What You Need to Know About Machine Learning in 2019

Let’s talk about the 6 steps to becoming a machine learning expert in 2019. This was inspired by our team’s machine learning masterclass, which launched this week.

1. Python Programming

Python Code

Any learner who wants to learn machine learning the practical way, rather than just the theory, should first learn how to code in Python. Being a more concise and clean language than others like Java and C#, Python is easy to learn for even someone who has never coded before.

2. Machine Learning Concepts

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Once you’ve learned Python, you’ll want to learn the fundamentals and theory on machine learning.

It is important to have a solid footing on what machine learning is, before you start making a specific project. This ensures you know all the terms and concepts mentioned when you’re studying a course or looking up documentation for your first actual project.

3. TensorFlow 2.0

TensorFlow Logo
TensorFlow is a library for machine learning research and production.

As it was developed by Google, it’s no surprise that TensorFlow is the most popular library. And it’s open source as well! TensorFlow 2.0 was just released, so make sure you’re not choosing an outdated tutorial using an earlier version. If you want to learn on the most current library, look for TensorFlow 2.0.

First you’ll want to learn TensorFlow fundamentals and get yourself familiar with the library and its syntax. The best way to learn is to make a few simple projects that are targeted for beginners and don’t overwhelm you.

4. Data Models and Algorithms

Data on Computer
Once you have a foundation of TensorFlow and a few easy projects under your belt, you can start making more advanced projects using data models and algorithms.

At this point, you should be able to create your own data pipeline for production. A lot of tutorials out there either target beginners or experts, so it can be hard to find a full tutorial series when you’re in that intermediate stage. Where you can find the projects, building examples is the best way to learn more in-depth how to work with data in various ways.

5. Neural Networks

Artificial Intelligence Head
After you’ve built several projects with TensorFlow, you can start implementing neural networks into projects.

This is where you get to dive into deep learning and use Python and TensorFlow together to create even more advanced projects. Make sure you try implementing various types of neural networks, including different types of unsupervised and supervised networks.

6. Real-World Projects

3D Model of Car
Once you have these 5 core skills, you can start building the real-world projects that we often see making headlines.

That means computer vision for image recognition. That means stock prediction. Fraud detection.

Machine learning has so many applications that you can dive into right away on a small scale. You can build your own version of Amazon and Netflix’s recommender systems. You can implement lane detection, car detection, and curve navigation for self-driving cars.

In the real world, these projects are handled by teams of people. While you’re learning, you can build a subset of such a project, or the project on a smaller scale, to show what you can do, and set yourself up to become one of the people on a machine learning team.

If you’re as serious about machine learning as we are, check out our team’s machine learning masterclass.

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