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
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
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Everything you need to know about the math for machine learning as a beginner.

🧵👇

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

For example👇

(2 / 17)

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 / 17)

If you want to learn python, these courses are freecodecamp could be of help to you.

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

(4 / 17)


You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 17)
MACHINE LEARNING
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Pratham
Pratham
@PrasoonPratham
A list of my favourite tutorials for learning Python as a beginner.

🧵 👇🏻

All the tutorials below include the basics like installation, variables etc.

I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.

Before going through these tutorials I would highly suggest you to go through this thread if you are a complete


The Full Python Course from learn with Python with Rune

key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included

Duration: 8

The Python Beginner's course on FreeCodeCamp's YouTube Channel.

Key highlights

- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.

Duration: 4
ALL
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
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

More from Machine learning

Santiago
Santiago
@svpino
20 machine learning questions that will make you think.

(Cool questions. Not the regular, introductory stuff that you find everywhere.)

🧵👇

1. Why is it important to introduce non-linearities in a neural network?

2. What are the differences between a multi-class classification problem and a multi-label classification problem?

3. Why does the use of Dropout work as a regularizer?

👇

4. Why you shouldn't use a softmax output activation function in a multi-label classification problem when using a one-hot-encoded target?

5. Does the use of Dropout in your model slow down or speed up the training process? Why?

👇

6. In a Linear or Logistic Regression problem, do all Gradient Descent algorithms lead to the same model, provided you let them run long enough?

7. Explain the difference between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.

👇

8. What are the advantages of Convolution Neural Networks (CNN) over a fully connected network for image classification?

9. What are the advantages of Recurrent Neural Networks (RNN) over a fully connected network when working with text data?

👇
ALL , MACHINE LEARNING
Kieran Snyder
Kieran Snyder
@KieranSnyder
1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.
MACHINE LEARNING
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING
Cassie Kozyrkov
Cassie Kozyrkov
@quaesita
1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.

3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*

4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.

5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do:
MACHINE LEARNING
Santiago
Santiago
@svpino
11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇
MACHINE LEARNING

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Just note, I am not bitter or salty in any way at all, the last 2 years have been an amazing ride - travelled the world, been wealthy, been poor.

2/ Dec '16, my advertising agency folded, I had a little bit of money left and I put $32k into Bitcoin and Ether. As it started to go up I diversified into everything, Monero, Dash, this that, any crap - even Ripplecoin. Everything just kept going up.

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Yes - all of the above as a one-man army :)
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Priyanka (Astrology Guidance)
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1

From today, we will memorize the names of 27 Nakshatras in Vedic Jyotish to never forget in life.

I will write 4 names. Repeat them in SAME sequence twice in morning, noon, evening. Each day, revise new names + recall all previously learnt names.

Pls RT if you are in.

2

Today's Nakshatras are:-

1. Ashwini - अश्विनी

2. Bharani - भरणी

3. Krittika - कृत्तिका

4. Rohini - रोहिणी

Ashwini - अश्विनी is the FIRST Nakshatra.

Repeat these names TWICE now, tomorrow morning, noon and evening. Like this tweet if you have revised 8 times as told.

3

Today's Nakshatras are:-

5. Mrigashira - मृगशिरा

6. Ardra - आर्द्रा

7. Punarvasu - पुनर्वसु

8. Pushya - पुष्य

First recall previously learnt Nakshatras twice. Then recite these TWICE now, tomorrow morning, noon & evening in SAME order. Like this tweet only after doing so.

4

Today's Nakshatras are:-

9. Ashlesha - अश्लेषा

10. Magha - मघा

11. Purvaphalguni - पूर्वाफाल्गुनी

12. Uttaraphalguni - उत्तराफाल्गुनी

Purva means that comes before (P se Purva, P se pehele), and Uttara comes later.

Read next tweet too.

5

Purva, Uttara prefixes come in other Nakshatras too. Purva= pehele wala. Remember.

First recall previously learnt 8 Nakshatras twice. Then recite those in Tweet #4 TWICE now, tomorrow morning, noon & evening in SAME order. Like this tweet if you have read Tweets #4 & 5, both.
VEDICNAKSHATRA
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Meet Shah
Meet Shah
@ms89_meet
List of companies with more than 50% Market Share:

1) Maruti Suzuki (passenger cars)
2) APL Apollo (structural & pre galvanized tubes)
3) CDSL (investors accounts)
4) Interglobe Aviation (air traffic passengers)
5) GMM Pfaudler (glass lined equipment)

6) Asian Paints (decorative paints)
7) Colgate (oral care)
8) Symphony (coolers)
9) PGHH (female care & vaporub)
10) La Opala Rg (opalware)
11) HLE Glasscoat (filtration & drying equipment)
12) Delta Corp (online poker games)
13) Bajaj Auto (3W segment)
14) Vinati Organics (IBB)

15) OCCL (insoluble sulphur)
16) LMW (textile machinery)
17) Bajaj Consumer (almond hair oil)
18) Indiamart Intermesh (online B2B Classified space)
19) Vst Tillers (power tillers)
20) Sanghvi Movers (overall domestic crane hiring market)
21) Emami (antiseptic & male grooming)

List of companies with more than 60% Market Share:

1) Concor (domestic container cargo transport)
2) Exide (lead batteries)
3) Naukri (Indian job market space)
4) Praj (ethanol plant installing)
5) ACE (mobile & tower cranes)
6) Pidilite (adhesives)
7) Jamna Auto (leaf spring)

8) CAMS (RTA within mutual fund industry)
9) Time Technoplast (polymer based industrial packaging)
MONOPOLIES , STOCKS , EQUITIES
Arif Hasan\U0001f1ee\U0001f1f3
Arif Hasan🇮🇳...
@arifhasan18
5 star RSI Strategy by @VishalMalkan

Unconventional approach of 60-40 RSI and range shift concept.

According to him,
whenever the market is bearish,
it won’t be able to cross 60,
and whenever in a bullish trend,
it won’t be able to cross 40.

Like & Retweet for Maximum Reach.

So, instead of the 70-30 Relative Strength Index,
we will use the 60-40 range for the RSI indicator for this strategy.

When the
Monthly and Weekly RSI>60,
& when the RSI takes support at 40 in the daily chart,
it may bounce back and most probably it will not go below 40.

If a bullish candle is formed at the 40 RSI level,
the prices may bounce back, and one can enter the stock.

The stoploss can be placed at the low of that bullish candlestick,
and the target can be the 60 RSI level.

When the
Monthly and Weekly RSI<40,
& when the RSI takes resistance at 60 in the daily chart,
it may bounce back and most probably it will not go above 60

If a bearish candle is formed at the 60 RSI level,
the prices may bounce back from there, & 1 can enter the stock 4 shorting

The stoploss can be placed at the high of that bearish candlestick, and the target can be the 40 RSI levels.

#banknifty

This range shift concept can be applied in any timeframe,
whether intraday, daily,weekly, etc.
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Jaya_Upadhyaya
Jaya_Upadhyaya
@Jayalko1
DODDA GANAPATHI, BENGALURU
#karnatakatemples

Dodda in Kannada means big. Sri Ganesha here is 18 ft tall in height and 16 ft wide. Dodda Ganesha is also known as Shakthi Ganapathi and Satya Ganapathi.

It is said that Kempe Gowda, the founder of Bengaluru, saw a rock here.


The rock had engravings of Ganesha. His sculptors cut the Ganesha murti from that single rock. Later, a shrine was built around the Ganesh murti.

It is believed that Dodda Ganapathi is the protector of Bengaluru. All auspicious events start by praying to Him.


Benne Alankara is the most famous festival here when about 100 kg of butter is coated over Ganapathi every four years and the rich prasad is offered to the worshippers.

Ganesha is also covered in cream colour and is decorated with golden laces and buttons
@GampaSD


The Dodda Ganapathi murti is believed to be still growing in size, especially towards right.

The weavers from old Bengaluru tie a coconut to their looms before starting work. When the work is completed, they break 100 coconuts here along with the one tied on the loom.
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