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Data Science and Machine Learning

This repo is developed as a course portfolio for the graduate class "INDE 577 - Data Science and Machine Learning" at Rice University, Sping 2023.

In this course, multiple algorithms for supervised, unsupervised, and reinforcement learning are discussed. The machine learning algorithms are implemented from scracth as well as uisng state-of-the-art packages such as Scikit-Learn, Keras, and Tensorflow.

Introduction to Machine Learning and Data Science

Machine learning and data science are two rapidly growing fields in the world of technology. They are often used together to analyze and extract insights from large datasets.

Data Science

Data science is the study of data, its structure, and its analysis. It involves using statistical and computational techniques to extract meaningful insights from data. Data science includes tasks such as data cleaning, data visualization, and statistical modeling.

Machine Learning

Machine learning is a subset of artificial intelligence that involves training machines to learn patterns and make predictions based on data. It involves developing algorithms that can learn from data and improve their performance over time. Machine learning is often used for tasks such as image recognition, natural language processing, and predictive modeling.

Machine Learning and Data Science Together

Machine learning is a tool used by data scientists to extract insights from data. Data scientists use machine learning algorithms to identify patterns and trends in large datasets. Machine learning can also be used to develop predictive models that can be used to forecast future trends or make decisions based on data.

• Data science involves the study of data, while machine learning involves developing algorithms that can learn from data. Together, they form a powerful combination that can be used to solve complex problems and make data-driven decisions.

Content

The following is the list of algorithms developed and discussed in this portfolio.

Supervised Learning

1. Perceptron
2. Gradient Descent
3. Single Neuron Linear Regression
4. Logistic Regression
5. Deep Neural Network
6. K-Nearest Neighbors
7. Decision Trees
8. Ensemble Learning - Random Forest
9. Support Vector Machines

Unsupervised Learning

1. K-Means Clustering
2. DBSCAN Clustering
3. Visualizing: K-Means vs DBSCAN
4. Principal Component Analysis

Reinforcement Learning

1. CartPole Problem

Datasets

All datasets used in this work are publically available. The source and description of each dataset used is provided within the respective model.

Tools and Packages

Python, Visual Studio Code, Jupyter Nootbooks, Scikit-learn, TensorFlow, Keras, Matplotlib, Numpy, Pandas, Seaborn.

About the Author

Kashif Liaqat

Kashif is a Ph.D. student in the department of Mechanical Engineering at Rice University. His areas of research are energy systems, thermal fluids, and machine learning. Check the following links to know more about Kashif: Personal Web, Google Scholar.

References/Resources

• An Introduction to Machine Learning, Its Importance, Types, and Applications
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurélien Géron