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Santiago

10 Sep, 9 tweets, 2 min read

Here are 20 fundamental questions that you need to ace before getting a Machine Learning job.

Almost every company will ask these to weed out non-prepared candidates. You don't want to show up unless you are comfortable having a discussion about all of these.

🧵👇

Of course, this is not an exhaustive list. There are many more topics and concepts you should master before applying for a job.

But hopefully, these will give you an idea of where you stand today.

🏃‍♂️Let's get started!

👇

▫️ Warming up ▫️

1. Explain the difference between Supervised and Unsupervised methods.

2. What's your favorite algorithm? Can you explain how it works?

3. Given a specific dataset, how do you decide which is the best algorithm to use?

👇

▫️ Getting one step deeper ▫️

4. When should you use classification over regression?

5. Can you explain how Logistic Regression works?

6. What are the advantages and disadvantages of decision trees?

7. Can you compare K-means with KNN?

👇

▫️ This is about to get real! ▫️

8. How much data would you allocate for your training, validation, and test sets?

9. Can you explain what is the "Curse of Dimensionality"?

10. What are some methods to reduce dimensionality?

11. How would you handle an imbalanced dataset?

👇

▫️ Let's now get deep into it! ▫️

12. Can you explain the trade-off between bias and variance?

13. Can you define and explain the differences between precision and recall?

14. How do you define the F1 score and why is it useful?

👇

15. How do you ensure you're not overfitting? Can you explain some techniques to reduce overfitting?

16. Can you explain what is cross-validation and how is it useful?

17. Can you explain the difference between L1 and L2 regularization?

👇

18. What is the ROC Curve?

19. What is a Confusion Matrix and how is it useful?

20. Which is more important: model accuracy or model performance?

👇

If you start practicing these questions, a whole universe of knowledge will open before you. It's fascinating!

In the coming days, I'll be posting the answers and more specific content related to each one of these questions.

I hope you are there to add to the conversation!

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More from @svpino

Santiago

@svpino

12 Sep

A step-by-step guide to starting with Machine Learning. (For beginners looking to get on it right away.)

Table of Contents:

1. Where do I put the code?
2. Manipulating data
3. Let me see those charts
4. Decision Trees
5. Tying everything together
6. Our very first project

🧵👇

0⃣ Requirements to go through this guide:

▫️Python 🐍
▫️Wanting to make a difference.

To finish this tutorial you do not need any of the following:

▫️Math
▫️Degrees
▫️(Irrelevant) years of experience
▫️Superpowers

I promise; this is for you.

Let's get started!

👇

@CoreyMSchafer

1⃣ Where do I put the code?

Jupyter is gonna be your code editor. Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

👇

Read 9 tweets

Santiago

@svpino

9 Sep

It took me 4 years to complete my Master's while I was working full time (2015 - 2019).

It's a Master of Science in Computer Science with a Machine Learning Specialization.

Here are all courses I had to complete to finish the program: 🧵👇

For the Machine Learning Specialization, I needed 15 hours that I distributed across the following courses:

1. Machine Learning
2. Computer Vision
3. Reinforcement Learning
4. Intro to Graduate Algorithms
5. Machine Learning for Trading

👇

To complete the program, I needed another 15 hours (but I finished 18):

6. Database Systems Concepts and Design
7. Software Development Process
8. Software Architecture and Design
9. Human-Computer Interaction
10. Advanced Operating Systems
11. Software Analysis and Testing

👇

Read 5 tweets

Santiago

@svpino

8 Sep

Artificial Intelligence can be a bitch.

Here are 6 high-profile projects that have miserably failed and have made the respective companies look really foolish:

🧵👇

1⃣ Back in 2015, a software engineer reported that Google Photos was classifying his black friends as gorillas.

The algorithm powering the service was unable to properly classify some people of color 🤦!

Here is the story: theverge.com/2018/1/12/1688…

👇

2⃣ Back in 2016, Amazon had to scrap it's AI recruiting tool because it discovered that the system taught itself that male 👨 candidates were preferable, and penalized every resume that pointed to a female 👩 candidate.

Here is the story: reuters.com/article/us-ama…

👇

Read 10 tweets

Santiago

@svpino

7 Sep

Got asked a ton of questions about Machine Learning!

I decided to build a short FAQ to help you move forward.

Here are my answers to the 10 most frequently asked questions about getting into Machine Learning: 🧵👇

1⃣ Do I need Probabilities / Statistics / Linear Algebra to get started?

All of these help tremendously, especially if you want to understand how the algorithms work.

But they aren't a hard requirement to start applying some of the algorithms.

👇

2⃣ How relevant is a Ph.D. or MS degree to get a job?

Companies are currently asking for degrees to weed out people that apply to jobs prematurely.

But degrees aren't a requirement most of the time. Your skillset is the most important factor.

👇

Read 12 tweets

Santiago

@svpino

5 Sep

One of the best steps I’ve taken as a Software Engineer has been to get into Machine Learning.

If you are looking for what's next in your career, here are some pointers to get you started: 🧵👇

I always answer “what would you recommend next?” with "Machine Learning."

Here is why:

1⃣ Not only we are barely touching the surface of how Machine Learning will transform our lives in the next 10 years, but the need for qualified professionals will continue to rise.

👇

2⃣ As of today, Machine Learning is one of the fields that pay the most money, at least in the United States.

3⃣ There's huge demand, but there aren't many people competing in the market which opens many opportunities.

👇

Read 18 tweets

Santiago

@svpino

31 Aug

I've been in leadership positions for over a decade, and during that time, I've helped sell millions of dollars in software services.

Here are some of the things that I've learned about charging money for your skills. 🧵👇

First, we need to talk about hourly pricing.

Everyone will tell you to get away from hourly pricing and more into value-based pricing.

I agree.

But here is the thing: value-based pricing doesn't work a lot of the time. Hourly pricing always works.

👇

A lot of clients don't fall for the "I'm increasing your sales by $50,000 a year, so my website is worth $20,000" strategy.

That $50,000 is —most of the time— a bad guesstimate. You can't pretend to understand a business that well in a few days. And clients will call BS.

👇

Read 16 tweets

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