Santiago Profile picture
14 Sep, 10 tweets, 2 min read
Here are 12 skills that you wanna add to your Data Science / Machine Learning resume.

The first 6 are foundational and important. The other 6 are in crazy high demand, harder to build, and will set you apart.

The industry is relatively young so we are still figuring out titles and requirements, but certain skills are already surfacing as fundamental.

Here I compiled twelve of them.

They aren't all required. They just represent a good blueprint for you to focus on.

1⃣ Notions of Probabilities and Statistics — You need at least enough to understand how some algorithms work and how to interpret their results.

2⃣ Data Management — Capturing, querying, storing, and transferring data. SQL is a very important skill here.

3⃣ Data Wrangling — Preparing, cleaning, transforming the data for further analysis. This is one of the most important skills to build.

4⃣ Data Visualization — Usually an underrated skill. Your data is telling a story, and it's your job to present it to the world.

5⃣ Programming — It's imperative that you know enough to draw insights from data using your language of choice.

6⃣ Machine Learning Algorithms — Understanding existing algorithms, and having the capability to apply them and interpret their results is key.

Most people check these six skills.

But you aren't most, so here you have a list with the other six.

These are sexier but harder to build. These will set your resume apart.

1⃣ Deep Learning — A subset of Machine Learning methods based on Neural Networks.

2⃣ Computer Vision and Natural Language Processing — These are probably the two hottest areas in the industry right now. They are about extracting meaning from images, videos, and text.

3⃣ TensorFlow, Keras, PyTorch — These are the most popular libraries to build Deep Learning applications.

4⃣ Cloud Computing — Today, there's no Machine Learning without having access to the resources and services provided by the Cloud.

5⃣ Big Data — The ability to deal with large and complex data sets. Tools like Hadoop and BigQuery are examples here.

6⃣ DevOps / MLOps — These skills are centered around the ability to build and manage machine learning pipelines and workflows.

It's really difficult to acquire all of these skills and be good at every single one of them.

But you don't need that.

Instead, focus on the basics and expand your capabilities into areas that will increase your value.

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More from @svpino

15 Sep
TensorFlow is currently the most popular end-to-end platform for Machine Learning.

Here you have a free 7-hour TensorFlow 2.0 course that's packed with everything you need to get started.

(A single hour per day can get you through this course in a week! Just one week!)

The course is structured in 8 different modules that cover different aspects of Machine Learning and focus on how to apply TensorFlow 2.0 to solve different problems.

Here is the list of modules:

1⃣ Machine Learning Fundamentals
2⃣ Introduction to TensorFlow

3⃣ Core Learning Algorithms
4⃣ Neural Networks with TensorFlow
5⃣ Deep Computer Vision - Convolutional Neural Networks
6⃣ Natural Language Processing with RNNs
7⃣ Reinforcement Learning with Q-Learning
8⃣ Conclusion and Next Steps

Read 5 tweets
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:

▫️(Irrelevant) years of experience

I promise; this is for you.

Let's get started!

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
10 Sep
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?

Read 9 tweets
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
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:…

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:…

Read 10 tweets
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

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