Machine Learning bootcamp adminestered by RoboGarden - https://bootcamps.robogarden.ca/bootcamp/machine-learning

This Repository has projects I completed in RoboGardnen’s ML bootcamp following in class presentations and later improving code as homework.

Since I had to learn two relatively new subjects : Machine Learning and Python, I concentrated mostly on getting concepts right and learning how to use python to analyze and visualize data.

As a result, most of the original coding is done by our instructor Dr. Qazi and TA Kanishqk Singh. I also researched Kaggle and other sources to find interesting code especially for visualizations and adjusted to different datasets.

Each project includes data cleaning, analysis and visualization.

Python Libraries used during the course:

• Pandas
• Numpy
• Scikit-Learn
• Matplotlib
• Seaborn

Training Machine Learning Model: Supervised - Prediction and Classification

• Linear and Polynomial Regression
• KNN
• SVR / SVC
• Different types of Decision Tree, including Random Forest
• Naive Baynes
• Understanding results using Classification Matrix, R2-score and MSE.

Unsupervised:

• K-means
• To evaluate results: homogeneity_score, completeness_score, v_measure_score

Most projects in this repository is a work in progress since they were introductions to different concepts and allowed me to practice data cleaning and visualizations skills and apply different Machine Learning techniques to build models to be used in prediction and / or classification.

I usually update projects when I come across interesting code or shortcuts or new ways of visualizing data that I thought a project will benefit from I come back and make changes.