What you will learn in Natural Language Processing Specialization Course
- Gain a comprehensive understanding of Natural Language Processing (NLP) and its applications.
- Learn fundamental NLP techniques like text processing, tokenization, and sentiment analysis.
- Develop machine learning models for NLP tasks, including text classification and named entity recognition.
- Explore deep learning approaches for NLP, including recurrent neural networks (RNNs) and transformers.
- Work with industry-standard NLP libraries such as NLTK, SpaCy, and Hugging Face Transformers.
- Apply NLP to real-world applications, including chatbots, text summarization, and machine translation.
Program Overview
Introduction to Natural Language Processing
⏱️4-6 weeks
- Understand the basics of NLP and its role in AI and data science.
- Learn text preprocessing techniques, tokenization, and part-of-speech tagging.
Text Classification & Sentiment Analysis
⏱️6-8 weeks
- Apply machine learning algorithms for text classification.
- Build a sentiment analysis model using Python and Scikit-Learn.
Deep Learning for NLP
⏱️8-10 weeks
- Explore neural networks, word embeddings, and sequence models.
- Understand transformers, BERT, and GPT for state-of-the-art NLP applications.
Advanced NLP Applications
⏱️10-12 weeks
- Learn how to build chatbots, machine translation models, and text summarization tools.
- Use Hugging Face Transformers and TensorFlow/PyTorch for NLP projects.
Capstone Project: Real-World NLP Application
⏱️12-14 weeks
- Work on a hands-on NLP project using real-world datasets.
- Develop a full NLP pipeline from data preprocessing to model deployment.
Get certificate
Job Outlook
- NLP is one of the fastest-growing fields in AI, with increasing demand for NLP engineers and data scientists.
- NLP professionals work in tech companies, research labs, and industries like healthcare and finance.
- NLP-related jobs have high salaries, with an average of $100K+ per year for NLP engineers.
- Skills in machine learning, deep learning, and NLP frameworks open doors to careers in AI, data science, and software engineering.
- Growing adoption of chatbots, virtual assistants, and automated text analysis is fueling demand for NLP expertise.
Specification: Natural Language Processing Specialization
|
FAQs
- Yes, learners with basic Python and ML knowledge can start.
- Covers foundational NLP concepts and builds up to advanced techniques.
- Ideal for data scientists, AI enthusiasts, and developers entering NLP.
- Text preprocessing, tokenization, and vectorization.
- Word embeddings, Word2Vec, and GloVe usage.
- Sequence models, LSTM, GRU, and attention mechanisms.
- Transformers and neural machine translation.
- Build chatbots, sentiment analysis, and question-answering systems.
- Consists of 4 courses plus a Capstone Project.
- Estimated duration: 4 months at 6 hours/week.
- Covers classification, probabilistic models, sequence models, attention models, and a practical Capstone Project.
- Yes, a Certificate of Completion from DeepLearning.AI.
- Can be added to your resume or LinkedIn profile.
- Demonstrates proficiency in NLP and deep learning applications.
- Rated 4.7/5 stars by learners.
- Appreciated for structured lessons, hands-on coding exercises, and practical Capstone projects.
- Prepares learners for NLP roles in AI, data science, and machine learning.
