiment analysis has a wide range of real-world applications, including:
- Customer feedback analysis: This can be used to understand the sentiment of customers towards a product or service, and to identify areas for improvement.
- Social media monitoring: This can be used to track the sentiment of social media users towards a brand, product, or event.
- News analysis: This can be used to track the sentiment of news articles towards a particular topic or event.
Overall, sentiment analysis is a powerful tool in NLP that can be used to gain insights into the sentiment behind text, and to make data-driven decisions based on those insights.
NLP Techniques and Algorithms
Text classification is a fundamental technique in NLP that involves categorizing text into predefined categories or labels. It is widely used in various applications, including sentiment analysis, spam detection, and topic classification.
The following are some of the popular algorithms used for text classification:
- Naive Bayes: It is a simple yet effective algorithm that is commonly used for text classification. It works by calculating the probability of each word in a document being classified into a particular category.
- Support Vector Machines (SVMs): SVMs are a popular machine learning algorithm that can be used for text classification. They work by finding the best line that separates the data into different categories.
- Decision Trees: Decision trees are a popular algorithm used for text classification. They work by creating a tree-like structure that classifies the data into different categories based on the features of the text.
- Neural Networks: Neural networks are a type of machine learning algorithm that can be used for text classification. They work by training a model to recognize patterns in the data and classify it into different categories.
The following are some of the use cases and applications of text classification:
- Sentiment Analysis: Text classification is widely used for sentiment analysis, which involves classifying text into positive, negative, or neutral categories. This is useful for businesses to understand customer feedback and improve their products and services.
- Spam Detection: Text classification is used for spam detection in email systems. It involves classifying emails into spam or non-spam categories to filter out unwanted emails.
- Topic Classification: Text classification is used for topic classification, which involves categorizing text into different topics or themes. This is useful for content analysis and recommendation systems.
Overall, text classification is a powerful technique in NLP that is used in a wide range of applications to analyze and classify text data.
Introduction to Machine Translation
Machine translation (MT) is the process of automatically translating text or speech from one language to another. The goal of MT is to enable efficient and accurate communication across language barriers. The first attempts at MT can be traced back to the 1940s, with the development of the first computer-based translation systems. Since then, MT has evolved significantly, with advancements in artificial intelligence, natural language processing, and machine learning enabling more sophisticated and accurate translations.
Techniques and Algorithms for Machine Translation
There are several techniques and algorithms used in MT, including rule-based, statistical, and neural machine translation.
- Rule-based MT: This approach uses linguistic rules and dictionaries to translate text. The system analyzes the structure of the source text and generates a corresponding structure in the target language. This method is less flexible than statistical or neural MT and can struggle with ambiguity and idiomatic expressions.
- Statistical MT: This approach uses statistical models to analyze large amounts of parallel text (i.e., texts in both the source and target languages) to learn how words and phrases are translated. The system then uses this knowledge to generate translations. Statistical MT has seen significant improvements in recent years, thanks to the availability of large datasets and advanced algorithms.
- Neural MT: This approach uses artificial neural networks to learn how to translate text. Neural MT models are trained on large datasets and can capture more nuanced aspects of language, such as grammar, syntax, and context. Neural MT has become the dominant approach in the field and has led to significant improvements in translation quality.
Challenges and Advancements in Machine Translation
Despite its successes, MT still faces several challenges, including dealing with ambiguity, handling idiomatic expressions, and maintaining context. However, ongoing research and development in the field are continually improving MT technology. Recent advancements include the use of deep learning, transfer learning, and attention mechanisms, which have significantly enhanced the accuracy and fluency of MT systems. Additionally, the integration of MT with other NLP techniques, such as information retrieval and sentiment analysis, is expected to further enhance the capabilities of MT systems in the future.
Information extraction is a fundamental technique in NLP that involves the process of automatically extracting structured information from unstructured text. This technique is widely used in various NLP applications, such as information retrieval, text summarization, and question answering.
Importance of information extraction in NLP
Information extraction plays a crucial role in NLP as it helps in extracting useful information from large volumes of text data. This information can then be used for various purposes, such as analyzing sentiment, identifying entities, and extracting relationships between entities.
Methods and algorithms for information extraction
There are several methods and algorithms used for information extraction in NLP, including:
- Rule-based systems: These systems use predefined rules to extract information from text.
- Machine learning-based systems: These systems use machine learning algorithms, such as decision trees and support vector machines, to extract information from text.
- Hybrid systems: These systems combine rule-based and machine learning-based approaches to extract information from text.
Applications of information extraction in various fields
Information extraction has numerous applications in various fields, such as:
- Healthcare: Information extraction can be used to extract medical information from patient records, such as symptoms, diagnoses, and treatments.
- Finance: Information extraction can be used to extract financial information from news articles, stock reports, and other financial documents.
- Social media: Information extraction can be used to extract sentiment and opinion from social media posts, which can be used for market research and customer feedback analysis.
- E-commerce: Information extraction can be used to extract product information from online retailers, such as prices, descriptions, and reviews.
Overall, information extraction is a powerful technique in NLP that can help extract valuable information from large volumes of text data, enabling various applications in different fields.
Question Answering Systems
Question answering systems are a key application of NLP. These systems are designed to process natural language queries and return relevant information in response.
Overview of question answering systems
Question answering systems have become increasingly sophisticated over the years, with many different approaches and algorithms being developed to handle a wide range of queries. These systems typically involve several components, including a natural language processing (NLP) module for understanding the query, a knowledge base or database for storing relevant information, and a response generation module for formulating a response.
Techniques and algorithms for question answering
One of the most important challenges in question answering systems is effectively retrieving relevant information from a knowledge base or database. This typically involves using techniques such as semantic analysis and vector search to identify documents or passages that are most closely related to the query.
Another key challenge is formulating a response that accurately and effectively answers the query. This typically involves using algorithms such as summarization and text generation to synthesize information from multiple sources and construct a coherent response.
Examples of question answering systems in practice
There are many different question answering systems in use today, ranging from simple rule-based systems to more complex machine learning-based systems. Some examples of question answering systems include:
- [Question Answering System A]: This system uses a combination of rule-based matching and statistical machine learning to identify relevant documents and generate responses to user queries.
- [Question Answering System B]: This system uses a neural network-based approach to understand the semantics of user queries and retrieve relevant information from a knowledge base.
- [Question Answering System C]: This system uses a hybrid approach that combines semantic analysis with vector search to identify relevant documents and generate responses to user queries.
NLP Applications in Real-World Scenarios
Chatbots and Virtual Assistants
Chatbots and virtual assistants are two of the most prominent applications of NLP in real-world scenarios. They are designed to simulate human conversation and provide automated assistance to users. The use of NLP in chatbots and virtual assistants has become increasingly popular due to its ability to understand and respond to natural language input from users.
Role of NLP in chatbot development
NLP plays a crucial role in the development of chatbots. Chatbots use NLP techniques to understand the intent behind a user's message and generate an appropriate response. The NLP system processes the user's input, identifies the relevant information, and extracts the key concepts to determine the user's intent. Based on the user's intent, the chatbot then selects an appropriate response from its pre-defined database of responses.
NLP techniques used in virtual assistants
Virtual assistants, such as Siri and Alexa, use a combination of NLP techniques to understand and respond to user queries. They use speech recognition to transcribe the user's spoken words into text, which is then analyzed using NLP techniques to extract the relevant information and determine the user's intent. Once the user's intent has been identified, the virtual assistant uses a natural language generation system to generate an appropriate response.
Benefits and challenges of using NLP in chatbots
The use of NLP in chatbots and virtual assistants has several benefits, including increased efficiency, 24/7 availability, and cost-effectiveness. Chatbots can handle a large volume of queries simultaneously, without the need for human intervention. They are also available 24/7, providing users with immediate assistance whenever they need it. Additionally, chatbots can save companies money by reducing the need for human customer service representatives.
However, there are also challenges associated with the use of NLP in chatbots. One of the main challenges is dealing with ambiguity and context. NLP systems may struggle to understand the nuances of natural language and may fail to understand the context in which a query is made. Additionally, chatbots may struggle to handle complex queries or queries that require a high degree of domain-specific knowledge. To overcome these challenges, chatbot developers must continually improve their NLP systems and develop new techniques to enhance their ability to understand and respond to natural language input.
Text summarization is a widely used NLP technique that involves generating a concise and accurate summary of a longer text. The process of text summarization typically involves extracting the most important information from a text and presenting it in a shorter form.
Overview of text summarization in NLP
Text summarization has been a popular research area in NLP for many years. It has a wide range of applications, including news summarization, academic research summarization, and social media summarization. Text summarization is useful in situations where the user needs to quickly understand the main points of a document without having to read the entire text.
Techniques and algorithms for text summarization
There are several techniques and algorithms used in text summarization, including:
- Reduction techniques: These techniques involve selecting a subset of the most important sentences from the text. This can be done using methods such as sentence weighting, where the most important sentences are given higher weights, or sentence selection, where a fixed number of the most important sentences are selected.
- Abstraction techniques: These techniques involve extracting the key information from the text and presenting it in a shorter form. This can be done using methods such as keyword extraction, where the most important keywords are selected, or extractive summarization, where the most important sentences are selected.
- Abstractive techniques: These techniques involve generating a summary that is not directly extracted from the text. This can be done using methods such as sentence combination, where the most important sentences are combined to form a summary, or sequence-to-sequence models, where the model generates a summary as a sequence of words.
Use cases and advantages of text summarization
Text summarization has a wide range of use cases, including:
- News summarization: Text summarization can be used to generate a summary of news articles, which can be useful for users who want to stay up-to-date with the latest news without having to read through long articles.
- Academic research summarization: Text summarization can be used to generate a summary of academic research papers, which can be useful for researchers who want to quickly understand the main findings of a paper without having to read through the entire text.
- Social media summarization: Text summarization can be used to generate a summary of social media posts, which can be useful for users who want to quickly understand the main points of a post without having to read through the entire text.
Text summarization has several advantages, including:
- It saves time by allowing users to quickly understand the main points of a text without having to read through the entire text.
- It can improve comprehension by presenting the most important information in a shorter form.
- It can be used to generate summaries of long texts, such as news articles or academic research papers, which can be useful for users who want to stay up-to-date with the latest information.
Voice Recognition and Speech Processing
Natural Language Processing (NLP) plays a significant role in voice recognition technology and speech processing. Voice recognition is the ability of a computer or machine to identify and interpret spoken words and convert them into machine-readable text. On the other hand, speech processing refers to the analysis and manipulation of spoken language to extract meaning and information.
NLP's role in voice recognition technology is multifaceted. It involves the use of various techniques and algorithms to analyze and process speech data. One such technique is the Hidden Markov Model (HMM), which is a statistical model that uses a sequence of hidden states to model speech patterns. Another technique is the Deep Neural Network (DNN), which is a machine learning model that uses a multi-layered network of artificial neurons to analyze speech data.
In addition to these techniques, NLP also utilizes various algorithms for speech processing. One such algorithm is the Spectral Subband Coding (SSC), which is a technique used to encode speech signals into a more compressible form. Another algorithm is the Linear Predictive Coding (LPC), which is a method used to predict the next sound in a sequence of speech based on previous sounds.
Applications and advancements in voice recognition and speech processing have revolutionized various industries. In the healthcare industry, NLP is used to transcribe medical dictation and extract patient information from electronic health records. In the automotive industry, NLP is used to develop voice-activated assistants and improve driver safety. In the financial industry, NLP is used to analyze customer speech and improve customer service.
Overall, NLP's role in voice recognition and speech processing is critical in enabling machines to understand and interpret human speech, opening up new possibilities for improving efficiency, safety, and communication in various industries.
Natural Language Generation
Introduction to natural language generation (NLG)
Natural Language Generation (NLG) is a subfield of NLP that focuses on the automated production of human-like language by computers. The primary goal of NLG is to transform structured data or input into coherent, fluent, and grammatically correct natural language text. NLG is an essential component of several AI applications, including chatbots, virtual assistants, and content generation.
Techniques and algorithms for NLG
There are several techniques and algorithms used in NLG, including:
- Template-based methods: These methods use predefined templates to generate text. They are efficient but limited in their ability to produce varied and creative text.
- Statistical methods: These methods rely on statistical models to predict the most likely output for a given input. They are more flexible than template-based methods but require large amounts of training data.
- Neural methods: These methods use deep learning architectures, such as recurrent neural networks (RNNs) and transformers, to learn the underlying patterns in text data. They have shown significant improvements in text generation quality and are widely used in NLG applications.
Real-world applications of NLG in content generation
NLG has numerous real-world applications in content generation, including:
- Automated financial reports: NLG can be used to generate automated financial reports for businesses, summarizing financial data and generating insights for decision-making.
- News article generation: NLG can be used to generate news articles based on factual data, allowing news organizations to produce content quickly and efficiently.
- Personalized emails: NLG can be used to generate personalized emails for marketing campaigns, addressing customers by their names and providing tailored content.
- Chatbot responses: NLG can be used to generate human-like responses for chatbots, enhancing user experience and providing more accurate and relevant information.
- Content creation for SEO: NLG can be used to generate optimized content for search engine optimization (SEO), helping businesses improve their online visibility and reach.
Future Perspectives and Challenges in NLP
Emerging trends and advancements in NLP
- AI-driven NLP: The integration of Artificial Intelligence (AI) into NLP is expected to significantly enhance the capabilities of NLP systems. This includes the development of deep learning algorithms that can automatically learn and improve from data, as well as the use of reinforcement learning techniques to optimize the performance of NLP models.
- Multimodal NLP: The incorporation of multiple modalities, such as text, images, and speech, into NLP systems is an emerging trend. This enables NLP systems to better understand and process complex, multidimensional data, which has applications in fields such as medical diagnosis and sentiment analysis.
Ethical considerations in NLP development and usage
- Bias and fairness: The development and deployment of NLP systems can perpetuate existing biases present in the data they are trained on. This can lead to unfair treatment of certain groups, such as perpetuating gender or racial disparities. Developers and users of NLP systems must be aware of these biases and work towards creating fair and unbiased systems.
- Privacy and security: As NLP systems are increasingly used to process sensitive personal data, such as medical records or financial information, concerns around privacy and security become paramount. It is crucial to ensure that NLP systems are developed and deployed in a secure manner, with appropriate measures taken to protect user data.
Existing challenges and areas for further research
- Low-resource languages: NLP systems have traditionally been developed for and applied to English and other widely spoken languages. However, there is a significant need for NLP systems to be developed for low-resource languages, which often lack adequate NLP resources and infrastructure. This presents an opportunity for further research and development in this area.
- Cross-lingual NLP: As the amount of multilingual data available for training NLP models increases, the need for NLP systems that can effectively process and analyze data across multiple languages becomes more pressing. Cross-lingual NLP research aims to develop NLP systems that can effectively translate and analyze text in multiple languages, which has applications in fields such as international business and diplomacy.
1. What is NLP?
Natural Language Processing (NLP) is a field of study that focuses on enabling computers to understand, interpret, and generate human language. It involves developing algorithms and models that can process, analyze, and generate text or speech data.
2. How is NLP used?
NLP has a wide range of applications across various industries. Some common use cases include:
* Sentiment Analysis: analyzing customer feedback, reviews, and social media posts to determine overall sentiment towards a product, service, or brand.
* Chatbots and Virtual Assistants: building conversational interfaces that can interact with users and provide assistance or answer questions.
* Language Translation: automatically translating text from one language to another.
* Speech Recognition: enabling computers to understand spoken language and perform actions based on the input.
* Text Classification: categorizing text data into predefined categories, such as spam vs. non-spam emails, or news articles by topic.
* Information Extraction: automatically extracting relevant information from unstructured text data, such as extracting names, addresses, and phone numbers from a document.
3. What are some examples of NLP applications?
Some examples of NLP applications include:
* Google Translate: a language translation service that can translate text from one language to another.
* Siri and Alexa: virtual assistants that use NLP to understand and respond to voice commands and questions from users.
* Netflix: uses NLP to recommend movies and TV shows to users based on their viewing history and preferences.
* Social media monitoring: NLP is used to analyze social media posts and identify trends, sentiment, and key topics.
* Spam filtering: NLP is used to automatically classify emails as spam or non-spam based on the content and context of the message.
4. How does NLP work?
NLP works by using algorithms and models to process and analyze natural language data. This involves techniques such as tokenization (breaking down text into individual words or phrases), part-of-speech tagging (identifying the grammatical role of each word in a sentence), and sentiment analysis (determining the overall sentiment of a piece of text). NLP models can then be trained on large amounts of text data to learn patterns and relationships between words and phrases, which can be used to perform tasks such as language translation or sentiment analysis.
5. What are some challenges in NLP?
NLP presents several challenges, including:
* Ambiguity: natural language is often ambiguous and can have multiple meanings, which can make it difficult for computers to understand the intended meaning.
* Context: natural language is highly dependent on context, which can be difficult for computers to infer and understand.
* Sarcasm and irony: natural language can be difficult for computers to interpret when it comes to sarcasm and irony, which can lead to errors in understanding.
* Accent and dialect: speech recognition can be challenging when dealing with accents and dialects that are different from the training data.
* Data bias: NLP models can be biased if the training data is not diverse or representative of the population.
6. How can I learn more about NLP?
There are many resources available for learning about NLP, including online courses, tutorials, and books. Some popular resources include:
* Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
* Deep Learning for Natural Language Processing by Palash Goyal
* NLP with Python: Analyzing Text with Machine Learning and AI by Siddhesh Jumde
* Natural Language Processing Course on Coursera by Dan Jurafsky and Christopher Manning
* NLP with spaCy and Python: Analyzing Text with Machine Learning and AI by Jake VanderPlas.