What You'll Learn
- Design robust NLP pipelines from raw text to model input
- Apply text preprocessing, tokenization, parsing, and normalization correctly in production settings
- Build and evaluate classical NLP systems using Bag-of-Words, TF-IDF, and statistical features
- Understand and implement word embeddings, sentence embeddings, and document embeddings
- Use transformers for understanding tasks, not just text generation
- Choose the right encoder-only, sequence, or attention-based model for a given problem
- Evaluate embeddings using intrinsic and extrinsic metrics, while accounting for bias and representation risks
- Think like an AI Engineer, not just a model user
Requirements
- Basic Python programming
- Fundamental understanding of machine learning concepts
- Curiosity to understand how AI systems actually work
- No prior NLP experience is required—everything is built step by step
Who This Course is For
- Aspiring AI Engineers who want strong NLP fundamentals
- Machine Learning Engineers looking to specialize in NLP
- Data Scientists transitioning into AI-focused roles
- Software Engineers moving into applied AI
- Students preparing for NLP, ML, or AI job interviews
Your Instructor
Data Science Academy
Bridging knowledge to industry with Data & AI education
4.3 Instructor Rating
1,070 Reviews
50,911 Students
24 Courses
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