NLP Explained: Natural Language Processing Basics for Beginners

Structured data is organized and formatted according to pre-defined models and schemas, while unstructured data lacks specific structure and remains unorganized in raw form. Structured data uses table formats with defined columns (name, roll number, branch) and follows relational database principles where data elements relate to each other. It relies on three schema types: physical, logical, and external schemas that define the database's overall structure. In contrast, unstructured data operates on character and binary formats without any predefined structure, making it more flexible but harder to process systematically.

The key differences between structured and unstructured data are: (1) Structure - structured data has a fixed pattern and predefined format, while unstructured data has no fixed pattern or predefined structure, (2) Quantitative nature - structured data is quantitative and can be calculated, while unstructured data is qualitative, (3) Database management - structured data is managed using relational database software, while unstructured data is managed using non-relational database systems.

The fundamental difference between structured and unstructured data lies in organization. Unstructured data is disorganized information that requires independent classification by readers (whether human or machine) to identify specific points within content. In contrast, structured data organizes properties and types ahead of time before reading, such as identifying 'name,' 'role,' and 'company' from a sentence like 'John Smith is a software engineer for Salesforce.'

Data exists in various forms including audio, video, images, text, and messages. Structured data is organized in rows and columns like Excel, with fixed formats stored in databases and analyzed using SQL. Examples include sales databases with customer and product information. Unstructured data lacks definite structure, including text, audio, video, social media, and IoT streaming data. It captures feelings, opinions, and ideas beyond numbers. Text reviews cannot fit tabular formats, making storage challenging. IDC predicts global data will reach 163 zetabytes by 2025, with most being unstructured. Structured text is simple (addresses, names), while unstructured text requires complex sentiment analysis.

Structured data can be visualized as an Excel workbook with data sorted into rows and columns based on certain factors. Unstructured data includes images and unorganized data in text files. Key differences include: storage methodologies (structured uses database management systems while unstructured depends on particular applications), protocol standards (structured uses ODBC, SQL, ADO.NET while unstructured uses XML, CSV, SMTP), and scaling (structured data makes schema expansion difficult while unstructured data can be converted into schemas efficiently).

English vocabulary can be expanded by understanding prefixes and suffixes. Prefixes (placed at word beginning) change meaning: 'un-' makes 'happy' to 'unhappy', 'in-' makes 'complete' to 'incomplete'. Suffixes (placed at word end) change grammatical function: '-er' turns 'sing' to 'singer', '-able' means 'able to', '-less' means 'without', '-ty' means 'quality of', '-ist' means 'person associated with field', '-ize' means 'to make', '-ion' means 'act or process', '-ness' means 'quality of', '-ous' means 'full of', '-y' means 'full of'. English has 8 parts of speech: Noun, Verb, Adjective, Adverb, Pronoun, Preposition, Conjunction, Interjection. Adjectives modify nouns and appear before them, while adverbs modify verbs and typically end with '-ly'. Understanding these components allows learners to derive 5-8 related words from one root word, significantly expanding vocabulary knowledge.

English has 8 parts of speech, but only 4 are 'root' or 'main' words: Noun, Verb, Adjective, and Adverb. These carry independent meaning and form the foundation of sentences. The other 4 are 'subordinate' words: Pronoun, Preposition, Conjunction, and Interjection. Prefixes and suffixes are called 'affixes' and are used to create new words from existing words. A prefix is added to the beginning of a word, while a suffix is added to the end. The main purpose of affixes is to change the meaning of a word without changing its part of speech. Nouns can appear as subjects (before the verb) or objects (after the verb). Verbs are the 'soul' of a sentence and must always be present. The main verb typically comes after the subject, while helping verbs (is, was, can, could, will, would, should, may, might, have, has) come before the main verb. Adjectives describe or modify nouns and pronouns by indicating qualities, characteristics, conditions, or states. They answer questions like 'What kind?' 'Which one?' 'How many?' or 'How much?' and always come before the nouns they modify. Adverbs modify verbs, adjectives, or other adverbs by describing how, when, where, or to what extent something happens. They answer questions like 'How?' 'When?' 'Where?' or 'To what extent?' and can appear after the main verb, before the main verb, or at the beginning of a sentence for emphasis. Prefixes are commonly used to create negative or opposite meanings. The most common negative prefixes are: 'un-' (e.g., unhappy, unfair, unknown), 'in-' (e.g., inactive, incomplete, impossible), 'im-' (e.g., immoral, impatient, impossible), 'dis-' (e.g., dislike, disconnect, disapprove), and 'non-' (e.g., nonsense, nonstop, nonliving). Prefixes can also indicate size, direction, or position. For size: 'micro-' means very small (e.g., microscope, microorganism), 'macro-' means very large (e.g., macroeconomics, macrocosm). For direction/position: 'trans-' means across or through (e.g., transport, transform), 'pre-' means before (e.g., preview, preface), 'post-' means after (e.g., postwar, postgraduate), 're-' means again or back (e.g., rewrite, replay, rewind).

Prefixes are word parts attached at the beginning of a base word to modify its meaning (e.g., 'un' meaning not, 'pre' meaning before), while suffixes are attached at the end of a base word (e.g., 'full' meaning full of, 'at' meaning small/diminutive); together with the base or root word, these affixes transform simple words into more complex ones by changing their meaning, as demonstrated by examples like 'disappear' (dis- + appear), 'rewrite' (re- + write), and 'triangle' (tri- + angle).

This video explains how English words are formed by combining word roots with prefixes and suffixes, demonstrating that understanding these components helps learners decode unfamiliar words and expand their vocabulary. The content covers common prefixes (such as 'anti-', 'pre-', 're-'), suffixes (such as '-tion', '-ment', '-ness'), and word roots, providing examples to illustrate how these elements combine to create new words with specific meanings.

Words consist of roots plus affixes. Roots carry core meaning; affixes modify it. Prefixes attach before roots (un-, re-, anti-, dis-, non-), while suffixes attach after (less, ed, ly, able, er). Affixes cannot stand alone. Prefixes like 'un-' mean 'not' (unhappy), 're-' means 'again' (renew), 'anti-' means 'against' (antibiotic). Suffixes change word categories: 'ed' creates past tense (loved), 'ly' forms adjectives (lovely), 'able' indicates capability (comfortable), 'er' creates comparatives (bigger). Understanding this structure helps decode vocabulary.

The goals of AI include: (1) Making computers think like machines with minds in the full and literal sense; (2) The study of mental faculties through the use of computational models; (3) The art of creating machines that perform functions requiring intelligence when performed by people. AI is defined as a field of study that seeks to explain and emulate intelligent behavior in terms of computational processes.

This section introduces the foundational concepts of Artificial Intelligence and Machine Learning. AI refers to computers and machines that can reason, learn, and act in ways requiring human intelligence or processing vast amounts of data. Machine learning is a subfield of AI that enables machines to learn without specific programming, using algorithms and statistical models to learn from data, make predictions, and make decisions. The course aims to make these complex topics accessible through simple explanations.

The goals of AI include replicating human intelligence under certain conditions, solving knowledge-intensive tasks that currently depend only on humans, creating intelligent connections between action and perception, and developing machines that can replace humans in tasks requiring human intelligence.

Artificial Intelligence (AI) is a field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence, including learning, reasoning, problem-solving, perception, and language understanding. The primary goals of AI include automation (performing repetitive tasks automatically), understanding human cognition (mimicking human thought processes in various emotional states), problem-solving through logical reasoning, implementing learning algorithms that enable systems to learn from data and improve over time, and enabling natural interaction between humans and machines using natural language and intuitive interfaces. Machine Learning, a subfield of AI, encompasses three main approaches: Supervised Learning (learning from clean labeled data), Unsupervised Learning (discovering patterns in raw unlabeled data), and Reinforcement Learning (learning through rewards and punishments).

Artificial Intelligence (AI) is a field of computer science that enables computers to exhibit intelligent behavior, including learning, reasoning, and decision-making. AI aims to help computers perform tasks traditionally done by humans with greater accuracy and efficiency. Machine Learning (ML) is a subfield of AI that uses mathematical techniques to enable computers to learn from data without explicit programming. Unlike rule-based programming where all scenarios are hardcoded, ML allows systems to discover patterns and make predictions based on data. AI applications span diverse fields including business, law, sports, education, and transportation.