Free Download Modern NLP for AI Engineers & Data ScientistsPublished 1/2026
Created by Data Science Academy, School of AI
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner |
Genre: eLearning |
Language: English |
Duration: 54 Lectures ( 4h 49m ) |
Size: 2.8 GB
✓ 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
Description"This course contains the use of artificial intelligence"
Modern NLP for AI Engineers: Beyond LLMs is a comprehensive, industry-focused course designed to help you master Natural Language Processing as an engineering discipline, not just as a collection of prebuilt models. NLP sits at the core of modern AI systems, powering search engines, recommendation systems, customer intelligence platforms, fraud detection, document understanding, and enterprise AI applications. While many modern courses focus only on large language models and prompt engineering, this course fills a critical gap by teaching how real-world NLP systems are actually built, evaluated, and deployed.
This course takes you far beyond surface-level usage of APIs and pretrained models. You will learn how raw text is transformed into structured signals, how classical NLP techniques still form the backbone of many production systems, and how modern transformers and embeddings are used for understanding tasks without relying on text generation. The goal is to help you think like an AI Engineer who can design, debug, and optimize NLP systems from first principles.
Throughout the course, you will develop a deep understanding of text preprocessing, tokenization strategies, stemming and lemmatization, sentence segmentation, and linguistic pipelines that are essential for building robust NLP workflows. You will explore feature engineering for classical NLP, including Bag-of-Words, n-grams, TF-IDF, and statistical weighting, gaining insight into why these methods are still widely used in production environments today. Rather than treating these techniques as outdated, the course shows how they complement modern deep learning systems.
You will then move into word representations and distributional semantics, learning how meaning emerges through vector space geometry. Concepts such as the distributional hypothesis, static word embeddings, embedding similarity, vector arithmetic, and semantic drift are explained clearly and intuitively. The course emphasizes not just how embeddings work, but how they fail, covering critical limitations such as polysemy, context blindness, and vocabulary freeze, which directly motivate the transition to contextual models.
As the course progresses, you will learn how NLP handled context before transformers through sequence modeling, including Markov assumptions, recurrent neural networks, LSTMs, GRUs, and bidirectional models. These topics are presented not as historical artifacts, but as foundational ideas that still shape modern architectures and interview discussions. You will understand why transformers replaced RNNs, focusing on parallelization, long-context modeling, and training stability, without unnecessary hype.
A major focus of the course is contextual embeddings and representation learning, where you will learn how encoder-only models are used for text understanding, classification, and semantic similarity. You will explore sentence and document embeddings, compare CLS token representations versus mean pooling, and understand how these embeddings power semantic search, clustering, and retrieval systems used in real companies. The course also teaches how to properly evaluate embeddings using intrinsic and extrinsic metrics, while addressing bias, fairness, and representation risks, ensuring you build systems that are both effective and responsible.
This course is specifically designed to help you become employable in the AI and NLP job market. The skills you gain align directly with expectations for NLP Engineers, Machine Learning Engineers, AI Engineers, and Applied Scientists. Employers look for candidates who understand how NLP systems work end-to-end, how embeddings power search and recommendation, how transformers are used for understanding tasks, and how to evaluate models beyond accuracy numbers. This course prepares you to confidently answer interview questions, reason about system design, and contribute meaningfully to real NLP projects.
If you are an aspiring AI Engineer, Machine Learning Engineer, Data Scientist, or Software Engineer transitioning into AI, this course gives you the depth and structure needed to move beyond model usage and into system-level thinking. With a foundation in Python and basic machine learning concepts, you will be guided step by step through the full NLP stack, from text to vectors to models to evaluation.
If your goal is to land an NLP or AI engineering role, this course provides the practical understanding, conceptual clarity, and engineering mindset that employers value. You will not just learn NLP tools-you will learn how NLP works, why design choices matter, and how to build systems that scale in production. This is not a shortcuts or prompt-only course. This is a career-building NLP course for serious AI engineers.
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
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