I remember how hard it was to find resources for machine learning a couple of years ago.

Now we have so many of them, its impossible for you to not find what you're looking for.

Here are some of them 🧵

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.

These are the tools you will need for machine learning in Python.

🧵👇

Anaconda

When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python.

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Now imagine downloading a new version of python and then installing it for every framework you want to work with 😬.

Meet Anaconda which allows you to run several versions of python. It comes pre-installed with several data science and machine learning frameworks.

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Pip-env is also a way of maintaining several versions of Python and comes pre installed with Python.
You can use pip env or Anaconda, whichever works for you.

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Jupyter Notebooks

Jupyter notebooks is an IDE just like VS code or Sublime. The special thing about jupyter is that you can parts of code in mini code editors called cells. This is great for prototyping and testing code.

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A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
🧵👇

For this exercise, you'll need :
- A computer/phone
- An internet connection
- Very basic Python knowledge
- Willingness to learn

What you do not need:
- Complex math
- An expensive computer
- A PhD

Math will come later on in your machine learning journey :)

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Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

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We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

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Here's the code👇

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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.

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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. )

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Here's the entire list:

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

Let's see what they do.

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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...

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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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Practice and improve your HTML, CSS and JavaScript skills by building these 50 projects.

A thread 🧵

1. Expanding Cards
2. Progress Steps
3. Rotating Navigation
4. Hidden Search
5. Blurry Loading
6. Scroll Animation
7. Split Landing Page
8. Form Wave Animation
9. Sound Board
10. Dad Jokes App

11. Event KeyCodes
12. FAQ Collapse
13. Random Choice Picker
14. Animated Navigation
15. Incrementing Counter
16. Drink Water
17. Movie App
18. Background Slider
19. Theme Clock
20. Button Ripple Effect

21. Drag N Drop
22. Drawing App
23. Kinetic Loader
24. Content Placeholder
25. Sticky Navigation
26. Double Vertical Slider
27. Toast Notification
28. Github Profiles
29. Double Click Heart
30. Auto Text Effect

31. Password Generator
32. Good Cheap Fast
33. Notes App
34. Animated Countdown
35. Image Carousel
36. Hoverboard
37. Pokedex
38. Mobile Tab Navigation
39. Password Strength Background
40. 3D Boxes Background

Six Blogs/Websites from which you can learn and stay updated about Python 🐍

Thread 🧵👇

1⃣ @realpython

The best site for Python. It helped me a lot personally in the learning process.

2⃣ Python Blogs

A dedicated platform for learners and professionals for

3⃣ @PythonPool

Python Pool is a platform where you can learn and become an expert in every aspect of Python programming language as well as in AI, ML, and Data

4⃣ Microsoft Dev Blog - Python

Read the latest updates about all things Python at

If you're learning or planning to learn Web Development in 2021 then this thread is for you

🧵👇🏻

WHERE TO START? Front-end or Backend?

- Well this may be a debatable question but I would suggest you to start with front-end development

- It depends entirely on the individual but in my opinion, starting with front-end development has its advantages.

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WHY TO START WITH FRONT-END DEVELOPMENT?

- More visual content gives you an aesthetic vibe

- You can share your creations

- Easier to get feedback as you can show your webpages to any non technical person as well

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FUNDAMENTALS ARE IMPORTANT

- Don't rush! Spend some time on HTML, CSS and JavaScript. You can do pretty amazing things using these three languages

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- Make sure to learn and build some web pages using HTML and CSS before jumping onto JavaScript

- JavaScript is the backbone of web development, Don't rush to start with React/Angular/vue or any other front-end framework or library

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You are looking to get into Machine Learning? You most certainly can
Because I believe that if an above-average student like me was able to do it, you all certainly can as well

Here's how I went from knowing nothing about programming to someone working in Data Science👇

The path that I took wasn't the most optimal way to get a good grip on Machine Learning because...

when I started out, I knew nobody that worked or had knowledge of Data Science which made me try all sorts of different things that were not actually necessary.

I studied C programming as my first language during my freshman year in college. And before the start of my second year, I started learning python just because I knew C is not the way to go.
I learned it out of curiosity and I had no idea about Machine Learning at this point.

I did not learn python by courses but by books. I'll link all the resources at the end.
The approach I took was just to make the same kind of programs I made in C but just replacing the syntax with that of python and practised those.
Already knowing a language made it easier.

For Machine Learning, the first thing that I did was to join Andrew NG's course which really hit it off for me
I didn't have to have any idea about Machine Learning for the course. I completed that in almost a month and it gave me a good intuition of things and the flow of ML.