BC MACHINE LEARNING

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Santiago
@svpino 5 years, 7 months ago 1425 views

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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.

👇

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.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇

5⃣ Focus on the analysis and not the code

In the beginning, spend your time and energy analyzing the problem and its solution.

Code is not important at this stage. Code can come later.

👇

6⃣ Start incorporating new algorithms

As you work through problems, start incorporating new algorithms into your toolset.

Here are a few great options to start:

1. Decision Trees
2. Linear regression
3. Logistic regression
4. Neural Networks
5. KNN

👇

7⃣ Get familiar with a general process to approach problems

Here is a good start:

1. Define the problem
2. Prepare the data
3. Spot-ccheck algorithms
4. Improve the results
5. Present the results

👇

8⃣ Pick a new problem and repeat

It shouldn't be surprising that the best way to improve is to practice and solve new problems.

If you don't have access to real-life problems, get familiar with Kaggle: everything you need will be there.

In the next coming weeks, I'll be posting a whole series of machine learning advice for people wanting to start.

Stay tuned!

More from Santiago

Santiago
@svpino

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
• ...

Santiago
@svpino

I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."

This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.

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Santiago
@svpino

10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB

2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5

3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB

4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm

Santiago
@svpino

10 machine learning questions you should be able to answer.

I started experimenting with a questionnaire before interviewing candidates. Applicants submit a video or written answers to apply for the position.

Thread: Let's look at 10 of these questions.

The questionnaire solves a couple of problems:

1. Weeds out candidates that aren't ready for the job.

2. It significantly cuts down the time we spend talking synchronously.

One problem: It requires extra time from the candidate before the phone interview.

Here are some numbers on a job posting before using the questionnaire:

• 249 applicants
• 13 good applications

That's only 5%.

This sucks.

Numbers after using the questionnaire:

• 44 applicants
• 21 good applications

That's 47%.

This is much better.

I look at it this way:

Sooner or later, I'll ask these questions, and the candidate will have to answer them.

I'd rather give them space and the opportunity to think about their answers without any pressure.

Those who can't or don't want to answer won't apply. That's fine.

Santiago
@svpino

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.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

More from Machine learning

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)

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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Pratham Prasoon
@PrasoonPratham

Everything you need to know about the math for machine learning as a beginner.

🧵👇

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

For example👇

(2 / 17)

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

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(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

(4 / 17)

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

It is 100% compulsory.

(5 / 17)

Pratham Prasoon
@PrasoonPratham

Here's what you'll hear when you first get into machine learning.

- Neural networks
- Loss
- Weights
- Biases
- Epochs
- Neurons
- Optimizers
...

It can get very confusing really fast!

Here are some of the terms you should know about.
(I wish I had this before)

🧵👇

These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

$Fionna O'Leary, \U0001f56f\U0001f1ea\U0001f1fa$

Fionna O'Leary, 🕯🇪🇺...
@fascinatorfun

Thanks for this incredibly helpful analysis @dgurdasani1

Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published

SUMMARY: the Oxford/Astra trial examined dosing with gaps between 4-12 wks- although longer gaps appear to be limited mostly to younger participants. There was no difference reported in published data between these & efficacy from the 1st dose seems high for severe disease.
— Deepti Gurdasani (@dgurdasani1) December 31, 2020

Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?

Not to mention the efficacy data of the full first dose over that same period?

I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage

You attached photos of excerpts from a paper. Could you attach the link?

Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.

Discussions of 1 vs 2 doses suggest many are not aware of Pfizer's trials which evaluated 1 vs 2 dose immunogenicity, assessed multiple formulations (BNT162b1 BNT162b2 etc) & conducted dose-ranging in both young & old adults at the start. Saw "clear benefit of booster at day 21" pic.twitter.com/mpyxu9xFSF
— Dr Nicole E Basta (@IDEpiPhD) December 31, 2020

Here’s the link to the

I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.

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There this story was discussed.

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He was childless. He arranged for an elaborate yagya for a intelligent son to be born. He invited all the eminent rishi's to conduct this Yagya. Rishi Suhotra, Pael, Brahaspati Yagyavalka were assigned different tasks. Rishi Gobhil was asked to sing the Saamved.

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UN Watch
@UNWatch

The UN just voted to condemn Israel 9 times, and the rest of the world 0.

View the resolutions and voting results here:

The resolution titled "The occupied Syrian Golan," which condemns Israel for "repressive measures" against Syrian citizens in the Golan Heights, was adopted by a vote of 151 - 2 - 14.

Israel and the U.S. voted 'No' https://t.co/HoO7oz0dwr

The resolution titled "Israeli practices affecting the human rights of the Palestinian people..." was adopted by a vote of 153 - 6 - 9.

Australia, Canada, Israel, Marshall Islands, Micronesia, and the U.S. voted 'No' https://t.co/1Ntpi7Vqab

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Canada, Israel, Marshall Islands, Micronesia, and the U.S. voted 'No'
https://t.co/REumYgyRuF

The resolution titled "Applicability of the Geneva Convention... to the
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https://t.co/xDAeS9K1kW

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dan spaceman
@ohlaika

ugh i wanted to do a thread about the wars in Star Wars and how the two outlying wars (the clone wars in the prequels and the cold war/civil war in the sequels) are really weird but i don't feel like it any more.

my first argument is that the Clone Wars were a really complex morally gray moment in Star Wars canon in like, 5 different directions and I'd kind of like to know why Lucas did that.

especially with the direct line that's drawn in the OT with the allegory of WW2 and Vietnam with the rebels being the Vietnamese and the Empire being America. That...doesn't exist in the Clone Wars.

for starters: it's weird it's called the Clone Wars. I get why, because that's what Kenobi calls it in STAR WARS, but the clones are a small facet in the larger story of the war.

the clones basically only exist as a macguffin in AOTC that is...extremely confusing ultimately. Because if you accept that Sidious has this grand master plan to use this war to destabilize the Republic and form an Empire...it's way too complex.

Noah Smith
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That suggests there is still SOME grassroots support for democracy that transcends

4/Yet more good news: Michigan made it easier to vote. Again, by plebiscite, showing broad support for voting rights as an

5/OK, now the bad news.

We seem to have accepted electoral dysfunction in Florida as a permanent thing. The 2000 election has never really

Bad ballot design led to a lot of undervotes for Bill Nelson in Broward Co., possibly even enough to cost him his Senate seat. They do appear to be real undervotes, though, instead of tabulation errors. He doesn't really seem to have a path to victory. https://t.co/utUhY2KTaR
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antifa.drone
@antifa_drone

Fake chats claiming to be from the Irish African community are being disseminated by the far right in order to suggest that violence is imminent from #BLM supporters. This is straight out of the QAnon and Proud Boys playbook. Spread the word. Protest safely. #georgenkencho

There is co-ordination across the far right in Ireland now to stir both left and right in the hopes of creating a race war. Think critically! Fascists see the tragic killing of #georgenkencho, the grief of his community and pending investigation as a flashpoint for action.

Across Telegram, Twitter and Facebook disinformation is being peddled on the back of these tragic events. From false photographs to the tactics ofwhite supremacy, the far right is clumsily trying to drive hate against minority groups and figureheads.

Be aware, the images the #farright are sharing in the hopes of starting a race war, are not of the SPAR employee that was punched. They\u2019re older photos of a Everton fan. Be aware of the information you\u2019re sharing and that it may be false. Always #factcheck #GeorgeNkencho pic.twitter.com/4c9w4CMk5h
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Declan Ganley’s Burkean group and the incel wing of National Party (Gearóid Murphy, Mick O’Keeffe & Co.) as well as all the usuals are concerted in their efforts to demonstrate their white supremacist cred. The quiet parts are today being said out loud.

There is a concerted effort in far-right Telegram groups to try and incite violence on street by targetting people for racist online abuse following the killing of George Nkencho

This follows on and is part of a misinformation campaign to polarise communities at this time.
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The best thing you can do is challenge disinformation and report posts where engagement isn’t appropriate. Many of these are blatantly racist posts designed to drive recruitment to NP and other Nationalist groups. By all means protest but stay safe.