11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

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

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1⃣ Start with Python 🐍

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

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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.)

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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.

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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.

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An introduction to one of the the most basic structures used in machine learning: a tensor.

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Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

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The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

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The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

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What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X → y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...

A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

• Class A: 90% accuracy
• Class B: 80% accuracy
• Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ↓

Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.

How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

• Class A: 95% accuracy
• Class B: 100% accuracy
• Class C: 71% accuracy

Do you see what just happened?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you 😉.

I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.

The absolute best 15 scanners which experts are using.

Got these scanners from the following accounts:

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

Share for the benefit of everyone.

Listing all scanners from @Pathik_Trader Sir first.

1. Open

2. 80-20 Reversal Setup


3. Fake

4. Trapping Inside Bar (Positional Setup)

🚔 HUGE #Tesla SERVICE PROBLEMS THREAD🚔

You might remember my 3 prior threads on how
TESLA. TREATS. ITS. CUSTOMERS. LIKE. TRASH.

Here's the 4th installment.
@tomkeinan @jonsey_lisa @jwmooc @tchanpoker @bryanthediamond @EconomicManBlog
$TSLA $TSLAQ 1/22

Welcome to Greta's 4th edition of #Tesla customers getting used and abused. Because that's the Elon Musk way.
@coffeebreakls @KocgibiHarun @omaragha @JayGhahari
$TSLA $TSLAQ 2/22

"The best service is no service." - Elon Musk a.k.a. #SpaceKaren
@briansaziz @FarSha998899 @thinktankfour @Billoday1
#Tesla $TSLA $TSLAQ 3/22

These #Tesla customers represent just the very tip of the iceberg. They are ones who have Twitter, have decided to complain about their #TeslaServiceIssues publicly, and were found by yours truly.
@yanismydj @hajelatweets @AlanHeldman @francislongo5 $TSLA $TSLAQ 4/22

Dear #Tesla customers: In California, if your vehicle has been out of service for >30 days total, it's a lemon. You can get punitive damages and attorney's fees. Don't sit around and take Elon's abuse. @13arm13arm @cecimar12 @richardson_skip @shootinglee $TSLA $TSLAQ 5/22

Setting up Git/GitHub to work with R/RStudio can be challenging.

Here's a thread to walk you through the process step by step.

This thread also lives in blog post form. #rstats

Preamble: the best overall resource, and one I rely on extensively, is Happy Git with R by @JennyBryan and @jimhester_.

If you have additional questions about Git/GitHub + R/RStudio, this should be your first

The first step to set things up is to install Git.

Chapter 6 of Happy Git with R lays out the process for Mac, Windows, and Linux users.

I’m on a Mac so Git came pre-installed on my computer. I was able to verify that I had Git installed using the terminal in RStudio.

The next step is to configure Git.

This is covered in Chapter 7 of Happy Git with R , though I show what I believe to be a slightly easier process using the edit_git_config() function from the {usethis} package.

Last weekend, I made a threat on the ongoing #SocialMedia shutdown in Uganda and some implications on the future of #internet & Uganda's #digital agenda.

The thread attracted a lot of engagement and questions. Tonight, I will share some final thoughts on the subject. (1)

Conceptually, social media sites are the largest on-boarding platforms for internet users in Uganda. Notably, Facebook, WhatsApp, Twitter, Instagram & Snapchat attract the highest internet traffic- which drives demand for other platform services e.g Google and Zoom recently.

So- when we talk about an "open internet", we mean the FULL resources of the internet. Any encumberance on selected applications has ripple effects across the entire network thus jeorpadizing standardization of internet/data transmission which is essential for innovation & growth

Furthermore, shutdowns hinder network interoperability (technical ability to plug/link one digital product or service into another product or service). Interoperability is the heart of the "Internet of things" eg business, devices, networks, services, e-govt, integration etc #IoT

Because the internet is a global resource, shutdowns enhance a digital divide (the gap between demographics and regions that have access to modern ICTs, and those that don't or have restricted access). Ironic that 3rd world countries have the highest number of shutdowns globally.