BC MACHINE LEARNING
Saved by @SteveeRogerr

1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

 2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.
3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*
4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.
5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do:
6/🧵 Low-quality data: use small sample sizes, synthetic data, and non-randomly sampled data to gain insights about the data collection process itself.
7/🧵 Rough-and-dirty models: seek an understanding of what the payoff from minimum effort looks like. Start with bad algorithms which you know are only going to give you a benchmark, not your best solution.
8/🧵 Multiple comparisons: instead of picking a single hypothesis test, feel free to throw the kitchen sink at your data to discover signals worth basing your final approach on. Add deadlines and MVP milestones to avoid the trap of infinite polishing, poking, and prodding.
9/🧵 If the statistician in you isn’t screaming yet, I admire your sangfroid. This advice breaks pretty much every rule you learned in class. So why am I endorsing these “bad behaviors”?
10/🧵 So why am I endorsing these “bad behaviors”? Because this is the pilot phase. I’m all about following the standard advice later, but this early phase has different rules.
11/🧵 The important thing is to avoid rookie mistakes by remembering these 2 crucial principles:
12/🧵 Principle 1: Don’t take any findings from the early phase too seriously.
13/🧵 Principle 2: Always collect a clean new dataset when you’re ready for the final version.

For more info: https://t.co/Ue332SMjy1
14/🧵 You’re using your initial iterative exploratory efforts to inform your eventual approach (which you’ll take just as seriously as the most studious statistician would). The trick is to use the best of exploratory nimbleness to inform what’s worth considering along the way.
15/🧵 If you’re used to the rigidity of traditional statistical inference, it’s time to rediscover the benefits of pilot studies in science and find ways to embed the equivalent into your data science projects.
16/🧵 The key thing to understand about this advice is that

- finding good questions
- finding good answers
- finding good approaches going from one to the other

are all different objectives that require different approaches. Sometimes there's homework to do before answers...

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🧵👇

Before diving into the math, I suggest first having solid programming skills.

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(2 / 17)

In Python, these are the concepts which you must know:

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(3 / 17)

If you want to learn python, these courses are freecodecamp could be of help to you.

🔗Basics: youtube .com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube .com/watch?v=HGOBQPFzWKo

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You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 17)
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The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

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2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

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3⃣ Start using notebooks

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Pratham Prasoon
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@PrasoonPratham
Are you planning to learn Python for machine learning this year?

Here's everything you need to get started.
🧵👇

In this thread, we'll look at all the concepts in Python you need to know for machine learning along with free resources to help you out.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19

You can use many languages for machine learning, why Python?

Because of 2 reasons:
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This makes Python a no-brainer to learn for machine learning as a beginner.

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These are the absolute basics which you must know about:

- Basic terminal commands
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11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
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👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

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- pixels of a picture
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3. Samples

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First of all, we do not start from zero. I got some prior knowledge from past reversing attempts and want to share some important facts.

TikTok's (log data) encryption is accomplished by a native library. The Android Java code just serves as proxy function to the native function

The decompiled code for the respective native JNI function of an older TikTok version looks something like this, but in this example I use the most current TT version (no statical analysis done, yet)


In case you never reversed native libraries which were build to interface with Android Java layer via JNI, I highly suggest the entry level introduction on the topic by @maddiestone

Before we start, I want to pinpoint some important aspects (which are also covered by Maddie's videos).

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Read BS self-help.

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