Natural Language Processing (NLP) Mastery in Python

Master Python string data types by learning assignment, concatenation, repetition, and indexing and slicing, then applying common string methods like length for practical NLP tasks.

Explore if, else, and else if statements in Python, test conditions such as age and grades, and see how conditional blocks execute to assign outcomes.

Explore how to control loops with break and continue, stopping at a condition or skipping iterations in for and while loops, including printing subsets and handling spaces.

Learn to define and call methods using def, pass parameters, and return results; explore lambda functions for inline use and how to reuse a square and greeting method.

Explore list comprehension and lambda method to write concise, single-line Python code for mapping numbers to squares, filtering evens, building dictionaries, and flattening nested lists.

Explore creating numpy arrays of 1d, 2d, and 3d (and beyond) using np.array, arange, ones, and linspace; learn about shape, depth, indexing, slicing, and modifying elements.

Learn to create numpy arrays using arange, zeros, and ones, specify shape and data type, and build multi-dimensional arrays from simple lists, with examples of int and float types.

Explore numpy statistical operations on 1d, 2d, and 3d arrays, including mean, variance, standard deviation, min, max, and axis-based calculations, plus feature normalization by mean subtraction and std division.

Explore numpy reshaping with reshape, ravel, and flatten, compare view versus copy and memory impact, and flatten 100 images (28×28) to 100×784 for CNN input.

Discover how to generate sequences and random numbers in numpy with arange, linspace, repeat, and tile, and learn reshaping and seed-based initialization for deterministic machine learning experiments.

Learn how to generate random numbers with numpy, including uniform and normal distributions, using random.rand and rand int, and apply seeds for reproducible train-test splits and weight initialization.

Master reading csv, json, and txt files with pandas into a data frame, including from online sources like GitHub raw links.

Explore selecting and manipulating data frame columns with list comprehension and lambda, lowercase column names, and create a new 'family size' feature by adding sibling-spouse and parent-child.

Learn practical techniques for renaming and selecting dataframe columns, applying conditional logic, and filtering numerical data types in Python pandas for streamlined data engineering.

Master filtering a data frame with predefined conditions, including null handling and multi-condition filters on age and gender for targeted rows.

Identify and fill null values in a dataframe using fillna, forward fill, and backward fill. Learn to drop columns with excessive nulls using threshold and axis, preserving data integrity.

Apply a lambda function for feature engineering on age to label child, teen, adult, and senior, handle missing values with interpolation, and inspect value counts.

Apply lambda functions to engineer features across multiple columns in a data frame, such as pair per family member, name length, and title extraction for model improvement.

Learn to group by categorical columns and apply aggregation on numerical data with Pandas, using groupby to compute mean ages by passenger class and gender for smarter imputation.

convert a pandas column from object to category to reduce memory, reveal two unique values, and apply encoding techniques like get_dummies, mapping, and rename categories for machine learning.

Learn how to create a data directory and write or append text to files in Python using os and file pointers, both with and without the with open context manager.

Explore how to write multi-line content to a file in Python by using for loops, write mode vs append mode, and joining lines to preserve line breaks.

Learn to read a file in Python using read mode, file pointers, and read lines; split lines, evaluate expressions, and close the file for clean data.

Learn to read large csv files in chunks with pandas read_csv, concatenate chunks into a full dataframe, and save as csv or tsv with optional index control.

Learn to load nested json data into a pandas dataframe with json_normalize, using a passenger example to extract name, age, ticket number, and price.

Explore regex basics in Python, testing metacharacters like caret, dollar, dot, and pipe with regex101, including start-of-string concepts, and learn how quantifiers, groups, ranges, and escapes build pattern matching.

Explore regex quantifiers in Python with regex101 examples, covering plus, star, and question mark, and curly brace ranges to control repeated matches, optional elements, and min/max counts.

Explore how groups and ranges in regex match characters using parentheses, square brackets, and curly braces, define character sets, and handle patterns like A4 and 3D.

Explore Python regex dot search to find patterns within strings, retrieve spans and digits using re.search, and extract emails with a simple pattern in a multi-line text.

Explore spaCy, an open-source Python library for industrial-strength NLP on large data sets, offering tokenization, lemmatization, NER, pipelines, and vectorization with transformers.

Learn how to print spaCy tokens with or without a pretty table, access token text and attributes like is_alpha, is_punct, and is_numeric, and explore tokenization's impact on pre-processing for NLP.

Explore dependency parsing in spaCy, capturing syntactic relationships to boost NLP tasks such as translation, sentiment analysis, named entity recognition, and information extraction alongside POS tagging.

Explore how to measure sentence similarity by projecting text into a vector space with spacy and embedding vectors, then use cosine similarity on 300-dimensional vectors.

Explore stop words in Bert extracted text data by comparing NLTK and Spacy lists, loading a bird corpus, tokenizing, lowercasing, and removing stop words to study filtered text.

Explore stemming with the porter stemmer in NLTK, compare it to lemmatization from spacy, noting that stemming yields non-dictionary bases but runs faster for latency-critical tasks.

Learn to perform part-of-speech tagging with NLTK by importing pos_tag, loading the averaged perceptron tagger, tagging words, and examining tag sets, with a Spacy comparison.

Explore lemmatization with NLTK using the WordNet Lemmatizer, download WordNet, and compare lemmatized words with stemming through tokenized text to show valid root forms.

Learn to extract meaningful noun phrases with the NLTK chunking approach by defining a determiner-adjective-noun grammar and applying a regex parser to tagged text.

master named entity recognition with NLTK by tokenizing text, POS tagging, and building a named entity tree with chunkers and neural models; compare NLTK and Spacy for NER tagging.

Load and preprocess Twitter data for NLP mastery by downloading the Twitter 4000 dataset, reading with pandas, and performing feature extraction and cleaning on the tweets column.

Extract the character count feature from a tweet column by removing spaces and computing length, using either direct length or regex methods, applied via a lambda on the data frame.

Count words in tweet data by splitting text on whitespace, compute word counts and average word length from character counts, and round results for analysis.

Identify numeric digits in tweets using regex with word boundaries, count each digit word with a lambda on the tweet column, and validate results in a data frame.

Learn to remove repeated characters in text using regex patterns, capture groups, and replace with a single character (or two), then apply to a dataframe column.

Demonstrates common words removal by tokenizing tweet data, counting word frequencies with Counter, and filtering top ten words to produce no common tweets.

Understand word cloud visualization, where frequent words appear larger and brighter. Follow steps to plot it in Python: install wordcloud, import libraries, and feed string data.

Write a clear readme.md and license for a Python NLP package, outlining purpose, requirements, installation via pip, and licensing options. Explain license constraints and repository location.

Learn to complete a setup.py workflow, including package name and data, include package data, manifest.in, versioning, author details, long description from readme, classifiers, and Python version requirements.

Extend the Python nlp package by adding character count, word count, average word length, and counts for stop words, hashtags, mentions, numerics, and uppercase text.

Write and assemble Python functions for text preprocessing, removing accented characters, mentions, special characters, URLs, repeated characters, and stop words, then lemmatize to base forms with spaCy.

Build a Python text preprocessing pipeline that removes common and rare words, corrects spelling, detects language, translates, and extracts noun phrases and n-grams with TextBlob.

Test and use your Python package locally by installing in the correct environment, importing preprocess_cp, and validating cleaning steps like removing retweet, HTML tags, lemmatization, and translation.