Machine Learning Guide MLG 018 Natural Language Processing 1
58:08
NLP as a Key ML Specialty
- Natural Language Processing (NLP) is a specialization within machine learning focusing on language.
- At a career crossroads, you must choose a machine learning specialty like NLP, vision, or reinforcement learning.
NLP Powers Major Tech Giants
- NLP drives major tech products like Siri, Google Assistant, and Ads.
- Language understanding has vast economic value and job opportunities today.
NLP Evolution to Machine Learning
- NLP evolved from hard-coded linguistic rules to machine learning algorithms.
- Today, NLP mostly consists of machine learning models that bring flexibility and improved performance.
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Introduction
00:00 • 2min
The Basics of Machine Learning
02:06 • 2min
Machine Learning - The Granddaddy Company of All Natural Language Processing Ever
04:14 • 2min
Machine Learning and NLP
06:38 • 3min
What Makes N L P Unique?
09:55 • 4min
Syntax and Semantics in Machine Translation
13:41 • 2min
Learning Natural Language Processing (NLP)
16:05 • 3min
Using Tokens in Machine Learning
18:37 • 2min
Lemma, Stem, and Edit Distance
20:46 • 4min
Parts of Speech - Machine Learning
24:31 • 2min
Parsing a Sentence Into a Tree
26:35 • 3min
The High Level Goals of N L P
29:16 • 4min
Sentiment Analysis and Machine Learning
32:58 • 5min
The Tasks of Natural Language Understanding
37:33 • 3min
Machine Translation
40:13 • 4min
Chatbots and Image Captioning
43:48 • 2min
Automatic Summarization Is in Use to Day in Summarizing Legal Documents
45:36 • 3min
A Giant Lay of the Land of NLP
49:03 • 2min
The Resources for Learning Natural Language Processing
50:59 • 3min
N L T K Is the Lowdash of Natural Language Processing
53:52 • 2min
The Lay of the Land of N L P
56:06 • 2min
Try a walking desk to stay healthy while you study or work!
Full notes at ocdevel.com/mlg/18
Overview: Natural Language Processing (NLP) is a subfield of machine learning that focuses on enabling computers to understand, interpret, and generate human language. It is a complex field that combines linguistics, computer science, and AI to process and analyze large amounts of natural language data.
NLP StructureNLP is divided into three main tiers: parts, tasks, and goals.
1. PartsText Pre-processing:
- Tokenization: Splitting text into words or tokens.
- Stop Words Removal: Eliminating common words that may not contribute to the meaning.
- Stemming and Lemmatization: Reducing words to their root form.
- Edit Distance: Measuring how different two words are, used in spelling correction.
Syntactic Analysis:
- Part-of-Speech (POS) Tagging: Identifying the grammatical roles of words in a sentence.
- Named Entity Recognition (NER): Identifying entities like names, dates, and locations.
- Syntax Tree Parsing: Analyzing the sentence structure.
- Relationship Extraction: Understanding relationships between entities in text.
High-Level Applications:
- Spell Checking: Correcting spelling mistakes using edit distances and context.
- Document Classification: Categorizing texts into predefined groups (e.g., spam detection).
- Sentiment Analysis: Identifying emotions or sentiments from text.
- Search Engine Functionality: Document relevance and similarity using algorithms like TF-IDF.
- Natural Language Understanding (NLU): Deciphering the meaning and intent behind sentences.
- Natural Language Generation (NLG): Creating text, including chatbots and automatic summarization.
Evolution:
- Early Rule-Based Systems: Initially relied on hard-coded linguistic rules.
- Machine Learning Integration: Transitioned to using algorithms that improved flexibility and accuracy.
- Deep Learning: Utilizes neural networks like Recurrent Neural Networks (RNNs) for complex tasks such as machine translation and sentiment analysis.
Key Algorithms:
- Naive Bayes: Used for classification tasks.
- Hidden Markov Models (HMMs): Applied in POS tagging and speech recognition.
- Recurrent Neural Networks (RNNs): Effective for sequential data in tasks like language modeling and machine translation.
NLP offers robust career prospects as companies strive to implement technologies like chatbots, virtual assistants (e.g., Siri, Google Assistant), and personalized search experiences. It's integral to market leaders like Google, which relies on NLP for applications from search result ranking to understanding spoken queries.
Resources for Learning NLP-
Books:
- "Speech and Language Processing" by Daniel Jurafsky and James Martin: A comprehensive textbook covering theoretical and practical aspects of NLP.
-
Online Courses:
- Stanford's NLP YouTube Series by Daniel Jurafsky: Offers practical insights complementing the book.
-
Tools and Libraries:
- NLTK (Natural Language Toolkit): A Python library for text processing, providing functionalities for tokenizing, parsing, and applying algorithms like Naive Bayes.
- Alternatives: OpenNLP, Stanford NLP, useful for specific shallow learning tasks, leading into deep learning frameworks like TensorFlow and PyTorch.
NLP continues to evolve with applications expanding across AI, requiring collaboration with fields like speech processing and image recognition for tasks like OCR and contextual text understanding.