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.
Applied Learning Project
Learners in this Natural Language Processing (NLP) Specialization will work on hands-on, applied projects that cover the complete NLP pipeline—from text preprocessing and tokenization to feature extraction and classification. These projects reinforce essential skills like building bag-of-words and TF-IDF representations, training sentiment analysis models, and applying named entity recognition (NER).
As the specialization progresses, learners will tackle advanced NLP tasks using neural networks, including sequence-to-sequence (seq2seq) models, recurrent neural networks (RNNs), and transformer-based architectures such as BERT and RoBERTa. Projects emphasize real-world NLP applications like sentiment classification, language modeling, and neural machine translation. Designed for practical relevance, these guided projects help learners build, fine-tune, and evaluate production-ready NLP systems that address domain-specific language processing challenges.
