Here's everything you need to know about the math for machine learning.

(+ free resources )
🧵👇

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

For example👇

(2 / 18)
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 / 18)
If you want to learn those concepts for python, these courses are freecodecamp could be of help to you.

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

(4 / 18)
👉You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 18)
👉Another question that I get asked quite often is when should you even start learning the math for machine learning?

(6 / 18)
👉Math for machine learning should come after you have worked on a project or two, doesn't have to a complex one at all, but one that gives you a taste of how machine learning works.

(7 / 18)
👉Here's how I do it, I look at the math when I have a need for it.

For instance I was recently competing in a kaggle machine learning challenge.

(8 / 18)
I was brainstorming about which activation function to use in a part of my neural net, I looked up the math behind each activation function and this helped me to choose the right one.

(9 / 18)
The topics of math you'll have to focus on for machine learning
- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability

Now here are the resources and a brief description about them.

(10 / 18)
Neural Networks
> A series of videos that go over how neural networks work with approach visual, must watch.

🔗youtube. com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

(11 / 18)
Seeing Theory
> This website helps you learn statistics and probability in an intuitive way.

🔗seeing-theory. brown. edu/basic-probability/index.html

(12 / 18)
Gilbert Strang's lectures on Linear Algebra (MIT)

> This is 15 years old but still 100% relevant today!
Despite the fact these lectures are made for freshman college students at MIT,I found it very easy to follow👌

🔗youtube. com/playlist?list=PL49CF3715CB9EF31D

(13 / 18)
Essence of Linear Algebra
> A beautiful playlist of videos which teach you linear algebra through visualisations in an easy to digest manner

🔗youtube. com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

(14 / 18)
Khan Academy
>The resource you must refer to when you forget something or want to revise a topic super quick

🔗khanacademy. org/math

(15 / 18)
Essence of calculus
> A beautiful series on calculus, makes everything seem super simple.

🔗youtube. com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x

(16 / 18)
The math for Machine learning e-book

> This book is for someone who knows quite a decent amount of high school math like trignometry, calculus, I suggest reading this after having the fundamentals down on khan academy.

mml-book. github .io

(17 / 18)
If you found this thread helpful then don't forget to follow me, it takes a ton of effort to write these threads and your support keeps me going. 🔥🙏

Good luck in your machine learning journey!

(18 / 18 🎉)

More from Pratham Prasoon

More from Machine learning

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
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


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.


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.
With hard work and determination, anyone can learn to code.

Here’s a list of my favorites resources if you’re learning to code in 2021.

👇

1. freeCodeCamp.

I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things

2.
https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:

1) You can see the most upvoted solutions so you can read really good code

2) You can ask questions in the discussion section if you're stuck, and people often answer. Free

3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE

On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.

4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.

Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.

You May Also Like

क्या आप जानते हैं कि क्या है, पितृ पक्ष में कौवे को खाना देने के पीछे का वैज्ञानिक कारण!

श्राद्ध पक्ष में कौओं का बड़ा ही महत्व है। कहते है कौआ यम का प्रतीक है, यदि आपके हाथों दिया गया भोजन ग्रहण कर ले, तो ऐसा माना जाता है कि पितरों की कृपा आपके ऊपर है और वे आपसे ख़ुश है।


कुछ लोग कहते हैं की व्यक्ति मरकर सबसे पहले कौवे के रूप में जन्म लेता है और उसे खाना खिलाने से वह भोजन पितरों को मिलता है

शायद हम सबने अपने घर के किसी बड़े बुज़ुर्ग, किसी पंडित या ज्योतिषाचार्य से ये सुना होगा। वे अनगिनत किस्से सुनाएंगे, कहेंगे बड़े बुज़ुर्ग कह गए इसीलिए ऐसा करना

शायद ही हमें कोई इसके पीछे का वैज्ञानिक कारण बता सके।

हमारे ऋषि मुनि और पौराणिक काल में रहने वाले लोग मुर्ख नहीं थे! कभी सोचियेगा कौवों को पितृ पक्ष में खिलाई खीर हमारे पूर्वजों तक कैसे पहुंचेगी?

हमारे ऋषि मुनि विद्वान थे, वे जो बात करते या कहते थे उसके पीछे कोई न कोई वैज्ञानिक कारण छुपा होता था।

एक बहुत रोचक तथ्य है पितृ पक्ष, भादो( भाद्रपद) प्रकृति और काक के बीच।

एक बात जो कह सकते कि हम सब ने स्वतः उग आये पीपल या बरगद का पेड़/ पौधा किसी न किसी दीवार, पुरानी

इमारत, पर्वत या अट्टालिकाओं पर ज़रूर देखा होगा। देखा है न?

ज़रा सोचिये पीपल या बरगद की बीज कैसे पहुंचे होंगे वहाँ तक? इनके बीज इतने हल्के भी नहीं होते के हवा उन्हें उड़ाके ले जा सके।