Natural Language Processing Bootcamp in Python

Import the NLP course environment in Anaconda using the provided YAML from course materials, then launch a Jupyter notebook to access libraries like the Natural Language Toolkit.

Create and manage a Python environment from scratch for the NLP bootcamp, install libraries (nltk and pandas) via UI or command line, and verify setups in Anaconda Navigator or Colab.

Gain a practical introduction to Python basics and strings manipulation, covering data types, variables, lists, indexing and slices, and using libraries like Pandas, NumPy, and Keras in a Jupyter notebook.

Learn to navigate and run code in the Jupyter notebook environment, activate environments, and use code and markdown cells to work with Python, pandas, and numpy.

Explore Python libraries by importing the math module to access functions like sqrt, ceil, floor, and log, and retrieve attributes such as pi.

Master Python strings by declaring with single or double quotes, indexing and slicing with zero-based positions, and understanding concatenation, immutability, and basic type operations.

Learn how Python lists store mixed objects and are mutable, unlike strings. Create, index (zero-based), print, and modify lists using square brackets and commas, and understand list versus string behavior.

Explore how Python treats strings as immutable and uses dot notation to apply methods like replace, returning new strings. Contrast with mutable lists that modify in place.

Explore how Python sets store distinct elements using curly braces, extract distinct elements from a list, and convert a set to a list without indexing to see its unordered nature.

Explore Python dictionaries, a key–value pair data structure stored in curly brackets, and learn to access values with keys using square brackets, plus items, keys, and values properties.

Explore Python tuples as immutable data structures similar to lists, learn indexing and item access, understand their immutability, and compare performance benefits of tuples over lists.

Master defining and calling Python functions with def, return, and arguments to make code reusable and flexible, with notes on numpy and pandas for data analysis.

Explore numpy for vectors, matrices, and tensors, including creating arrays, checking shapes, and indexing across dimensions. Learn how pandas complements numpy by enabling labeled data frames for machine learning workflows.

Advance core Python skills in the natural language processing bootcamp in Python by completing the provided exercises, using math imports for log calculations, and validating your code across twenty exercises.

Learn basic text processing in Python by manipulating strings: indexing, slicing, immutability, concatenation with plus, replication, iteration, and key methods like strip, split, join, replace, find, count, and case conversions.

Learn to index and slice strings in Python, including zero-based and negative indices, handle index errors, and understand string immutability and type checks.

Explore the strip, split, and join string methods in Python, learning how strip removes leading and trailing spaces, how split tokenizes text, and how join rebuilds sentences with separators.

Solve string basics in Python through a code-along, covering indexing, slicing, concatenation with spaces, counting o's, lowercasing, splitting, and converting numbers to strings.

Explore how natural language processing tools help computers understand human language, review NLP pipelines and use cases, and learn the NLTK library for training and applying models in Python.

Explore tokenization in Python, learning how sentence tokenizers and word tokenizers split text, with a hands-on example using Nltc's Punkt to handle decimals and abbreviations.

Learn to tokenize text with nltk's word_tokenize, understand why whitespace splitting fails, and explore treebank word tokenizer and alternatives like tweet and punctuation-based tokenizers.

Apply stemming to a full sentence using porter, snowball, and lancaster stemmers, compare readability and length, and explore how the tokenized definition becomes stems in NLP pipelines.

learn to train a simple unigram pos tagger using the Brown corpus, exploring tagged sentences and the role of context in tagging words.

Train our own unigram pos tagger on the Brown corpus using a train/test split to assess generalization and understand its accuracy on unseen data.

Learn how lemmatization uses POS tagging by mapping Brown Corpus tags to WordNet tags and applying NLTK’s lemmatizer, then compare with stemming on sample text.

Explore n-grams in natural language processing by building unigrams, bigrams, and trigrams from tokenized text, analyzing context, and extracting common word pairs.

Master NLP with the NLTK library by tokenizing text into sentences. Identify the most common word per sentence, stem with snowball, and train a unigram tagger on the Brown corpus.

Build a co-occurrence matrix to capture word meaning from neighbor contexts, replacing one-hot vectors with counts, using tokenization, a vocabulary, and a windowed neighborhood.

Visualize word vectors using numpy and matplotlib to explore cosine similarity and co-occurrence, showing how vectors align, differ, and relate across dimensions.

Learn how to read CSV data in Python using the csv module or pandas, handle encodings, and build a list of dictionaries from rows for NLP workflows.

Learn to read a txt file into Python, decide between pandas or native open, handle relative or absolute paths, and tokenize the text for analysis.

Explore scraping a web page with Python using the Wikipedia library to extract text, titles, summaries, and links, offering a cleaner alternative to requests and Beautiful Soup.

Train a CBOW style neural network with Keras. Build a sequential model with dense layers and softmax activation to learn word vectors through training with Adam and cross entropy.

Build word vectors using a continuous bag of words model trained on Wikipedia data from Europe, America, and Africa, with preprocessing and tokenization to create a vocab and embeddings.

Turn word and context into mathematical vectors using one-hot representations and neural network–learned embeddings, with a context window to optimize weights and measure word similarities.

Train a neural network on Wikipedia word vectors with Keras, using a 40-node hidden layer and softmax output; validate with a 0.2 split and apply argmax for predictions.

Explore word2vec embeddings, compute cosine similarity to reveal latent word meaning, and perform analogies like king plus woman minus men yielding queen, or Spain plus England minus London yielding Madrid.

Explore word vectors and embeddings in Python by building a CBOW vocabulary, one-hot representations, and a 50-node neural network in Keras; then use Gensim Word2Vec for similarities and analogies.

Use word vectors to predict missing center words in documents, preprocess data, tokenize, train a word-vector model, and explore similarities among stored vectors in Python.