28 Mistakes That Destroy The Financial Life Of people ---

A Mega Thread -

👇

Inspiration- @FI_InvestIndia @warikoo

Grab Your Cup ☕ of Coffee and let's Get Started --

1. Buying insurance policies for investment purpose:

Have you invested your money in insurance plan to get a return in future?

Big mistake! Out of 100 people I have spoken, 95 have made this mistake..

Very few people understand the difference between term plan, endowment plan, etc.

2. Not able to crack the credit card mystery:

Are you paying the minimum amount due on your credit card payment?

If yes, you are trapped in credit card mystery.

On the other side, very few people really enjoy the benefits like free lounge access, buy one get one movie ticket, etc.

A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

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"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

🧵Introducing a new MOOC, by the creators and contributors of @scikit_learn:

Machine learning in Python with scikit-learn
• Cost: free
• 8 weeks / (35 hours total)

#datascience #MachineLearning #python #ScikitLearnMooc
Enroll now:
https://t.co/P2ZJH1uETw

Course Prerequisites
Strong technical background not required.

** Required **
• basic knowledge of Python programming : defining variables, writing functions, importing modules
** Recommended **
• some prior experience with the NumPy, pandas and Matplotlib libraries

How it works
•videos, slides, notebooks to run online
• programming exercises at the end of every modules.
• quizzes after the lessons
• A certificate will be issued upon completion (share it on LI, etc)

Course syllabus; 7 modules
🟠Introduction
1⃣The Predictive Modeling Pipeline
2⃣Selecting the best model
3⃣Hyperparameters tuning
4⃣Linear Models
5⃣Decision tree models
6⃣Ensemble of models
7⃣Evaluating model performance

Everyone tells you how important math is in machine learning.

But I believe that if you want to learn machine learning today, there are things more important than math which very few people will tell you about.

Here are 5 of them.

🧵 👇🏻

The field of machine learning has been very academic which is why there is so much emphasis on learning math for it.

But, today with the frameworks that we have, not knowing how to multiply matrices is far less of an issue than knowing how to use the tools in this list 👇🏻

Yes, I know; you have R, Julia, and whatnot for machine learning but as of today, Python is the industry standard.

Get your data structures and algorithms right in Python because you will need them, here are some concepts worth learning 👇🏻

Let's say you're building the next big GPT-3 powered app that's going to take over the world.

You add a new feature to your app but it breaks the entire thing, what do you do now?

Learn Git, the ultimate version control tool.

I use this cheat sheet for all my git needs 👇🏻

You will be constantly be working on multiple packages which specific python version requirements 🐍

Virtual environments like Anaconda or Poetry will make this process much easier for you.

Who likes to see broken dependencies while running code?

There are a million machine learning tutorials and courses out there which makes it really hard to choose the right one for you.

Here are some that I really like that you might find useful.
🧵 👇🏻

Before we begin, here's some unsolicited advice.

It is not about how many courses you take that matters but the problems that you solve with your knowledge.

You can take any combination of courses and tutorials from this list that you think will be useful.

Python crash course by Traversy Media (YouTube)

• Introduces you to a lot of basic concepts in Python
• Only 1.5 hours long

Intermediate Python course (YouTube)

• Helps you understand some of the more difficult concepts in Python

The neural networks series by 3blue1brown (YouTube)

• Visually explains neural networks
• Short and concise

ECONOMIC DESTRUCTION OF INDIA UNDER THE BRITISH RULE

A 🧵

In 1800 CE, India amounted to a whopping 20% of the total world manufacturing output!

But more importantly the balance of trade went into the Indian economy in the form of goods in

1/8

exchange or bullion and helped Indian people’s standard of living. It had second most powerful economy, the wages and productivity throughout all the sections of society were high. It had a simple, inexpensive education system suited for its needs.

2/8

But, by the year 1900 , India had become synonymous with poverty, its exports accounted for mere 1.7% of the total world trade , and more than 30 million (more than 10 % of India's population) died of starvation in the British rule.

3/8

After 1818 when British became the major power in India Indian manufacturing output rapidly declined while British
increased.

See the attached image of table given by Paul Kennedy in his book ‘The Rise and Fall of Great Powers’

4/8

During the same period per capita levels of industrialization (relative to UK in 1900 =
100 scale) is given by Paul Kennedy in the following table.

5/8

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.

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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.

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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

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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)