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Practical advice to make an impact with Machine Learning

 
 
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Beware the power of Data!

Unless you have shut yourself away for the past 2 years, you probably heard about what everybody else in the tech/business industry is talking about: "the biggest threat that will redefine markets and crush businesses". You see it coming? I am of course talking about all the hype surrounding Big Data and Predictive Analytics!

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Louis Dorard Louis Dorard

Learnings from running my first online workshop

Two weeks ago I ran my first Bootstrapping Machine Learning workshop. I thought it would be a great way to get some feedback on the content of the book I am writing on that topic, and on what people are most interested in learning about. From using the right tools to host the workshop to structuring the content, I learnt quite a good deal on how to deliver the most value to participants and justify the amount of money I will be charging for future workshops.

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Business Louis Dorard Business Louis Dorard

Going to Silicon Valley? Plan carefully.

I recently came back from a 3-week long business trip to Silicon Valley and thought I'd share my experience, in the form of a list of recommendations, for those who are thinking about expanding/moving their startup to SV. Unfortunately, such trips are not always well motivated, but they're always costly.

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Louis Dorard Louis Dorard

Writing about writing a book...

A couple of days ago I published a post on Medium where I related my experience since I started writing Bootstrapping Machine Learning. In this post, I covered a few different aspects of writing, from ego to business... you should check it out! I was quite impressed with the Medium platform and its text editor, you should check it out too!

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Machine Learning Louis Dorard Machine Learning Louis Dorard

When Machine Learning fails

Machine Learning has been used successfully in so many apps that you'd think you can use it to predict just about anything you want. Unfortunately, that's not the case. If you have an idea that relies on ML, it pays to have an understanding of why ML doesn't work all the time, and to use that understanding to reflect on potential issues while working on that idea.

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