Machine Learning for the Web developer in 2021.

The beginner's guide.​

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

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"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

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Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

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In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

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The two most common misconceptions about getting started with machine learning are:

- You need PhD math
- You need a really expensive computer

Math is important but it is not for getting started with machine learning, it can come later on.

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You do not need any those of those to get started, here's what you really need:

- A computer or smartphone
- Knowing how to program decently well
- Hunger for learning

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Most web developers pretty much have all of these under their belt!
What you really need are some resources and guidance.

Let's start with the language you should use for machine learning

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The JavaScript machine learning ecosystem is quite mature enough yet, which is why I will suggest you to learn Python.

Not to mention that getting started with Python will be a piece of cake if you already know JavaScript.

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This course by FreeCodeCamp will help you get started with Python.

👉www.​youtube.​com/watch?v=rfscVS0vtbw

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It is highly recommended that you use Google colab (an online IDE) for your machine learning code. You'll get a free GPU and you will not have to download large libraries onto your computer, everything stays in the cloud.

👉colab.​research.​google.​com

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Kaggle is the best place to look for datasets and competitions which you can participate in to take your machine learning skills to the next level.

This thread will guide you on how you can get started with one such kaggle challenge

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https://t.co/yo2W7nWBj2
You've probably learnt a lot by now and you should be proud about it, however there is still lots to learn.

- Visualising data using matplotlib
- Activation functions
- Decision Trees
....

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More from Pratham Prasoon

More from Machine learning

This is a Twitter series on #FoundationsOfML.

❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.

This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵

Last time we talked about how Machine Learning works.

Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric


According to the nature of that experience, we can define different formulations, or flavors, of the learning process.

A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇

1️⃣ Supervised Learning

In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.

👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
Happy 2⃣0⃣2⃣1⃣ to all.🎇

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.
⬇️⬇️⬇️

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city
https://t.co/9aUK1Tclw4

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel
https://t.co/f1joTRaG55

✔️ @dollarsanddata
https://t.co/Mj1owkzRc8

✔️ @awealthofcs
https://t.co/y81KHfh8cn

✔️ @iancassel
https://t.co/KEMTBHa8Qk

✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s

✔️

Tech focused

✔️ @stratechery
https://t.co/VsNwRStY9C

✔️ @bgurley
https://t.co/NKXGtaB6HQ

✔️ @CBinsights
https://t.co/H77hNp2X5R

✔️ @benedictevans
https://t.co/nyOlasCY1o

✔️

Tech Deep dives

✔️ @StackInvesting
https://t.co/WQ1yBYzT2m

✔️ @hhhypergrowth
https://t.co/kcLKITRLz1

✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh

✔️ @SeifelCapital
https://t.co/CXXG5PY0xX

✔️ @borrowed_ideas
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

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First thread of the year because I have time during MCO. As requested, a thread on the gods and spirits of Malay folk religion. Some are indigenous, some are of Indian origin, some have Islamic


Before I begin, it might be worth explaining the Malay conception of the spirit world. At its deepest level, Malay religious belief is animist. All living beings and even certain objects are said to have a soul. Natural phenomena are either controlled by or personified as spirits

Although these beings had to be respected, not all of them were powerful enough to be considered gods. Offerings would be made to the spirits that had greater influence on human life. Spells and incantations would invoke their


Two known examples of such elemental spirits that had god-like status are Raja Angin (king of the wind) and Mambang Tali Arus (spirit of river currents). There were undoubtedly many more which have been lost to time

Contact with ancient India brought the influence of Hinduism and Buddhism to SEA. What we now call Hinduism similarly developed in India out of native animism and the more formal Vedic tradition. This can be seen in the multitude of sacred animals and location-specific Hindu gods