You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.

My recommendations to start with machine learning math:

• Statistics 110: Probability — Harvard University
• Essence of Linear Algebra — 3Blue1Brown
• Multivariate Calculus — Coursera

Links and 3 additional free resources here: https://t.co/fEmpmRKEvJ

If you are looking for more machine learning content, give me a follow and I’ll keep your feed interesting 😉.

What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X → y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...

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?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you 😉.

New Cannabis Based Menu for Chao Phraya Hospital patients (service being rolled out to other hospitals too)
"This cannabis special menu is bringing different parts of the cannabis plant to use with leaves to cook as ingredients in food" #cannabis #Thailand

Whilst patients in the UK are still being denied with almost no progress with #NHS Amazing to see how much progress is being made with #cannabis as #medicine in #Thailand is the same 2 year period:

A Thread: 1/10 #Thailand allowed #cannabis #medicine in Jan 2019, 2 months after the UK.
NOW:
Is approved for 160+ conditions (everyone!)
Village doctors, clinics and hospitals can supply approved products or grow and produce their own, from nearly 100 approved formulas

2/10 - Average monthly prescription cost is £20-£30.
Educational programs started for the Medical Profession within 3 months, for many others at 6 months and school kids after a year. (Google Dr Ganja - see photo)

3/10 - Shortly they will launch a new policy for any individual or collectives to grow #cannabis for #medicine for themselves and any surplus material can be sold to the government for use in centralised production.

After therapy sessions, it is occurring to me how my parents and maybe most desi parents don't really grow as parents. For example, a person would be a very different to a baby and to a teenager because the needs and vulnerabilities are so different.

But here, I feel that growth for parents stagnants sometime around when a child is 12-13. None of our parents really talk to us about puberty and growing up. None of us are taught or can talk about relationships because of moral reasons. So what do you have?

You have parents who are still parenting their adult children like kids, not apologising to us but asking us to eat, reminding us to wear jackets, locking up cars. They care. But they are coming from a care where the parent is always concerned about keeping the kid safe and alive

which imo really reduces the kind of parental advice and care one needs as an adult. Because adults know enough about the world to no longer be infantilized but we still have parents who expect us to be the same children because they're still the same kind of parents.

This is why so many of our parental relationships are strained or just based on civilities. We're forced to hide a lot from them because parents refuse to grow as parents and acknowledge us as adults. How many of us are still lectured, have curfews, other restrictions?

While #Tulane school of medicine is rightfully being exposed for its contemporary racism towards faculty, staff, students (and patients) due to the racist firing of Dr. Princess Dennar, it’s important to call out the deeply anti-Black history of this school, specifically... 1/

The way #Tulane became a private institution (originally was a public university) at the end of Reconstruction to avoid having to desegregate. The school charter says it was founded for “white men” only... but there’s more (much more) to this story... 2/

cw: medical racism

Tulane’s school of medicine rose to national prominence for its collection of human “specimens” in their anatomy museum “rivaled only by Harvard”. How did it acquire these “specimens” you might ask?

The collection was “curated” by a Dr. Edward Souchon... 3/

Dr. Souchon was mentored by the late James Marion Sims, the “father of modern gynecology”, who himself had started collecting body parts, organs, and skeletal remains from enslaved Black people during slavery. Souchon continued “curating” his own collection in horrific ways. 4/

The Souchon Collection grew over several decades, eventually touring the state as a traveling “public health” exhibit- nearly all of the body parts stolen, harvested and illicitly acquired from formerly enslaved and poor Black people via Charity Hospital. 5/

If you want to get in the best shape of your life by 2023 read this:

1. Get 80-90% of your calories from single ingredient nutrient dense foods.

2. Avoid processed foods as much as possible because they're addictive, devoid of nutrients & leave you feeling empty.

3. Eating similar meals every day is an underrated way to get lean.

4. Lift weights 2-4x a week. Use progressive overload to build muscle.

5. Progressive overload is simply doing more through reps, weight or frequency,

6. Eat .8 to 1 gram of protein per pound of body weight. This helps you build & retain muscle. Eat protein with every meal.

7. Avoid trying to lose weight. Focus on creating good habits. The first is focused on an outcome the latter is focused on your lifestyle.

8. Your aim is to become the type of person who stays in shape. In order to change your body your identity must come along for the journey.

9. Eat your meals on a schedule. This will help train your hunger & circadian rhythms.

10. Replace snacking with water & drink it before meals. You usually want to snack because you're bored or dehydrated. Water also helps you feel full & increases energy.