| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - NLP Foundations Revisited Engineer View | |||
| 1. 1 1 What Makes NLP Different from Other ML Domains.mp4 | 80.7 MB | ||
| 10 - NLP Pipelines System Design | |||
| 11 - Beyond LLMs Hybrid NLP Systems | |||
| 12 - Ethics, Bias Responsible NLP | |||
| 2 - Text Preprocessing Linguistic Pipelines | |||
| 3 - SECTION 3 Feature Engineering for Classical NLP | |||
| 10. 3 2 TF-IDF Statistical Weighting.mp4 | 82 MB | ||
| 11. 3 3 Feature Selection for Text.mp4 | 79.4 MB | ||
| 12. 3 4 Classical NLP Models.mp4 | 67.9 MB | ||
| 13. Hands on Lab Model Evaluation.html | 11.6 KB | ||
| 4 - Word Representations Distributional Semantics | |||
| 14. 4 1 Distributional Hypothesis.mp4 | 69.8 MB | ||
| 15. 4 2 Static Word Embeddings.mp4 | 52 MB | ||
| 16. 4 3 Embedding Geometry.mp4 | 70.3 MB | ||
| 17. 4 4 Limitations of Static Embeddings.mp4 | 69.4 MB | ||
| 18. Hands on Lab Visualization Exercise.html | 11.7 KB | ||
| 5 - Sequence Modeling for NLP | |||
| 19. 5 1 Sequence Learning Fundamentals.mp4 | 59.6 MB | ||
| 20. 5 2 Recurrent Neural Networks.mp4 | 62.9 MB | ||
| 21. 5 3 LSTM GRU for NLP.mp4 | 71.1 MB | ||
| 22. 5 4 Bidirectional Models.mp4 | 60.6 MB | ||
| 23. Hands on Lab.html | 11.2 KB | ||
| 6 - Attention Transformer Fundamentals Pre-LLM | |||
| 24. 6 1 Attention Mechanism.mp4 | 64.9 MB | ||
| 25. 6 2 Transformer Architecture.mp4 | 75.3 MB | ||
| 26. 6 3 Why Transformers Replaced RNNs.mp4 | 66.2 MB | ||
| 27. 6 4 Transformer Use Without LLMs.mp4 | 66.3 MB | ||
| 28. Hands-On Lab Architecture Reasoning Exercise.html | 14.2 KB | ||
| 7 - Contextual Embeddings Representation Learning | |||
| 29. 7 1 Contextual Embeddings.mp4 | 71.2 MB | ||
| 30. 7 2 Encoder-Only Models.mp4 | 52.5 MB | ||
| 31. 7 3 Sentence Document Embeddings.mp4 | 58.9 MB | ||
| 32. 7 4 Embedding Evaluation.mp4 | 74.6 MB | ||
| 33. Applied Embedding Lab Evaluation Task.html | 13.3 KB | ||
| 8 - NLP Tasks in Practice | |||
| 34. 8 1 Text Classification.mp4 | 59.6 MB | ||
| 35. 8 2 Named Entity Recognition NER.mp4 | 63.8 MB | ||
| 36. 8 3 Text Similarity Semantic Search.mp4 | 62.8 MB | ||
| 37. 8 4 Topic Modeling.mp4 | 68 MB | ||
| 38. End-to-End Hands-On Mini Projects.html | 12.6 KB | ||
| 9 - Information Retrieval Search Systems | |||
| 39. 9 1 Classical IR.mp4 | 64.7 MB | ||
| 40. 9 2 Vector Search Semantic Retrieval.mp4 | 49.4 MB | ||
| 41. 9 3 Hybrid Search Systems.mp4 | 54.8 MB | ||
| 42. System-Level Hands-On Lab.html | 13.8 KB | ||
| 9. 3 1 Bag-of-Words N-grams.mp4 | 78.6 MB | ||
| 4. 2 1 Text Cleaning in Production.mp4 | 54 MB | ||
| 5. 2 2 Tokenization Strategies.mp4 | 64.7 MB | ||
| 6. 2 3 Stemming vs Lemmatization.mp4 | 59.5 MB | ||
| 7. 2 4 Sentence Segmentation Parsing Basics.mp4 | 65.3 MB | ||
| 8. Hands on Lab.html | 14.5 KB | ||
| 51. 12 1 Bias in Text Data.mp4 | 56.6 MB | ||
| 52. 12 2 Fairness Explainability.mp4 | 59.2 MB | ||
| 53. 12 3 Privacy-Aware NLP.mp4 | 63.7 MB | ||
| 54. Scenario-Based Audit Policy Exercises.html | 14.4 KB | ||
| 47. 11 1 When NOT to Use LLMs.mp4 | 44.9 MB | ||
| 48. 11 2 LLM Classical NLP.mp4 | 53.2 MB | ||
| 49. 11 3 Failure Modes in LLM-Centric NLP.mp4 | 52 MB | ||
| 50. Case-Study Design Decision Exercises.html | 13.6 KB | ||
| 43. 10 1 End-to-End NLP Pipelines.mp4 | 52.2 MB | ||
| 44. 10 2 NLP in Microservices.mp4 | 42.9 MB | ||
| 45. 10 3 Evaluation Monitoring.mp4 | 55.5 MB | ||
| 46. System Design Architecture Exercises.html | 13.4 KB | ||
| 2. 1 2 Text as Data.mp4 | 73.8 MB | ||
| 3. 1 3 NLP Problem Taxonomy.mp4 | 68.9 MB |
Modern NLP for AI Engineers & Data Scientists
https://WebToolTip.com
Published 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
Learn classical NLP, embeddings, transformers, and evaluation techniques beyond large language models
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
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Udemy - Snowflake Masterclass 2026 - Modern Data Cloud and Cortex AI Posted by
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