Machine Learning

Practitioner

We are on your path to a practice in Machine Learning and Artificial Intelligence.
Consider MLP your  resource and information site for news, tools and techniques to maintain your skillset.

We are MLP 

ML Practitioner are people who are learning, doing and sharing knowledge about A.I. and Machine Learning.

Our goals are to learn to practice data science by any and all means necessary.  Self-teaching is not a weakness when it comes to a field that is growing as fast as ours. If we don’t expand our knowledge we are in danger of falling far behind.

 

Steve Levine is the ‘founder’ of ML Practitioner.com. 

 

A long and sometimes quite interesting career path through hardware and software development, network engineering, media technology, enterprise architecture, product marketing and management, community design and agile development.

Interestingly, Steve self-learned coding in Python in the singular pursuit of Data Science and analytics. This happened post silicon-valley roles and into later years.

So, as it happens, technology has caught up to us and we now seek to build out this community of the hot-pursuers of A.I. and Data Science with the cloud, big datasets and deep learning as drivers.

Good reason to keep going.  No reason to stop and a lot of open source tools, algorithms and data to fuel our journey.

Why Subscribe?

 

 

MLP is an open informational site whose content is curated by all of us, including you. So we may share and communicate  fluidly, we’ve built a list of subscribers who opt-in to become more active members of our community.

That allows us to keep in touch, carefully curate content and distribute information you have shown an interest in.  Not everything is posted to the site so some secondary channels like Slack and Email make it possible to have offline conversations.

From this, we can catch ideas and content you think should be on the site or wish to ‘guest post’ yourself.

We have no need to bombard subscribers for the purpose of selling or marketing – in short:  generally nagging you. We value your independence and simply want to stay in touch.

From time to time, we will offer subscribers participation in surveys, help with projects – yes, consulting too. We also will present offers to interview or be on a virtual panel that we can post to the site. This participation would be entirely up to you.

Let’s Mix It Up

Collaboration on machine learning projects is much easier today 

Services such as Slack can be used as a technology community of sorts with Github serving as a source code repository. We can also share Python based A.I. projects with Google Collaboratory.

Take advantage of the learning channels we have setup for MLP and while you are at it, create some of your own and share them with us.

Let’s not limit ourselves

If you know of a worthwhile online collaborative service or community that addresses the interests of ML Practitioner, feel free to let us know and we will post a description and join link here.

The ML Practitioner Slack  Community is the best place to drill into issues raised on MLP.com. In the general‘ channel we can discuss site-related issues.

In the machinelearning channel, everything about that subject.

The ‘mlpractitioner‘ channel will be for learning and the practice of machine learning.

We can always add sub-channels that deal more directly with deeper issues as needed.

Google Collaboratory is a free, Jupyter notebook service hosted on Google Cloud Platform. In addition to allowing you to create and share Jupyter Notebooks, it provides GPU-based acceleration of your models. Once you get into deep learning networks, you will see need higher processing capacity. This is the place to go deeper and at no cost to you. 

Share what you accomplish with others and let the MLP community know how it goes.

The thoughtsociety github account has all the code and notebooks for everything we do here including the amateur data science course. Go get stuff and fork it.

Code and  notebooks For the Amateur Data Science Course

Here are some of the repo’s:

Gitdemo

ADS Track 3

ADS Track 4

 

Kaggle was created for the purpose of enabling data science competitions. It has brought many students of the field together with professionals who have shared their knowledge as well as collaborated on the competitions themselves.

After being acquired by Google, it has morphed into a very diverse and rich ecosystem of datasets, code kernels and people who have a keen interest in Machine Learning and data science in general.  Let’s make our presence known going forward and hopefully something great will come of it.