Machine-Learning-with-Python

I've earned the certification of this course (Machine Learning with Python). You can verify it on this page.

This repository contains my solutions to challenges from the freeCodeCAmp Machine Learning with Python course:

1. Rock Paper Scissors
   Developed a program to play Rock, Paper, Scissors against four different bots, achieving at least a 60% win rate. The solution used strategic adaptations based on the opponents' gameplay patterns, illustrating algorithmic decision-making.

2. Cat and Dog Image Classifier
   Created a convolutional neural network using TensorFlow and Keras to classify images of cats and dogs, achieving an accuracy of over 63%. This project introduced me to image processing and computer vision techniques.

3. Book Recommendation Engine using KNN
   Implemented a book recommendation algorithm using K-Nearest Neighbors on the Book-Crossings dataset. This project involved data cleaning, feature engineering, and the application of distance-based similarity measures to suggest books.

4. Linear Regression Health Costs Calculator
   Built a regression model to predict healthcare costs based on individual features such as age, BMI, and smoking status. The model achieved a Mean Absolute Error of under $3,500, emphasizing regression analysis and model evaluation.

5. Neural Network SMS Text Classifier
   Designed a neural network to classify SMS messages as either "ham" (non-spam) or "spam." The model provided predictions with a likelihood score and classification output, demonstrating text processing and binary classification.

Key Takeaways:

• Introduced to core machine learning concepts, including supervised learning, regression, and classification.
• Hands-on experience with scikit-learn, TensorFlow, and Keras for implementing machine learning pipelines.
• Gained an understanding of both traditional ML models (e.g., KNN, linear regression) and deep learning techniques (e.g., neural networks).