Santiago Profile picture
22 Dec, 10 tweets, 2 min read
The 8-step quick-start guide to learn Machine Learning.

πŸ§΅πŸ‘‡
1⃣ Start with Python 🐍

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

πŸ‘‡
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.)

πŸ‘‡
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.

πŸ‘‡
4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem β€”especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

πŸ‘‡
5⃣ Focus on the analysis and not the code

In the beginning, spend your time and energy analyzing the problem and its solution.

Code is not important at this stage. Code can come later.

πŸ‘‡
6⃣ Start incorporating new algorithms

As you work through problems, start incorporating new algorithms into your toolset.

Here are a few great options to start:

1. Decision Trees
2. Linear regression
3. Logistic regression
4. Neural Networks
5. KNN

πŸ‘‡
7⃣ Get familiar with a general process to approach problems

Here is a good start:

1. Define the problem
2. Prepare the data
3. Spot-ccheck algorithms
4. Improve the results
5. Present the results

πŸ‘‡
8⃣ Pick a new problem and repeat

It shouldn't be surprising that the best way to improve is to practice and solve new problems.

If you don't have access to real-life problems, get familiar with Kaggle: everything you need will be there.
In the next coming weeks, I'll be posting a whole series of machine learning advice for people wanting to start.

Stay tuned!

β€’ β€’ β€’

Missing some Tweet in this thread? You can try to force a refresh
γ€€

Keep Current with Santiago

Santiago Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @svpino

21 Dec
Rumors are going around that Twitter cripples tweets that include links.

There's nothing in their official documentation, but a lot of people think that's the case.

I thought this through, and I will not play along.

πŸ§΅πŸ‘‡
Allegedly, if I disable the links, my tweets will get much more impressions because Twitter will push them to more people.

Assuming this is the case, what's the cost?

My followers will have to start copying, then pasting any links that I post.

(2 / 4)
Seems like a small nuance, but reading comments on tweets with disabled links, the process is very error-prone and a lot of people have trouble accessing the content.

This is not what I want.

(3 / 4)
Read 4 tweets
3 Dec
Transitioning from Software Engineering to Machine Learning.

πŸ§΅πŸ‘‡
I'll tell you my story.

It might work for you. It might not.

Hopefully, it gives you another perspective. Hopefully, it helps.

(2 / 14)
Many people see "Software Engineering" and "Machine Learning Engineering" as two completely different specialization areas.

There are many differences, for sure.

But I personally like to think about them as a single, fluid, all-encompassing position.

(3 / 14)
Read 14 tweets
2 Dec
The HTML + CSS Twitter conspiracy.

A tread πŸ§΅πŸ‘‡
A lot of people out there recommend starting with HTML and CSS to aspiring developers.

They suggest this combination is a stepping stone for you to reach your goals.

That's nonsense.

(2 / 9)
There's absolutely nothing wrong with HTML and CSS.

But they aren't necessarily the foundation that you need when starting out.

Yes, they are simple to learn compared to a fully-fledged programming language, but they are also very different.

(3 / 9)
Read 9 tweets
1 Dec
Django versus Flask versus FastAPI.

🐍 πŸ§΅πŸ‘‡
Django

▫️ Rapid development
▫️ A lot of out-of-the-box functionality
▫️ Easy for building complex, full web applications
▫️ MVC design paradigm
▫️ Robust security features
▫️ Extensible (a lot of components out there)
▫️ Large community

πŸ‘‡
Flask

▫️ Very light
▫️ Doesn’t make decisions for you
▫️ Doesn’t bring anything that you don’t need
▫️ Modular, so it’s easy to extend
▫️ You can plug in your favorite ORM
▫️ Great documentation
▫️ Very easy to start with
▫️ Large community

πŸ‘‡
Read 5 tweets
30 Nov
Here is every course that I've taken over the last 5 years to work full-time in Machine Learning applications:

πŸ§΅πŸ‘‡
(I took the following four classes while going through my Masters at Georgia Tech):

- Machine Learning
- Reinforcement Learning
- Reinforcement Learning for Trading
- Computer Vision

πŸ‘‡
(The following three courses are available through Coursera, and I recommend them for anyone trying to start):

- Machine Learning
- Deep Learning Specialization
- TensorFlow In Practice Specialization

πŸ‘‡
Read 5 tweets
25 Nov
Working on problems is the best way to learn Machine Learning.

Here are 10 projects to start your journey.

πŸ§΅πŸ‘‡
I picked all 10 projects from Kaggle.

When you are getting started, having a community ready to help is very important.

Also, every one of these problems has been solved by many people, and you can find those answers if you get stuck!

πŸ‘‡
I sorted the problems in the way I'd recommend you to start.

They more or less increase in complexity as you move through the list.

Let's get started!

πŸ‘‡
Read 14 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!