Intermediate Machine Learning Projects

These intermediate machine learning projects focus on data processing and training models for structured and unstructured datasets. Learn to clean, process, and augment the dataset using various statistical tools.

6. Reveal Categories Found in Data

The Reveal Categories Found in Data project helps you explore customer feedback using clustering and natural language processing (NLP). You’ll organize reviews from the Google Play Store into distinct categories using K-means clustering. Understanding the common themes from customer feedback is essential for product development teams to address user pain points, improve features, and boost user satisfaction through actionable insights.

Try replicating the result on a different dataset, such as the Netflix Movie dataset.

7. Word Frequency in Moby Dick

In the Word Frequency in Moby Dick project, you will scrape the text of Herman Melville’s Moby Dick and analyze the word frequency using Python’s nltk library. This project introduces key natural language processing (NLP) techniques and helps develop an understanding of how frequently used words reveal patterns in the text. It is a great project for literature enthusiasts, historians, or researchers interested in text mining and linguistic analysis.

8. Facial Recognition with Supervised Learning

In the Facial Recognition with Supervised Learning project, you will build a facial recognition model using supervised learning techniques with Python and scikit-learn. The model differentiates between images of Arnold Schwarzenegger and other people. This project is important in the growing field of facial recognition technology, with broad applications in security, authentication systems, and even social media platforms where facial detection is commonly used.

9. Breast Cancer Detection

Use the Wisconsin Breast Cancer dataset to predict whether a tumor is malignant or benign. The dataset includes details about tumor features, such as texture, perimeter, and area, and your goal is to build a classification model that predicts a diagnosis based on these characteristics.

This project is essential in healthcare applications, providing valuable insights into medical data analysis and the potential for developing diagnostic tools that can aid in early cancer detection.

10. Speech Emotion Recognition with librosa

In the Speech Emotion Recognition with Librosa project, you will process sound files using Librosa, sound file, and sklearn for the MLPClassifier to recognize emotion from sound files.

You will load and process sound files, perform feature extraction, and train the Multi-Layer Perceptron classifier model. The project will teach you the basics of audio processing so that you can advance into training a deep learning model to achieve better accuracy.

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