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19 May, 5 tweets, 2 min read
Wanna get into Machine Learning but don't know where to start?

Join the other 1,400 people that have watched my course. It's gotten well over 100 5-star reviews!

$5 for the next 24 hours only!

Your full money back if you don't like it.

gum.co/kBjbC/five
If you want to support my content but don't have the money or don't need the course, a retweet/like of the original tweet goes a long way!

If you can't afford the course, comment under the original tweet, and I'll send you a link to a free copy.

Thanks for the support!
18 more hours until the price goes back to normal.
6 more hours.
1 more hour for the sale to close.

Thanks, everyone, for the support so far!

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More from @svpino

Santiago Profile picture
20 May
Machine learning is not what you think.

Here are five levels of automation:

• L0. Human-only
• L1. Shadow mode
• L2. AI-Assisted
• L3. Partial automation
• L4. Full automation

Everyone thinks L4, but that's not necessarily the end game.

Let's break these down. ↓
• L0. Human-only

Obviously, there's no automation at this level. Human makes all the decisions, so this is a manual process.

This is usually the initial state of any system right before we start automating it.
• L1. Shadow mode

As we introduce AI, a useful step is to deploy it parallel to the existing manual process.

We route requests to both the person and the system and get answers from both.

At this level, the AI system is not involved in any decisions.
Read 9 tweets
Santiago Profile picture
18 May
A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

• Class A: 90% accuracy
• Class B: 80% accuracy
• Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ↓
Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.
How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

• Class A: 95% accuracy
• Class B: 100% accuracy
• Class C: 71% accuracy

Do you see what just happened?
Read 6 tweets
Santiago Profile picture
10 May
A 6-step process that completely changed my life:

• Maximize what you don't learn
• Avoid schedules
• Use uncomfortable situations
• Learn as a byproduct
• Teach somebody else
• Circle back in a month

On how to learn efficiently and get ahead in life: ↓
Everything starts with maximizing the things I don't learn.

If I spend time on things that don't bring me value, I can't focus on what really matters to me.

By default, everything around me is noise until it's impossible to ignore.
If I don't see the value right away, I'll ignore it. Important things will make their way back to me.

Ignoring the noise makes space for what truly matters.
Read 15 tweets
Santiago Profile picture
8 May
My recommendation to learn machine learning:

• Introduction to Python Programming (Udacity)
• Machine Learning Crash Course (Google)
• Machine Learning (Coursera)

In that order. They are all free. They are all amazing.

And take your time. This is a marathon, not a sprint.
Kaggle is an amazing place to practice what you learn.
And of course, there’s always my newsletter, and my Twitter account… if you truly want to learn machine learning, you definitely want to stay tuned!
Read 4 tweets
Santiago Profile picture
8 May
A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓
Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5
"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.
Read 9 tweets
Santiago Profile picture
7 May
Do you know what scares me? Data labeling in machine learning.

We don't talk enough about it, and yet we can't do anything unless we solve this first. Labeling enough data is expensive or even outright impossible.

Some ideas to solve this problem.

↓
Let's start with an example:

You have terrain and weather information for different locations. Your goal is to build a model that predicts where to drill to find oil.

How do you label this data? You drill to find out where the oil is.

This is ridiculously expensive.
To get around this problem, you need to minimize the number of labeled examples you need to build a good model.

1. Take the data
2. Select as few examples as possible
3. Drill those holes to come up with the labels
4. Train the model

How can you achieve #2?
Read 11 tweets

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