Resources on Machine Learning, Python, Big Data, etc.
M. Ali Yousuf
Last updated: 03/14/2024
Over the last 30 years, I have taught a wide range of courses on artificial intelligence and big data. The course materials for these courses are either available via Blackboard LMS or have been removed due to their age. This page lists all of the courses I have taught and provides information about some of the free resources that are still available.
Introduction to Data Science
Introduction to Data Analysis and Machine Learning
Platforms for Big Data Processing
Capstone Project in Data Science
Deep Learning with MATLAB
Artificial Intelligence in Films
Robotics
Robotics and Cybernetics
Intelligent Manufacturing
Image Processing
Advanced Robotics
Parallel Robots and their Control
Parallel Supercomputing
Genetic Algorithms
Artificial Neural Networks
These are the most useful books for ML:
Machine Learning with PyTorch and Scikit-Learn, https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312
GitHub page to download code, https://github.com/rasbt/machine-learning-book
Hands-On Machine Learning with Scikit-Learn and TensorFlow
First Edition of the book: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition
Latest Edition: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-dp-1098125975/dp/1098125975/ref=dp_ob_title_bk
GitHub page of the book with code as jupyter notebooks: https://github.com/ageron/handson-ml3
See the book page: http://shop.oreilly.com/product/0636920052289.do
Python Machine Learning 1st Edition by Wei-Ming Lee
Download the full book sample code: https://www.wiley.com/en-us/Python+Machine+Learning-p-9781119545637
Free: Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan, https://alex.smola.org/drafts/thebook.pdf
Free: An Introduction to Statistical Learning. with Applications in R. G. James, D. Witten, T. Hastie and R. Tibshirani, https://www.statlearning.com/
Python Data Science Handbook, in the form of (free!) Jupyter notebooks
Deep Learning by I. Goodfellow, Y. Bengio & A. Courville, https://www.deeplearningbook.org/
TigerGraph is a fast and scalable graph database,
Web page, https://www.tigergraph.com/
Github page, https://github.com/tigergraph
YouTube, https://www.youtube.com/@TigerGraph
Neo4j provides powerful native graph storage, data science, and analytics,
Webpage, https://neo4j.com/
Github page, https://github.com/neo4j
YouTube, https://www.youtube.com/neo4j
CogDB is a Micro Graph Database for Python Applications (Free Python Library)
Web page, https://cogdb.io/
Github page, https://github.com/arun1729/cog
Following references taken from a post by Maryam Miradi.
Graph Networks: Traditional Methods to extract features from the of Graph, https://medium.com/@aishweta/graph-networks-traditional-methods-to-extract-features-from-the-of-graph-2e6cd86e5c10
NetworkX: A Practical Introduction to Graph Analysis in Python, https://soumenatta.medium.com/networkx-a-practical-introduction-to-graph-analysis-in-python-cc72f3dda916
Graph Networks Visualization with pyvis and keyword extraction, https://medium.com/@stephanhausberg/graph-networks-visualization-with-pyvis-and-keyword-extraction-cd973d372e2c
Fraud Detection with Graph Analytics, https://towardsdatascience.com/fraud-detection-with-graph-analytics-2678e817b69e
NetworkX Tutorial, https://networkx.org/documentation/stable/tutorial.html
Different Graph Neural Network Implementation using PyTorch Geometric, https://arshren.medium.com/different-graph-neural-network-implementation-using-pytorch-geometric-23f5bf2f3e9f
A complete course on MACHINE LEARNING FOR HEALTHCARE at MIT https://ocw.mit.edu/courses/6-s897-machine-learning-for-healthcare-spring-2019/
2022 - AI in Healthcare, https://www.marktechpost.com/ai-magazine/ ; https://www.marktechpost.com/wp-content/uploads/2022/03/AI-in-Healthcare-Magazine-Marktechpost.pdf
2022 - A Clinician's Guide to Artificial Intelligence - Steven Lin, https://www.jabfm.org/content/35/1/175
2022 - Guidelines and quality criteria for artificial intelligence-based-prediction models in healthcare: a scoping review - Hond, https://www.nature.com/articles/s41746-021-00549-7
2021 - AI In Focus - The Healthcare Technology Roadmap - PYMNTS, https://www.pymnts.com/study/ai-in-focus-healthcare-technology-artificial-intelligence-data/
2020 - Working Group on Digital and AI in Health Reimagining Global Health through Artificial Intelligence: The Roadmap to AI Maturity, https://www.broadbandcommission.org/publication/reimagining-global-health-through-artificial-intelligence/
2020 - Healthcare AI Trends To Watch - CB Insights, https://www.cbinsights.com/reports/CB-Insights_AI-Trends-In-Healthcare.pdf?utm_campaign=ai-healthcare-trends_2018-09&utm_medium=email&_hsmi=99753836&_hsenc=p2ANqtz--PpB3sn0upMLxAlqgJJochcx-5PJeQ9l9K497i_1D3-1EdC__Hhyr2iDUGIjNzU8aIlTR2SIYFhR0Esfvei6pGipzTgQ&utm_content=99753836&utm_source=hs_automation
2020 - Bringing Analytics and AI into the Clinical Setting - Databricks-WEP, https://techresearchonline.com/wp-content/uploads/white-papers/HC_Asset_1.pdf