NLP and Python Development: Basics to Advanced Applications

Learn to build a chatbot from scratch with Python and NLTK, using Anaconda Navigator to install and manage packages via conda or pip, running code in Jupyter Notebook or Spyder.

Install and verify the NLTK package in Anaconda or via pip or conda, download and manage specific corpora like the Brown dictionary, and apply nltk.chat with reflections for chatbots.

Create and implement bot pairs by listing patterns and responses, including greetings like hi, hello, namaste, and good morning, and build from scratch from hard-coded basics to ml-driven improvements.

Explore building a simple Python chat bot using Anaconda and NLTK, handling intents like bye and quit with regular expressions to close conversations.

Learn to build a Python GUI calculator by importing math and Tkinter, creating a window, configuring its appearance, adding a calculator label, and arranging widgets with grid.

Build a python calculator gui that converts string input to integers, computes sqrt, sin, cos, tan and hyperbolic cosh, sinh, tanh, and adds button commands for log and log2.

Build and integrate exponentiation, factorial, square root, degrees, and power functions using the math module, verify availability with tab navigation, and test calculator operations while debugging syntax and commands.

Develop a Python calculator GUI with Tkinter, implementing power and dot operations using lambda and the ** operator, and verify functionality in Spyder, Jupyter, and PyCharm.

Learn how to create and manipulate numpy arrays and ndarrays, access functions, and perform indexing, slicing, and arithmetic operations using arrange and linspace, with printing and reshaping examples.

Explore NumPy array unary operations, axis-based sums, max, min, and cumulative sums with cumsum, plus ufuncs like sine, cosine, exponential, sqrt, and indexing for efficient manipulation.

Explore copies and views in numpy arrays, showing how shallow views share the base array and memory, while deep copies create a separate array with no reference to the original.

Explore numpy indexing and boolean masking, including i,j access for specific cells, colon comma j for 3-D arrays, boolean masks like a>4, and assigning or selecting using masks.

Learn how to plot x and y data with pyplot, customize axes ranges, colors, markers, and line styles, and use setp to adjust properties for multiple lines.

Master matplotlib basics for multiple figures and subplots, using pyplot, current figure and axis (gca, gcf). Annotate with text and the annotate method.

Explore pandas series and data frames as labeled data structures with data and index. Create series from ndarrays, dictionaries, or scalars, and understand index behavior and missing data.

Explore Pandas series and data frames, and learn how ndarray-like series work with numpy functions and slicing. Understand extension dtypes, extension arrays, and label alignment with nan for missing values.

Explore basic operations on pandas series, including nan propagation, naming and renaming series, and construct two-dimensional data frames from dicts and ndarrays with index and columns.

Learn scikit learn basics and key machine learning concepts. Understand problem setting, training versus testing, supervised and unsupervised learning, and handling multidimensional features.

Learn how to load inbuilt data sets from scikit-learn, including iris, digits, and Boston housing price data, and explore their 2d arrays and targets for classification and regression.

Explore how grid search optimizes model parameters with cross-validation and apply k-means clustering to iris data, illustrating centroids, inertia, and the impact of dimensionality reduction.

Explore principal component analysis (PCA) and how to project data with a PCA transformer to reduce dimensionality and explain variance. Build pipelines that chain transformers and estimators for seamless fit.

Identify languages with a character-level tf-idf using 1–3-gram features and a perceptron classifier, including data preparation with fetch data.py and evaluation via reports and confusion matrices.

Explore sentiment classification of movie reviews with Python, tuning n-gram ranges and classifiers to improve accuracy. Use grid search, confusion matrices, and mean and std scores to compare parameters.