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.
Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method
https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?

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शमशान में जब महर्षि दधीचि के मांसपिंड का दाह संस्कार हो रहा था तो उनकी पत्नी अपने पति का वियोग सहन नहीं कर पायी और पास में ही स्थित विशाल पीपल वृक्ष के कोटर में अपने तीन वर्ष के बालक को रख के स्वयं चिता पे बैठ कर सती हो गयी ।इस प्रकार ऋषी दधीचि और उनकी पत्नी की मुक्ति हो गयी।


परन्तु पीपल के कोटर में रखा बालक भूख प्यास से तड़पने लगा। जब कुछ नहीं मिला तो वो कोटर में पड़े पीपल के गोदों (फल) को खाकर बड़ा होने लगा। कालान्तर में पीपल के फलों और पत्तों को खाकर बालक का जीवन किसी प्रकार सुरक्षित रहा।

एक दिन देवर्षि नारद वहां से गुजर रहे थे ।नारद ने पीपल के कोटर में बालक को देख कर उसका परिचय मांगा -
नारद बोले - बालक तुम कौन हो?
बालक - यही तो मैं भी जानना चहता हूँ ।
नारद - तुम्हारे जनक कौन हैं?
बालक - यही तो मैं भी जानना चाहता हूँ ।

तब नारद ने आँखें बन्द कर ध्यान लगाया ।


तत्पश्चात आश्चर्यचकित हो कर बालक को बताया कि 'हे बालक! तुम महान दानी महर्षि दधीचि के पुत्र हो । तुम्हारे पिता की अस्थियों का वज्रास्त्र बनाकर ही देवताओं ने असुरों पर विजय पायी थी।तुम्हारे पिता की मृत्यु मात्र 31 वर्ष की वय में ही हो गयी थी'।

बालक - मेरे पिता की अकाल मृत्यु का क्या कारण था?
नारद - तुम्हारे पिता पर शनिदेव की महादशा थी।
बालक - मेरे उपर आयी विपत्ति का कारण क्या था?
नारद - शनिदेव की महादशा।
इतना बताकर देवर्षि नारद ने पीपल के पत्तों और गोदों को खाकर बड़े हुए उस बालक का नाम पिप्पलाद रखा और उसे दीक्षित किया।