Overview
This documentation site serves as an instruction manual for Python basics, data handling, visualization, machine learning, and introductory deep learning workflows.
The site contains 290 code listings organized by topic.
How to use this documentation
- Start with the topic you need from the left-hand contents list.
- Read the short concept notes at the top of the page.
- Copy code listings into a notebook, Colab, or a Python file when you want to run them.
- For Colab-only commands, such as Drive mounting, shell commands, package installs, and file upload helpers, run the code in Colab or adapt the path/file logic for local VS Code.
Topic map
| Page | What it teaches |
|---|---|
| Python Environment and Colab Workflow | This page covers executing Python, checking the runtime, using loops, storing dictionaries, and mounting Drive in Colab. |
| Image Processing with Pillow and OpenCV | This page demonstrates loading, inspecting, cropping, resizing, blurring, changing pixels, and displaying images with Pillow, Matplotlib, and OpenCV. |
| Local Files, Downloads, Drive, and Uploads | This page collects writing files, downloading files, saving to Drive, and uploading files in Colab. |
| Dataset Loading: Iris, seaborn, and scikit-learn | This page explains how to load the Iris dataset from seaborn and scikit-learn, inspect metadata, and separate features from labels. |
| NumPy Arrays, Statistics, Splitting, and Iris Plots | This page covers array indexing, slicing, basic statistics, manual train/test splits, train_test_split, class counts, and simple Iris visualizations. |
| Image Datasets and OpenML | This page covers image datasets, Olivetti faces, OpenML fetching, vector-to-image reshaping, and subplot grids. |
| Generated Data and Coordinate Plots | This page shows random integer and decimal data, coordinate pairs, point plots, and selected dimensions from generated matrices. |
| Clustering with k-means | This page explains unsupervised clustering with generated points and Iris data, including centroids, cluster labels, elbow evaluation, and silhouette score. |
| Linear Regression | This page covers simple and multivariate linear regression, train/test splitting, prediction, coefficients, and normalization. |
| Classification with KNN and Gaussian Naive Bayes | This page covers Iris classification with KNN and Gaussian Naive Bayes, including accuracy, confusion matrix, precision, recall, F1-score, and reports. |
| Neural Networks with Fashion MNIST | This page covers dense neural networks on Fashion MNIST, including normalization, model layers, training history, predictions, and classification metrics. |
| Convolutional Neural Networks with CIFAR-10 | This page covers CNN image classification on CIFAR-10, including convolution blocks, dropout, early stopping, model saving, and custom-image prediction. |
| Autoencoders for MNIST Reconstruction | This page covers simple, denoising, and convolutional autoencoders for MNIST image reconstruction. |
| Transfer Learning and Model Zoo Workflows | This page covers transfer learning with ResNet50, frozen pretrained bases, TensorFlow Hub, PyTorch models, and YOLO pose tracking in Colab. |