BC MACHINE LEARNING
Saved by @Mollyycolllinss

In this thread, I will tell you how to learn python for data science in 1 hour 👇 1/N

 It's not possible 2/N
It's not possible 3/N
It's not possible 4/N
It's not possible 5/N
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More from Machine learning

Pratham Prasoon
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)
MACHINE LEARNING , ALL , PYTHON STUDY
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
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
How to learn Python for machine learning. 🐍

The beginner's guide.
(based on my real life experience of mentoring 300+ students)

🧵👇

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

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 , PYTHON USEFUL
Madison Kanna
Madison Kanna
@Madisonkanna
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.
MACHINE LEARNING
Roger Grosse
Roger Grosse
@RogerGrosse
Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ


The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.
MACHINE LEARNING

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ashkan soltani
ashkan soltani
@ashk4n
BREAKING: @CommonsCMS @DamianCollins just released previously sealed #Six4Three @Facebook documents:

Some random interesting tidbits:

1) Zuck approves shutting down platform API access for Twitter's when Vine is released #competition


2) Facebook engineered ways to access user's call history w/o alerting users:

Team considered access to call history considered 'high PR risk' but 'growth team will charge ahead'. @Facebook created upgrade path to access data w/o subjecting users to Android permissions dialogue.


3) The above also confirms @kashhill and other's suspicion that call history was used to improve PYMK (People You May Know) suggestions and newsfeed rankings.

4) Docs also shed more light into @dseetharaman's story on @Facebook monitoring users' @Onavo VPN activity to determine what competitors to mimic or acquire in 2013.

https://t.co/PwiRIL3v9x
TECH
Naval
Naval
@naval
How to Get Rich (without getting lucky):

Seek wealth, not money or status. Wealth is having assets that earn while you sleep. Money is how we transfer time and wealth. Status is your place in the social hierarchy.

Understand that ethical wealth creation is possible. If you secretly despise wealth, it will elude you.

Ignore people playing status games. They gain status by attacking people playing wealth creation games.

You’re not going to get rich renting out your time. You must own equity - a piece of a business - to gain your financial freedom.
LIFE , STARTUPS , NAVAL1
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Aditya Todmal
Aditya Todmal
@AdityaTodmal
Master Thread of all my threads!

Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃

1. 7 FREE OPTION TRADING COURSES FOR


2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX


3. 12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4.


4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
1. Management
2. How to initiate
3. When to exit straddles
4. Examples
5. Videos on
OPTION SELLING , OPTIONSTRATEGIES , CATEGORY
Ankur Patel
Ankur Patel
@AnkurPatel59
FEW USEFUL CHARTINK SCANNERS :

This Scanner will be More than Enough for any Swing / Positional Trader to find Good Stocks.

Few Important Chart ink Scanners which can help you to find Setups and boost up your Trading Account : 🧵

Do Retweet If you If it Useful

1. Momentum Stocks

This Scanner can be helpful for Short term Swing Trades . you can easily find Stocks which are in Momentum.

Stocks which are up by 20 % in 5 Days
Stocks which are up by 30 % in 30 Days
Stocks which are up by 30 % in 30 Days

2. Volume Spike

We always Look for Price and Volume Confirmation for Breakout Trading. This scanner will help you to find the Stocks Which has given a move with Good Volume . You can keep this Stock in Radar and catch the Next move in them :

3 . Reversal Scan

Stocks which are Down by 20 % + in 5 days

When Market Corrects for sometime , You can find Stocks where selling is Exhausted and they can make a Good Reversal move .

4. Reversal Scan - Stock Down continues 5 days .

If you Know the Concept of 3-5 Days move . you might catch few stocks from this scanner. Always try and Pick stocks which are in strong momentum , you have more Chances of Getting Right in Reversal Trade.
TRADING SCREENERS , TRADING SCREENER , ALL
Paras Chopra
Paras Chopra
@paraschopra
1/ Here are my notes from @robertwrighter's book - Nonzero: the logic of human destiny. I enjoyed the book very much.

The basic premise of the book is that history has a direction which favors co-operation and non-zero sum games, and that causes an increase in complexity.


2/ Starting from the first replicating molecule which co-operated with an outer layer to form first proto-cell, evolutionary and cultural history is full of examples where two entities come together to survive and progress a lot more than they would have done individually.

3/ This co-operative entity fares much better than two individual entities because of specialization. If two entities are in the same boat - that they win together or lose together - then trust is implicit.

4/ In a non-zero sum game, trust causes entities to focus on what they do best.

For example, eukaryotic cells - the ones animals and plants have - emerged when two proto-cells merged, and one took the role of energy generator (mitochondria), the other specialized in protection.

5/ Some scientists believe that even nucleus in a cell is a result of an early cell merging with another cell.

So M&A is not a recent phenomena, entities have been merging because of common interests ever since life started.
CULTURE