Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning. On the other hand, these steps are becoming ever increasingly delicate due to the extensive rise in the number, diversity, and distributedness of machine learning resources. Multi-agent systems, when applied to the design of machine learning platforms, bring about several distinctive characteristics such as scalability, flexibility, and robustness, just to name a few. This paper proposes a fully automatic and collaborative agent-based mechanism for selecting distributedly organized machine learning algorithms and simultaneously tuning their hyperparameters. Our method builds upon an existing agent-based hierarchical machine-learning platform and augments its query structure to support the aforementioned functionalities without being limited to specific learning, selection, and tuning mechanisms. We have conducted theoretical assessments, formal verification, and analytical study to demonstrate the correctness, resource utilization, and computational efficiency of our technique. According to the results, our solution is totally correct and exhibits linear time and space complexity in relation to the size of available resources. To provide concrete examples of how the proposed methodologies can effectively adapt and perform across a range of algorithmic options and datasets, we have also conducted a series of experiments using a system comprised of 24 algorithms and 9 datasets.

Nothing needs to be downloaded and everything is absolutely free. This is truly one of the best resources for anyone, even a non-machine learning person, and I am very happy to share it with you. And the last section in this article is the most important. This is the answer to the question "how to start reading ML research articles?". And if I had something to say, I would definitely write a separate article about it.

The resources in this repo are only for educational purpose. Do not use resources in this repo for any form of commercial purpose. If the author of ebook found your intelligence proprietary violated because of contents in this repo, please contact me and I will remove relevant stuff ASAP.

What's Inside this Roadmap? 1. Machine Learning Problems - what does a machine learning problem look like? 2. Machine Learning Process - once you've found a problem, what steps might you take to solve it?

In this 28-video series, you will learn important concepts and technologies to build your end-to-end machine learning applications on Azure.

I have come across a few great resources that I wanted to share. For students taking a machine learning class (like Northwestern University's MSDS 422 Practical Machine Learning) these are great references, and a way to learn about them before, during, or after the class. This is not a comprehensive list, just a starter. There is a free online textbook, Neural Networks and Deep Learning. There is a great math visualization site called 3Blue1Brown and they have a YouTube channel.

Having a software background and transiting into data science career is quite fulfilling and opened an ample of opportunities to learn and share. With times even helped a quite a few people to leap jump into the field of data science. Below are the few hacks i used during my self-learning process which may also help you to start your career in data science. Python is my preferred language, You can try out SOLO learn with python for free. It even has quizzes to brush up what you have learned.

This is a not-particularly-systematic attempt to curate a handful of my favorite resources for learning statistics and machine learning. This isn't meant to be comprehensive, and in fact is still missing the vast majority of my favorite explainers. Rather, it's just a smattering of resources I've found myself turning to multiple times and thus would like to have in one place. Finally, I've added a section with links to a few miscellaneous websites that often produce great content. Of the above, the second section is both the most incomplete and the one that I am most excited about.

For almost all machine learning projects, the main steps of the ideal solution remains same. For each step, I was doing some research on the web depending on my business object and jotting down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I've listed them and categorised by each step (all of the resources are free except the ones that have'paid' in the end):

Machine Learning mostly requires the fundamental understanding of Linear Algebra, Statistics and Probability. While you can learn how to use all the advanced libraries to accomplish your ML tasks, once something breaks you won't be able to fix it. Even worse, you won't be able to understand any new studies being done in the field, since to understand them, you will need a somewhat deep understanding of mathematics. Also, you won't be able to conduct your own studies or play around with mathematical ML concepts. This being a very huge topic, it is somewhat hard to find good resources which explain the content properly.