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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Pratham Prasoon
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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:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
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Jan Giacomelli
Jan Giacomelli
@jangiacomelli
🤔 Python decorators

What are they? How do you use them?

🧵 Let's find out 👇

1️⃣ Decorator is a function or a class that wraps another function or class modifying its behavior.

So how does that work?

The first thing to know is that everything in Python is an object - functions too


2️⃣ That means they can be passed to another function as an argument or returned from a function

Functions that take other functions as an argument are called higher-order functions


3️⃣ In Python, you can define a function inside other function - such functions are called inner functions


4️⃣ To create a decorator you just need to apply all of that together

log_enter_leave is a decorator.

my_function is the function.

To alter my_function's behavior we reassign it applying log_enter_leave decorator.
MACHINE LEARNING
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Which libraries do you really need to get started with Machine Learning and why?

🧵👇

First and foremost, make sure that you have nailed the fundamental concepts of Python because machine learning requires a lot of programming!

(2 / 19)

If you know these topics, then you are good to go with machine learning in Python

- Object-oriented programming in Python:Classes,Objects,Methods
- Lists & List functions
- List comprehension
- List slicing
- String formatting
- List,Dictionaries & Tuples

(3 / 19)

Now, let's understand what popular python machine learning libraries do.

We will talk about👇
- TensorFlow (+ Keras)
- PyTorch
- Pandas
- Numpy
- Matplotlib
- SciKit Learn
- Seaborn

(4 / 19)

Now let's cover the libraries that you have to learn (at least the basics) for machine learning

1. Pandas

Pandas is a python library that allows you to store and read data from spreadsheets ( .csv, .xlsv files ) in structures called Dataframes.

(5 / 19)
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learning machine learning this year?

These are the Python frameworks you need to learn as an absolute beginner.

🧵👇

It can be overwhelming seeing all the machine learning frameworks at once and not even knowing what they do.

I made this thread so that your machine learning journey becomes just a little bit easier.

(2 / 11)

This thread will only focus on Python frameworks.

Why?
Because Python is the most common path into machine learning.

( If you have experience with any other language then learning Python will not be difficult at all. )

(3 / 11)

Here's the entire list:

- Pandas
- Numpy
- Matplotlib
- TensorFlow
- PyTorch
- Keras
- Scikit Learn

Let's see what they do.

(4 / 11)

You'll have to interact with csv files or databases on a regular basis to access data in machine learning, pandas helps you do just that.

The cool thing about Pandas is that it actually doesn't change the original data...

(5 / 11
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Jan Giacomelli
Jan Giacomelli
@jangiacomelli
🤔 Python generators

What are they? How to use them?

#Python

🧵Let's find out 👇

1️⃣ Python generators are lazy iterators delivering the next value when their .next() is called.

They are created by using the yield keyword

next() can be called explicitly or implicitly inside for loop

They can be finite or infinite


2️⃣ yield - where a value is sent back to the caller, but the function doesn’t exit afterward as with the return statement

The state of function is remembered.

For example, the number is incremented and sent back from yield at the consecutive call next()


3️⃣ Generator stores only the current state of the function - it generates next element on next() call and forgets the previous one -> it saves memory

For example, we don't need to store 1mio elements in memory to do something with each element


4️⃣ When you call a generator function generator object is returned - it's not executed yet

It executes only when next() is called

For example, that's why Exception is raised only on the next() call
MACHINE LEARNING

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