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NLP Courses

NLP courses can help you learn text processing, sentiment analysis, language modeling, and chatbot development. You can build skills in data preprocessing, feature extraction, and evaluating model performance. Many courses introduce tools like Python libraries such as NLTK and spaCy, as well as frameworks like TensorFlow and PyTorch, that support implementing NLP techniques and developing applications that utilize artificial intelligence.


More to explore:

Popular NLP Courses and Certifications


  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Natural Language Processing on Google Cloud

    Skills you'll gain: Natural Language Processing, Transfer Learning, Tensorflow, Google Cloud Platform, Recurrent Neural Networks (RNNs), Embeddings, Deep Learning, AI Workflows, Artificial Neural Networks, Feature Engineering, Model Evaluation

    4.4
    Rating, 4.4 out of 5 stars
    ·
    537 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Advanced Machine Learning on Google Cloud

    Skills you'll gain: Model Deployment, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Reinforcement Learning, Transfer Learning, Computer Vision, Systems Design, Applied Machine Learning, Image Analysis, Cloud Deployment, Hybrid Cloud Computing, Recurrent Neural Networks (RNNs), Systems Architecture, Performance Tuning, Embeddings, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    1.5K reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    U

    University of Michigan

    More Applied Data Science with Python

    Skills you'll gain: Unsupervised Learning, Data Mining, Social Network Analysis, ChatGPT, Embeddings, Machine Learning Methods, Data Science, Supervised Learning, Generative AI, Machine Learning, Anomaly Detection, Data Preprocessing, Data Analysis, Recurrent Neural Networks (RNNs), Data Manipulation, Python Programming, Exploratory Data Analysis, Machine Learning Algorithms, Jupyter, Classification Algorithms

    4.3
    Rating, 4.3 out of 5 stars
    ·
    8 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    AI Workflow: Machine Learning, Visual Recognition and NLP

    Skills you'll gain: Model Evaluation, Tensorflow, Supervised Learning, Artificial Neural Networks, Regression Analysis, Machine Learning Methods, Applied Machine Learning, Machine Learning Algorithms, Deep Learning, Image Analysis, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Decision Tree Learning, Natural Language Processing, Scikit Learn (Machine Learning Library), Convolutional Neural Networks, Computer Vision, Data Science, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    90 reviews

    Advanced · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Deep Learning for Healthcare

    Skills you'll gain: Deep Learning, Convolutional Neural Networks, Health Informatics, Autoencoders, Recurrent Neural Networks (RNNs), Image Analysis, Embeddings, Health Information Management, Machine Learning, Applied Machine Learning, Health Care, Model Deployment, Generative Adversarial Networks (GANs), Artificial Neural Networks, Healthcare Project Management, Supervised Learning, Model Evaluation, Machine Learning Methods, Graph Theory, Big Data

    3.3
    Rating, 3.3 out of 5 stars
    ·
    49 reviews

    Advanced · Specialization · 1 - 3 Months

  • G

    Google Cloud

    Transformer Models and BERT Model

    Skills you'll gain: Large Language Modeling, Natural Language Processing, Generative Model Architectures, Transfer Learning, Embeddings

    4.1
    Rating, 4.1 out of 5 stars
    ·
    124 reviews

    Advanced · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • C

    Coursera

    Deep Learning with PyTorch : Siamese Network

    Skills you'll gain: Embeddings, PyTorch (Machine Learning Library), Artificial Neural Networks, Image Analysis, Deep Learning, Applied Machine Learning, Convolutional Neural Networks, Computer Vision

    4.7
    Rating, 4.7 out of 5 stars
    ·
    49 reviews

    Advanced · Guided Project · Less Than 2 Hours

  • Status: Free Trial
    Free Trial
    I

    IBM

    Machine Learning Capstone

    Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Data Analysis, Applied Machine Learning, Data Presentation, Technical Communication, Machine Learning, Scikit Learn (Machine Learning Library), Python Programming, Regression Analysis, Keras (Neural Network Library), Artificial Neural Networks

    4.7
    Rating, 4.7 out of 5 stars
    ·
    189 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Preview
    Preview
    G

    Google Cloud

    Encoder-Decoder Architecture - Español

    Skills you'll gain: Model Deployment, Tensorflow, Recurrent Neural Networks (RNNs), Keras (Neural Network Library), Natural Language Processing, Applied Machine Learning, Text Mining

    Advanced · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    A

    AI CERTs

    AI for Telecommunications

    Skills you'll gain: Responsible AI, Tensorflow, PyTorch (Machine Learning Library), Internet Of Things, Real Time Data, Natural Language Processing, Artificial Intelligence and Machine Learning (AI/ML), Telecommunications, Artificial Intelligence, Emerging Technologies, Generative AI, Wireless Networks, Generative AI Agents, Network Performance Management, Network Architecture, Digital Communications, Network Protocols, Computer Networking, System Design and Implementation, Communication

    Advanced · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM AI Enterprise Workflow

    Skills you'll gain: Feature Engineering, Model Deployment, Data Visualization, Data Ethics, Exploratory Data Analysis, Model Evaluation, Unsupervised Learning, Data Presentation, Tensorflow, Dimensionality Reduction, MLOps (Machine Learning Operations), Probability Distribution, Apache Spark, Statistical Hypothesis Testing, Supervised Learning, Data Pipelines, Design Thinking, Data Science, Machine Learning, Python Programming

    4.3
    Rating, 4.3 out of 5 stars
    ·
    350 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    S

    Stanford University

    Probabilistic Graphical Models

    Skills you'll gain: Bayesian Network, Applied Machine Learning, Graph Theory, Machine Learning Algorithms, Probability Distribution, Network Model, Statistical Modeling, Markov Model, Decision Support Systems, Machine Learning, Probability & Statistics, Network Analysis, Machine Learning Methods, Statistical Inference, Sampling (Statistics), Statistical Methods, Natural Language Processing, Algorithms, Computational Thinking

    4.6
    Rating, 4.6 out of 5 stars
    ·
    1.5K reviews

    Advanced · Specialization · 3 - 6 Months

12345

In summary, here are 10 of our most popular nlp courses

  • Natural Language Processing on Google Cloud: Google Cloud
  • Advanced Machine Learning on Google Cloud: Google Cloud
  • More Applied Data Science with Python: University of Michigan
  • AI Workflow: Machine Learning, Visual Recognition and NLP: IBM
  • Deep Learning for Healthcare: University of Illinois Urbana-Champaign
  • Transformer Models and BERT Model: Google Cloud
  • Deep Learning with PyTorch : Siamese Network: Coursera
  • Machine Learning Capstone: IBM
  • Encoder-Decoder Architecture - Español: Google Cloud
  • AI for Telecommunications: AI CERTs

Skills you can learn in Software Development

Programming Language (34)
Google (25)
Computer Program (21)
Software Testing (21)
Web (19)
Google Cloud Platform (18)
Application Programming Interfaces (17)
Data Structure (16)
Problem Solving (14)
Object-oriented Programming (13)
Kubernetes (10)
List & Label (10)

Frequently Asked Questions about Nlp

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and generate human language in a valuable way. NLP is important because it powers various applications, from chatbots and virtual assistants to sentiment analysis and language translation. As businesses increasingly rely on data-driven insights, the ability to analyze and understand human language becomes crucial for enhancing customer experiences and making informed decisions.‎

Pursuing a career in NLP opens up a variety of job opportunities across multiple industries. Some common roles include NLP Engineer, Data Scientist, Machine Learning Engineer, and AI Research Scientist. These positions often involve developing algorithms and models that can process and analyze text data, creating applications that utilize NLP technologies, and conducting research to advance the field. As organizations continue to integrate AI and machine learning into their operations, the demand for skilled professionals in NLP is expected to grow.‎

To succeed in NLP, you will need a combination of technical and analytical skills. Key skills include programming languages such as Python or R, familiarity with machine learning frameworks, and a solid understanding of linguistics and language structure. Additionally, knowledge of data preprocessing techniques, statistical analysis, and experience with NLP libraries like NLTK or spaCy can be beneficial. Building a strong foundation in these areas will empower you to tackle complex NLP challenges effectively.‎

There are several excellent online courses available for those interested in learning NLP. For a comprehensive understanding, consider the Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization. Alternatively, the Applied NLP and Generative AI Specialization offers practical insights into applying NLP techniques. For a focus on modern architectures, the Introduction to Transformer Models for NLP Specialization is highly recommended.‎

Yes. You can start learning NLP on Coursera for free in two ways:

  1. Preview the first module of many NLP courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in NLP, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn NLP effectively, start by familiarizing yourself with the basics of programming and data science. Online courses can provide structured learning paths, allowing you to progress from foundational concepts to more advanced topics. Engage in hands-on projects to apply what you learn, and consider joining online communities or forums to connect with other learners and professionals. This collaborative approach can enhance your understanding and keep you motivated.‎

NLP courses typically cover a range of topics, including text preprocessing, sentiment analysis, language modeling, and machine translation. You may also explore advanced subjects like deep learning for NLP, sequence models, and transformer architectures. Practical applications, such as building chatbots or analyzing social media data, are often included to provide real-world context and enhance your learning experience.‎

For training and upskilling employees in NLP, consider courses that offer practical applications and industry-relevant skills. The Building AI Agents: Automation and NLP Foundations course is designed to provide foundational knowledge while focusing on automation. Additionally, the Natural Language Processing with Attention Models course can help employees understand advanced techniques that are increasingly important in the field.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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