Machine Learning Python Programs

Machine Learning Lab Programs in Python (PDF, 24 pages)

A ready-to-use collection of 11 machine learning programs in Python, laid out as clean, readable slides. It suits BCA, BSc and engineering students preparing for ML lab practicals, record writing and viva.

What’s inside

• Regression: Simple Linear Regression
• Classification: K-Nearest Neighbours (accuracy vs K, and uniform vs distance weights), Decision Tree (entropy), Gaussian Naive Bayes, Support Vector Machine (linear kernel, support vectors, decision boundary) and Random Forest on the breast cancer dataset
• Clustering: K-Means built from scratch with the elbow method, and Hierarchical Clustering with a dendrogram
• Neural networks: Single-Layer Perceptron using Keras and TensorFlow

Why you’ll like it

• Programs are in numerical order, so they’re easy to follow in lab
• Dark code panels with syntax colouring make the code easy to read on screen or in print
• Long programs are split at logical points across slides
• Uses standard libraries: NumPy, Matplotlib, scikit-learn, SciPy and TensorFlow/Keras
• Includes a contents page up front
• Comments in the code explain each step

Format: PDF, 16:9 landscape, 24 pages

Before you list it, a few things to consider:

• Add sample outputs. The PDF has code only, with no output screenshots or graphs. Buyers will value it more, and you can charge more, if you add them.
• Check the code is yours to sell. If these programs came from a course, a teacher’s syllabus or a shared repository, confirm you’re allowed to sell them. Some of the datasets and code patterns come from standard library examples, which is fine, but the lab material itself may not be.
• Don’t promise results. Avoid claims like “guaranteed marks”, since you can’t know what each institution expects.