Disclaimer and Publication Policy and Licensure

5 min readAug 18, 2019


Updated Nov 2021. This disclaimer applies to all of our articles. Before you read our articles, contents, or view our media please check our disclaimer page (this page). It is very important to note that this article and all articles, content on our website and affiliated websites, social media, or anywhere else on the internet are for discussion / entertainment / hypothetical scenarios, simulation purpose only. They should NOT be considered professional advice. Content on this site, our affiliated sites and social media and any where else on the internet are NOT intended for commercial purpose; NOT for production purpose; NOT for professional usage. Most of our contents are geared towards beginners, not professionals.

We pay our staff writers to write and we pay for our images, medias illustration and multi-media assets, so all rights reserved. No republication / repost without permission, please. Sometimes we cite and use images from publications and educational resources. These resources each have their own copyright and licensing fine prints. Please do not re-share images you see in our articles without prior permission. Our articles are exclusively published on https://ml.learn-to-code.co, https://uniqtech.medium.com, https://blog.uniqtech.co, any other sites have not be permitted to use our articles and may contain malicious links and content. Please only use contents from our main sites.

Our staff, founder, may own a variety of cryptocurrency, tokens, and blockchain related assets/ securities. Mentioning any blockchain tech or crypto is not a form of endorsement. We cannot give financial advice. We are not financial advisors. We will not be able to timely alert the users what we own.

Many discussions on this blog is highly experimental, and or informal, and or hypothetical and or theoretical. They are NOT professional advice. They are NOT education material.

Any discussion on privacy or security is strictly experimental, discussion-based or for hypothetical scenarios only. Should NOT be used for security design, implementation nor for any HIPAA related implementation. For example any differential privacy or secure AI is strictly experimental, and they are different concepts from HIPAA compliance for example. I/We are not professionals and CANNOT make any advice on GDPR nor HIPAA compliance.

Sometimes we use image and numeric data that is related to topic of health, for example x-ray image classifications. We’d like to emphasize that these articles are only informational. They are not to be used for diagnosis purpose, even if for personal use. They are not to be used in real health scenarios. They are not professional advice, not diagnosis, not health advice. Please only use trusted resources like the CDC and contact your physical and doctors. Data science models are quite often inaccurate, incomplete or highly specialized in one specific scenario, which will not apply generally. Generally you should not trust these experimental results.

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Our articles are for the exclusive publications on our Medium channel, other channels or domains we own or manage. Please no repost, no republication, no scraping. It takes us a lot of effort to write these tutorials. All rights reserved. We have granted one-off re-publication in the past, for example on KDNuggets. All rights reserved. Contact us https://ml.learn-to-code.co/message3.html

Thanks for reading our article, we publish exclusively on our own channels (newsletter, Medium, substack) exclusively. This is how we earn a living. Please no republication anywhere else.

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Please note while we strive for best high quality accurate content, there is no guarantee that the content is accurate and always fresh. Technical tutorials can quickly become out-of-date. We will try our very best to make corrections ASAP. Any community contribution is welcome. We are a resource and informational newsletter and content blogging publisher. We do not and cannot guarantee any level of knowledge for machine learning, deep learning, or any technology on our site. Only accredited schools and training can guarantee that. We currently do not offer such courses. There is definitely no job guarantee. Machine Learning is a deep and wide field. Often PhD, research experiences and graduate level coursework are required. That being said, not every Kaggle competition winner is a PhD student. There are opportunities for novices but there is no guarantee. Though words like production, professional may be mentioned, no articles or content should be considered production nor professional advice. These contents are for informational and discussion purpose only. They should be for personal development and not used for commercial work. Thank you for your understanding. We are a tiny team of recreational writers. Please take our writing and tutorials with a grain of salt. Every effort will be made : we will always try to write accurate, succinct and informational contents.

Uniqtech writers are great technical tutorial writers, who are bootcamp graduates, free lancers, technical founders and or entrepreneurs. Uniqtech writers do not necessarily hold degrees or advanced degrees in the subject area, nor do they have education or counseling credentials, so please take all the words with a grain of salt and no words should be considered professional opinions nor should they be considered advice. That being said, our writers are effective, great communicators and know what bootcamp graduates need. We know what are the knowledge gaps and obstacles because we were in your shoes not too long ago studying these subject areas. These publications are written by bootcamp graduates for bootcamp graduates.

Updated July 2020: We love providing developers and machine learnists with great resources. Please note : each of the entity mentioned in our contents may have their own restrictive term of usage, term of service, and licensure, restriction, limitation on data use and sharing. It is your responsibility to adhere to laws and follow individual policy and guideline. In general, we recommend using our resources, articles and contents for personal enrichment purpose only and do not use it in commercial settings, and do not use code examples in production settings. Our content does not constitute professional advice, does not diagnose, monitor, track or resolve any real world issues including health. Our tutorials have not been tested in production, nor in commercial settings. We do not have the manpower to ensure privacy, fairness of our algorithms.

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Please note that we are a small startup, while we take notes and use computer generated audio transcripts on events we attend, we do not have a designated human to review the transcripts. There may be mistakes, offensive language, typos and incorrect transcription. Please report any issues by messaging us https://ml.learn-to-code.co/message3.html We will correct ASAP. These transcripts are auto generated, we cannot ensure its accuracy, it may contain contents that does not represent our values. We will do our best to make corrections when they are brought to our attention. We ask the community for help.

We are basically only provide information. This information may be out of date. We are not responsible for any costs or damages occurred using or related to the services and technology mentioned in any of our articles, websites, contents. All our contents are for informational purpose only. Mentioning a technology does not constitutes a recommendation nor does it mean professional advice. While we try, we cannot guarantee the information mentioned in our contents are error-free and up-to-date.

Mentioning, discussing, even praises does not mean endorsement. A technology may seem useful, trendy, best practice at the time, but may soon prove to be a passing phase. We do not endorse any brand, tech, services nor methodology mentioned in our contents across all channels. We are not certified advisors of any kind. NONE of our content is for commercial use. Error may occur in our contents, please verify before using or sharing.