Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization

Upon completing the specialization, you will be able to design and implement end-to-end NLP workflows using both classical and neural approaches. You will gain the skills to build robust tokenization pipelines, perform sentiment analysis across languages and domains, fine-tune transformer-based models, and develop applications such as machine translation systems and conversational agents. These capabilities will prepare you for technical roles in NLP, applied machine learning, and AI product development.

The NLP specialization has been designed to equip learners with practical and theoretical knowledge in Natural Language Processing. It covers foundational concepts such as tokenization and sentiment analysis, and progresses to advanced topics like neural network architectures, machine translation, speech processing, and chatbot development. The course blends videos, readings, quizzes, demonstrations, and real-world projects to ensure applied learning outcomes.

This specialization is ideal for data scientists, machine learning engineers, NLP enthusiasts, and developers seeking to build or enhance language-aware applications. It’s also suitable for professionals in linguistics, AI product teams, or anyone looking to transition into NLP-related roles with hands-on experience.

Learners should have a foundational understanding of Python programming and basic machine learning principles. Familiarity with vectors, embeddings, and neural networks is helpful, though key concepts are introduced progressively throughout the course.

You’ll learn to:

- Apply tokenization and sentiment analysis techniques using modern tools

- Understand and implement neural architectures like RNNs, LSTMs, and Transformers

- Perform machine translation and multilingual NLP using state-of-the-art methods

- Process speech and multimodal data

- Design and evaluate intelligent chatbots

- Explore emerging NLP trends such as model compression and ethical AI

You will gain hands-on experience with widely used tools and frameworks, including:

- Hugging Face Transformers

- spaCy

- NLTK

- TensorFlow / PyTorch (for select demonstrations)

- Whisper and DeepSpeech (for speech processing)

- CLIP / BLIP (for multimodal NLP)

Jupyter notebooks and cloud-based environments will be used to execute real-world NLP pipelines and demonstrations.

Tokenization and sentiment analysis are taught through both theory and applied labs. You’ll explore subword and BPE techniques, use pretrained tokenizers, and manage out-of-vocabulary challenges. For sentiment analysis, you’ll implement rule-based methods (like VADER), lexicon-based scoring (SentiWordNet), and fine-grained approaches like ABSA, supported by annotated datasets and evaluation metrics.

The specialization covers:

- Neural architectures: RNNs, LSTMs, Seq2Seq, and Transformers

- Pretrained models: BERT, GPT, MarianMT, T5, BART

- Techniques: Pretraining and fine-tuning, attention mechanisms, multilingual transfer learning, low-resource translation, and reinforcement learning from human feedback (RLHF)

You’ll also explore real deployment scenarios, such as zero-shot translation and domain adaptation pipelines.

The course includes:

- A demonstration of multilingual tokenization for low-resource languages

- A hands-on lab for building a multiturn chatbot

- Demonstrations of intelligent query response systems for customer support

- Real-world use of Whisper/DeepSpeech for speech recognition

Each lab reinforces concepts from the modules using authentic datasets and practical challenges.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.