Machine Learning is a transformative field that’s reshaping industries across the globe. Whether you are a novice or an experienced practitioner, having the right resources can significantly enhance your learning experience. In this article, we present a curated list of awesome machine learning frameworks, libraries, courses, and more to help guide you through your journey into this fascinating domain. Let’s dive into the resources!
Table of Contents
Free Books
- Python Data Science Handbook, by Jake VanderPlas
- Pengenalan Pembelajaran Mesin dan Deep Learning (Bahasa Indonesia), by Jan Wira Gotama Putra
- Bayesian Reasoning and Machine Learning, by David Barber
- R Programming for Data Science, by Roger D. Peng
- Think Bayes, by Allen B. Downey
- Mathematics for Machine Learning, by Marc Peter
- Interpretable Machine Learning, by Christoph Molnar
Courses
- Applied Machine Learning in Python by University of Michigan
- Machine Learning by Stanford University
- Machine Learning with Big Data by University of California, San Diego
- Principles of Machine Learning by Microsoft
- Machine Learning for Data Science and Analytics by Columbia University
- Practical Deep Learning for Coders by Fast AI
Videos and Lectures
- Machine Learning by Andrew Ng
- Intro to Machine Learning by Eric Grimson
- Machine Learning Course – CS 156
- Machine Learning from Scratch using Python
- Gaussian Mixture Models – The Math of Intelligence (Week 7)
- Machine Learning and Data Mining Short Series for Beginner (UC Irvine)
- Complete Tutorial of Apache Spark (Beginner – Intermediate)
Papers
- Local algorithms for interactive clustering
- On Perturbed Proximal Gradient Algorithms
- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
- Nearly optimal classification for semimetrics
- A Bayesian Framework for Learning Rule Sets for Interpretable Classification
Tutorials
- A Simple Approach to Predicting Customer Churn
- Complete Guide to Topic Modeling
- K-Means Clustering in Python
- How To Implement Naive Bayes From Scratch in Python
- Twitter Sentiment Analysis with NLTK
Sample Code
- Practical Machine Learning with Python
- Data Science From Scratch
- Introducing Data Science
- Machine Learning with R
- Practical Data Science Cookbook
- Data Science with Python (Bahasa Indonesia)
- Deep Learning with PyTorch (Bahasa Indonesia)
Datasets
- UCI Machine Learning Repository
- Kaggle Datasets
- IMDb Datasets
- Machine Learning Datasets Repository
- Caption Contest Data
- Indonesia Family Life Survey
Conferences (Mostly in Indonesia)
- Seminar Nasional Sistem Informasi
- International Seminar on Intelligence Technology and Its Application
- International Conference on Advanced Computer Science and Information Systems
- International Conference on Science in Information Technology
- International Conference on Soft Computing, Intelligent Systems, and Information Technology
- International Conference on Data and Information Science
- 2019 International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)
- International Conference on Signals and Systems
Libraries
Troubleshooting
If you encounter issues accessing the resources or have questions regarding specific materials, try the following:
- Double-check the URLs for any typos or broken links.
- Ensure your internet connection is stable.
- Look for alternative sources or mirrors of the material you wish to access.
- Engage with the community for help; sometimes fellow learners can solve issues faster.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Understanding the Sample Code
The sample code listed above can feel like a maze of paths leading to different destinations. Imagine you’re in a vast library where each book represents a coding project. Each book comes with instructions, tools, and examples that guide you through various concepts in Machine Learning. Just like a library makes it easier to find knowledge, these GitHub repositories and projects simplify access to practical applications and deepen your understanding of Machine Learning principles.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.