BC MACHINE LEARNING

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Pratham Prasoon
@PrasoonPratham 5 years, 6 months ago 945 views

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There are several skills needed for learning machine learning that no one talks about.

Here are some of them.
(from what I've learned over the past two years)
🧵👇

This thread aims to introduce to you some of the skills often ignored for learning machine learning but are actually very important.

These skills will enable you to learn concepts quickly and more efficiently in this field.

(2 / 9)

1⃣ Reading

Probably one of the most underrated skills on this list.

In machine learning, you HAVE to read a lot of articles, papers, documentation, and whatnot.

It is mostly due to the theoretical nature of this field that reading is an important skill to have.

(3 / 9)

2⃣ Strong fundamentals in programming

Machine learning is just a lot of programming mixed with math and data.

Having clear programming fundamentals is crucial for this field.

I highly suggest you learn about these if you are using Python for machine learning.👇

(4 / 9)

- Object oriented programming in Python :Classes, Objects, Methods
- Lists & List functions
- Dunder Methods
- List comprehension
- List slicing
- String formatting
- List, Dictionaries & Tuples
- *args,**kwargs

(5 / 9)

3⃣ Focus

This rule applies to pretty much every kind of development, but I find that very few people talk about it in machine learning.

There is a lot you can do in this field also means you can get lost pretty quickly if you do not focus.

(6 / 9)

Here are some of the types of machine learning:

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Semi-Supervised Learning
- Self-Supervised Learning

.... and the list goes on.

Even I don't know most of these, you must have focus at the start!

(7 / 9)

Before we wrap up this thread keep in mind that whatever you have read until now is what I've learnt from "my experience" into machine learning over the past 2 years.

(8 / 9)

Yours could be totally different and that is perfectly fine, all I wanted to provide is a general direction that could help you.

Good luck with your machine learning journey!🔥

(9 / 9)

More from Pratham Prasoon

Pratham Prasoon
@PrasoonPratham

This is how I got three job offers as a 16-year-old high school student.

( and why I rejected them )
🧵👇

June 2020.

I receive a DM from a college student in California. The message read something like this "Hey Pratham, I am one of the co-founders of XYZ Inc, a robotics company, we are looking to add enthusiastic people to our team, would you be interested?"

(2 / 11)

It was the first time I had ever been offered a job, so I said Yes! (I did reject it later)

There were a couple of interview rounds I had to go through. None of them included technical questions, which was interesting.

(3 / 11)

After the first interview, I saw the list of the roles I could apply for. I realized how little I knew, terms like "Ui/UX, ROS(Robot operating system)", at that time, I was overwhelmed.

(4 / 11)

I knew that this wasn't a job for me. I decided that I would reject this job role even if I cleared the second round, which I ended up doing.

(5 / 11)

Pratham
@PrasoonPratham

This is a beginner's guide on mining cryptocurrencies.

🧵 👇🏻

Mining 101

Typically, when you transfer money using a service like Paypal, they take a small cut for facilitating the exchange.

In cryptocurrencies, people like you and me act as Paypal and facilitate exchanges of cryptocurrency. We get a cut for this just like Paypal did.

In order to make these transactions happen, our computers need to do some calculations which requires a lot of computational power.

A GPU or a Graphics Processing Unit which is typically marketed for gaming workloads can be used to mine cryptocurrencies.

Why do you need a GPU?

Today, there are so many miners that the "difficulty" of mining cryptocurrencies has skyrocketed, which basically means it takes a lot of computational power to mine crypto which GPUs can provide and CPUs cannot.

(❗ This is an oversimplification)

If you are interested in the inner workings of how blockchain and cryptocurrency, then I highly suggest that you read this thread by @oliverjumpertz

What actually is a Blockchain?

Bitcoin is breaking record after record, but there must be more to the technology than just crypto, or not? Well, we can take a look at the underlying technology first to understand what it actually provides to us.

\U0001f9f5\u2b07\ufe0f
— Oliver Jumpertz (@oliverjumpertz) February 16, 2021

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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)

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.

Pratham Prasoon
@PrasoonPratham

Are you planning to learn 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)

More from Machine learning

Pratham Prasoon
@PrasoonPratham

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.

(2 / 11)

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. )

(3 / 11)

Here's the entire list:

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

Let's see what they do.

(4 / 11)

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...

(5 / 11

$Unknown \U0001f464$

Unknown 👤
@console_404

10 PYTHON 🐍 libraries for machine learning.

Retweets are appreciated.
[ Thread ]

1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN

2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo

3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx

4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4

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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

Santiago
@svpino

The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇

Jan Giacomelli
@jangiacomelli

🤔 Python generators

What are they? How to use them?

#Python

🧵Let's find out 👇

1️⃣ Python generators are lazy iterators delivering the next value when their .next() is called.

They are created by using the yield keyword

next() can be called explicitly or implicitly inside for loop

They can be finite or infinite

2️⃣ yield - where a value is sent back to the caller, but the function doesn’t exit afterward as with the return statement

The state of function is remembered.

For example, the number is incremented and sent back from yield at the consecutive call next()

3️⃣ Generator stores only the current state of the function - it generates next element on next() call and forgets the previous one -> it saves memory

For example, we don't need to store 1mio elements in memory to do something with each element

4️⃣ When you call a generator function generator object is returned - it's not executed yet

It executes only when next() is called

For example, that's why Exception is raised only on the next() call

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5. Kimmerer does a commendable job of weaving in her own experiences, her family stories+nods to some very important knowledge keepers who deserve to be celebrated for their work. But Braiding Sweetgrass does not tend to the citational politics Sara Ahmed & others teach us about

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So it's now October 10, 2018 and....Rod Rosenstein is STILL not fired.

He's STILL in charge of the Mueller investigation.

He's STILL refusing to hand over the McCabe memos.

He's STILL holding up the declassification of the #SpyGate documents & their release to the public.

I love a good cover story.......

The guy had a face-to-face with El Grande Trumpo himself on Air Force One just 2 days ago. Inside just about the most secure SCIF in the world.

And Trump came out of AF1 and gave ol' Rod a big thumbs up!

And so we're right back to 'that dirty rat Rosenstein!' 2 days later.

At this point it's clear some members of Congress are either in on this and helping the cover story or they haven't got a clue and are out in the cold.

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Choosing the Right JavaScript Framework

Thanks for joining me @pluralsight to talk about javascript frameworks. We had 150+ awesome questions, so I'll try to answer the ones I couldn't get to online right here in a tweet stream

Watch the video -->
https://t.co/G7zeiKhXme

Q. Do arrow functions really make dealing with JavaScript's "this" keywords easier?

A. Yes! When using arrow functions, the scope of "this" is effectively passed in from the outer scope. See here in this REPL

1/n

Q. Where does Polymer fit in? Is it considered a framework like the others?

A. @Polymer is an excellent way to create web components, which are ideal for creating sets of interactivity that can be shared in web apps.

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Q. Have we gone framework crazy?

A. At one time, quite possibly. A few years back we literally had framework overload. I feel it really has solidified into a few very strong choices.

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It is said that the one who bears the dust thrown from the hoof of Gaumata on his head, as if taking a bath in the water of the teerth sthalam and getting rid of all sins.

Ved and all other Hindu scriptures venerate the cow. The Vedic dictionary, Nighantu, gives amongst other synonyms of 'Gau' [ or cow] the words 'Aghnya', 'Ahi', and 'Aditi'.

Yaska the vedic decoder and commentator on Nighantu, defines these as-

Aghnya —— the one that ought not to be killed
Ahi —— the one that must not be slaughtered.
Aditi —— the one that ought not to be cut into pieces.

These three names of cow signify that this is an animal which should not to be put to tortures. These words appear frequently throughout the Veda in context of the cow.

But in due course the falsehood of killing cows spread far and wide and they became even more deep rooted when western scholars with their half baked knowledge of Sanskrit transliterated these interpretations of commentaries of Sayanacharya ,