🦥 Machine Learning For Dummies

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Hello friends!

Welcome to this week’s Sloth Bytes.

I hope you had an amazing 2 weeks, sorry missing last week!

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Machine Learning For Dummies

Ever wanted to make computers learn from data? Create models that predict things? Or create the next ChatGPT?

Let me show you how Machine Learning works in simple terms.

Why Machine Learning?

Because it's powerful, it's everywhere, and it can solve problems that traditional programming can't.

Plus, who doesn't want to teach computers to think?

But Isn't It Super Complex?

It can because it’s more math heavy, but once you understand the math, you can learn anything in machine learning.

Let's break it down into pieces you can understand.

Types of Machine Learning

Think of these as different ways computers can learn:

1. Supervised Learning: Like learning with a teacher

   • You give it examples

   • You tell it the right answers

   • It learns patterns Perfect for: Spam detection, price prediction

2. Unsupervised Learning Like finding patterns in chaos

   • No right answers

   • Just data

   • Find hidden patterns Perfect for: Customer groups, anomaly detection

3. Reinforcement Learning Like teaching a dog tricks

   • Try things out

   • Get rewards or penalties

   • Learn from experience Perfect for: Game AI, robotics

What about those fancy neural network things?

Well those are usually used for unsupervised learning and reinforcement learning, but neural networks are a VERY COMPLEX topic that I may cover in the future.

For now just think of a neural network as an artificial brain we create to do certain actions.

How do I get started?

You'll need:

• Python (the most popular language for machine learning)

• Math (mainly calculus and linear algebra… yeah I told you, it’s more math heavy)

• Curiosity (lots of it)

Essential Programming Tools

These are your best friends:

• Python Libraries

  • NumPy (for numbers)

  • Pandas (for data)

  • Scikit-learn (for ML)

  • TensorFlow/PyTorch (for deep learning)

Start Small

Don't try to build ChatGPT on day one.

Begin with supervised learning with Linear regression and Logistic Regression

Free Resources

Tons of places to learn:

• Kaggle (datasets + tutorials)

• Google Colab (free GPUs!)

• YouTube tutorials

• Online courses

Remember

• Start simple

• Practice lots

• Learn from errors

• Keep experimenting

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Thank you to everyone who submitted 😃 

mc-milo, JamesHarryT, nhillemann, trgr-boi, Yoshlix, ravener, RelyingEarth87, agentNinjaK, vinny635,levi-manoel, amirsamgoharpay, tobiaoy, taypham88, anshurajpro23, and GabrielDornelas.

Amateur Hour

Write a function that takes time t1 and time t2 and returns the number of hours passed between the two times.

Examples

hoursPassed("3:00 AM", "9:00 AM")
output = "6 hours"

hoursPassed("2:00 PM", "4:00 PM")
output = "2 hours"

hoursPassed("1:00 AM", "3:00 PM")
output = "14 hours"

hoursPassed("4:00 PM", "4:00 PM")
output = "no time passed"

Notes

• Time t1 will always be the starting time and t2, the ending time.

• Return the string "no time passed" if t1 is equal to t2.

How To Submit Answers

Reply with

• A link to your solution (github, twitter, personal blog, portfolio, replit, etc)

• or if you’re on the web version leave a comment!

• If you want to be mentioned here, I’d prefer if you sent a GitHub link or Replit!

Docker video is out!

Check it out!

Working on 2 videos!

First video to come out will probably go over the whole DeepSeek situation and how to use it yourself, and the other one will be how to make money from programming!

That’s all from me!

Have a great week, be safe, make good choices, and have fun coding.

If I made a mistake or you have any questions, feel free to comment below or reply to the email!

See you all next week.