BC MACHINE LEARNING
Saved by @Mollyycolllinss

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)
Dataframes allow you to work with data present in a spreadsheet.

Pandas help you make the data frame itself.

(6 / 19)
2. Numpy

Numpy allows you to manipulate the data. It replaces python lists and does the same things, like list slicing for example. However numpy lists are much faster to execute than the default python lists.

(7 / 19)
3. Matplotlib

Matplotlib is a library for plotting data into pie charts, bar charts, and whatever kinds of graphs you can imagine.

(8 / 19)
There is another library based on Matplotlib which is called Seaborn.

Seaborn is based on Matplotlib and allows you to visualize data with support for themes (as in color schemes like VS code themes) and more visualization options.

(9 / 19)
It is definitely not mandatory to learn Seaborn at the beginning of your machine learning journey.

Use it when you need to.

(10 / 19)
Why is it important to learn these libraries?

In machine learning, you will have to work with a lot of messy data! A lot!
These libraries are essential for you so that you can manipulate and analyze data.

(11 / 19)
You must always remember that data largely dictates how well your machine learning model will perform.

Do not ignore data analysis and cleaning.

It is even more important than neural network!

(12 / 19)
Now we are left with TensorFlow, PyTorch, and Scikit Learn.

Let's talk about them.

(13 / 19)
TensorFlow allows you to make neural networks, anything from computer vision to natural language processing, TensorFlow will help you out. Keras allows you to make machine learning models and is heavily integrated with Tensorflow v2 (the latest version of TensorFlow).

(14 / 19)
- PyTorch does the same things as TensorFlow in a different syntax.

- Both PyTorch and TensorFlow are equally amazing libraries.

(15 / 19)
SciKit Learn

Scikit learn does a lot of things, from regression to classification, you name it.
It is a great tool to have when working on machine learning.

(16 / 19)
Let's summarize.

Step 1: Learn Python well.
Step 2: Learn the basics of Numpy, Pandas, and matplotlib.
Step 3: Learn either PyTorch or TensorFlow or SciKit learn at the start.

(17 / 19)
Step 4: Do a project from Kaggle where you can use these tools. (Pro-tip, look at notebooks and solutions from other people, this will help you in understanding how experienced people tackle certain problems)

(18 / 19)
The best tutorials to learn these would be from the freecodecamp YouTube channel, simply search for `framework name` freecodecamp.

They have some of the best machine learning tutorials out there.

(19 / 19)

More from Pratham Prasoon

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Do you want to learn the maths for machine learning but don't know where to start?

This thread is for you.
🧵👇

The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.

I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.

Before diving into maths, I suggest first having solid programming skills in Python.

Read this thread for more


These are topics of math you'll have to focus on for machine learning👇

- Trigonometry & Algebra

These are the main pre-requisites for other topics on this list.

(There are other pre-requites but these are the most common)

- Linear Algebra

To manipulate and represent data.

- Calculus

To train and optimize your machine learning model, this is very important.
MACHINE LEARNING
Pratham
Pratham
@PrasoonPratham
A git cheat sheet for all your needs!

https://t.co/H7QdOrthwV
GIT
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Pratham Prasoon
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.
MACHINE LEARNING
Pratham
Pratham
@PrasoonPratham
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 🧵

1️⃣ Kaggle

My go to place for finding datasets and code snippets, also a great place for discussions.


2️⃣ Papers with code

A collection of the latest and greatest research papers and the best part, a github repository with the code is included!


3️⃣ Kdnuggets

Guides, tutorials and articles on everything AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning.


4️⃣ Towards Data Science

A blog about data science with accessible and well written articles.
CODING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learn to Python for machine learning this year?

Here's everything you need to get started.
🧵👇

In this thread we'll look at all the concepts in Python you need to know for machine learning along with free resources to help you out.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19

You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
MACHINE LEARNING , PYTHONML

More from Machine learning

Alejandro Piad Morffis
Alejandro Piad Morffis
@AlejandroPiad
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.
MACHINE LEARNING
Ram Bhupatiraju
Ram Bhupatiraju
@RamBhupatiraju
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
MACHINE LEARNING , ALL
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Cory Doctorow #BLM
Cory Doctorow #BLM
@doctorow
We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

1/


People who hold disfavored views struggle to find one another and mobilize. To find other people that feel the same way as you and make common cause with them to effect political change, you have to reveal your views and suffer social sanction.

2/

Search allows people who hold these views to find one another. If you have a deep feeling about your gender being nonbinary but don't know the words for it, you can search for communities of people who have those words, join them, and discover who you've been all along.

3/

This is why, in this moment, so many ideas are migrating from the fringe to the center: ideas about racial justice, gender identity, alternatives to market systems, etc. People have harbored these views all along, but have held back on expressing them.

4/

Being able to express yourself in private, among people who share your views, is a prelude to going public and putting your case to the wider world in hopes of effecting change.

5/
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Can you get a job in data science and machine learning without a college degree?

🧵👇

Short Answer: Yes.

Long Answer, keep reading 👇

(Advice from industry experts who I talked to.)

Companies are looking for people who add value.
In order to add value, you'll need skills. Simple as that.

Get the skills to provide value and you'll get the job.

In the age of the internet where everything is pretty much free, why do college degrees matter?

A college degree makes it easier to get the skills to get a job in machine learning or data science.

College degrees also help you make many useful connections and provides many opportunities in the form of internships and whatnot.

College degrees have their own place
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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.

(2 / 14)

"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.

(3 / 14)

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.

(4 / 14)

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.

(5 / 14)
MACHINE LEARNING

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You take a loan from the bank.

The bank asks you, “what if you don’t pay back?”

You say, “take my home and sell it”.

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That makes sense. The bank gives you a loan and in case you don’t pay, the bank will take over your house and sell it and recover its money.

That’s collateral.

Banks also lend money to big corporations. With them, banks don’t always have collateral.

So a loan where the bank has collateral is considered safer for the bank.

In the early 2000s, the banks in the USA were giving out loans to people for buying houses.

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