Overview
Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Harness the power of language-driven AI with this applied Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization. Whether you're new to AI or expanding your machine learning expertise, this program guides you through essential and advanced NLP techniques—from sentiment analysis and tokenization to neural translation and transformer models.
You’ll complete three practical courses:
Course 1: Natural Language Processing Essentials
Learn linguistic structures and text preprocessing techniques Apply tokenization, stemming, lemmatization, and POS tagging Explore n-gram models and build basic NLP pipelines
Course 2: Advanced Tokenization and Sentiment Analysis
Master advanced tokenization methods like byte-pair encoding Perform NER, emotion classification, and sentiment analysis Build and fine-tune ML models using real-world text data
Course 3: Neural Models and Machine Translation
Implement RNNs, LSTMs, GRUs, and Transformer-based models Use pretrained models like BERT, RoBERTa, and MarianMT Train neural machine translation systems with encoder-decoder architecture
By the end, you'll be able to:
Design and deploy full NLP applications using classical and neural techniques Tackle real-world language tasks like sentiment prediction and translation Pursue roles in NLP, AI development, and applied machine learning
Enroll now to gain hands-on experience in building intelligent, language-aware systems.
Syllabus
- Course 1: Natural Language Processing Essentials
- Course 2: Advanced Tokenization and Sentiment Analysis
- Course 3: Neural Models and Machine Translation
Courses
-
This course introduces the fundamentals of Natural Language Processing (NLP), combining core linguistic concepts with hands-on programming techniques to help you understand how machines process human language. Whether you're new to NLP or looking to build foundational skills, this course provides a clear and practical path into one of the most exciting areas of AI and data science. Through guided lessons and real-world examples, you'll learn how to clean, structure, and analyze text data, apply feature extraction techniques, and build basic NLP models for tasks like text classification and named entity recognition. By the end of this course, you will be able to: • Understand NLP basics and key language concepts like morphology, syntax, semantics, and pragmatics. • Apply text cleaning and preprocessing techniques using NLTK and SpaCy, including tokenization, stemming, lemmatization, and embeddings. • Analyze text features by extracting Bag of Words, TF-IDF, and Word2Vec representations. • Evaluate machine learning models built for text classification. • Create NLP solutions by implementing Named Entity Recognition and syntactic parsing. This course is ideal for beginners, data enthusiasts, and aspiring NLP practitioners who want to gain a strong foundation in natural language processing and its applications in AI. No prior experience with NLP is required. A basic understanding of Python or machine learning concepts will be helpful, but not mandatory. Join us to begin your journey into the world of Natural Language Processing and text analysis with Python!
-
This course offers a clear pathway to undertsand advanced tokenization and sentiment analysis—two core pillars of modern NLP. You'll learn how to convert raw text into structured input using subword, character-level, and adaptive tokenization techniques, and how to extract sentiment using rule-based, statistical, and deep learning models. Through hands-on exercises, you’ll gain the skills to handle complex language input, model sentiment at fine granularity, and deploy systems that generalize across domains and languages. By the end of this course, you will be able to: - Explain and apply advanced tokenization techniques, including BPE, character-level, and streaming methods - Handle out-of-vocabulary terms and domain-specific language using adaptive and hybrid encoding strategies - Build sentiment analysis models using VADER, Naïve Bayes, BERT, and RoBERTa - Address challenges such as class imbalance, multilingual variation, and aspect-level sentiment - Evaluate sentiment systems using semantic similarity, temporal trends, and domain-specific metrics This course is ideal for NLP practitioners, data scientists, developers, and applied researchers aiming to build robust, ethical, and production-ready sentiment analysis systems. A basic understanding of Python, NLP fundamentals, and machine learning is recommended. Join us to learn how tokenization and sentiment analysis power the next generation of intelligent language technologies.
-
This course guides you through the core concepts behind neural language models and machine translation, focusing on how RNNs, attention, and transformers enable powerful NLP applications used in today’s AI systems. Through hands-on exercises, you’ll learn to build, fine-tune, and evaluate neural models for contextual language understanding, sentiment classification, and multilingual translation across various domains. By the end of this course, you will be able to: - Explain and implement core neural architectures, including RNNs, LSTMs, GRUs, and Transformers - Apply encoder-decoder frameworks and attention mechanisms to build translation systems - Fine-tune pretrained models like BERT, RoBERTa, and MarianMT for contextual NLP tasks - Address challenges such as domain adaptation, low-resource translation, and error correction - Evaluate model performance using BLEU, ROUGE, and semantic similarity metrics This course is ideal for NLP practitioners, machine learning engineers, and researchers aiming to build high-performing neural NLP systems for translation, classification, and conversational AI. A working knowledge of Python, NLP concepts, and machine learning is recommended. Join us to master the neural foundations driving next-generation language understanding and generation.
Taught by
Edureka