---
title: How to Break Into Machine Learning
description: Read our Natural Language Processing (NLP) and Machine Learning tips to have a clear path in order to break into these fields.
---

[Byte Academy Blog](https://www.byteacademy.co/blog)

# [How to Break Into Machine Learning](https://www.byteacademy.co/blog/how-to-break-into-machine-learning)

 Written by [Byte](https://www.byteacademy.co/blog/author/byte-academy) | Aug 6, 2020 11:12:15 AM

*Everyone seems to be intrigued by Machine Learning, which has become a "buzz word" lately.  Although intimidating,  you can certainly break into the field, or learn more about another phrase we hear a lot, Natural Language Processing. Read some tips and background from Data Scientist, [Lesley Cordero](https://www.linkedin.com/in/lesleycordero/), who teaches our [Data Science course.](https://www.byteacademy.co/courses/intro-to-data-science) Lesley also teaches a class in machine learning for those interested in the particular area.*

**What's the difference between Machine Learning and Natural Language Processing (NLP)?**

Both are similar because they allow for prediction based on pattern detection. Both are also similar in that they rely heavily on statistics.

With that said, **Machine Learning Algorithms** generally refer to algorithms like regression, classification, support vector machines, decision trees, random forests, etc. These algorithms are all used in the prediction of numerical data. **Natural Language Processing** is a more specific discipline that applies statistical models and techniques in order to detect patterns in text/speech.

**What are some tips for entering Machine Learning?**

Be strong in math and be incredibly comfortable with probability, statistics, and linear algebra.

If you want to learn the theory behind Machine Learning,   I would follow a useful online course like the one offered by Stanford (Byte Academy will be offering on soon).   On the technical side, you should become fluent in Python & R, especially the built in modules like nltk, sci-kitlearn, and theano.  I educate on these items in the [Data Science Course ](https://www.byteacademy.co/courses/intro-to-data-science)that I teach at Byte Academy.

**Is now a good time to start a career in Machine Learning?**

If you’re asking whether or not there’s a large demand in Machine Learning, the short answer is absolutely. There are plenty of job prospects for people with a machine learning background, whether that be in academia or industry. With that said, a career in machine learning doesn’t just happen overnight. A solid background in statistics and linear algebra is definitely needed, likely as well as a solid programming background (R or Python, most likely).

If you actually like machine learning and have the time to invest into creating a career out of it, I’d say go for it! The problem won’t be a lack of jobs, but, rather, possibly a lack of sufficient background.

If you’re interested in learning more data science/machine learning, check out Byte Academy’s Data Science course [here](https://www.byteacademy.co/courses/intro-to-data-science).

**What's it like to be a Machine Learning or Data Science Engineer?**

Much like software engineering roles, the core of your job is to be an **engineer**. So while your job encompasses the concepts behind the machine learning models you work with, you central focus is on the actual implementation. In my own experience, being a data engineer involves a lot less mathematical application than being a data scientist.

 

[View full post](https://www.byteacademy.co/blog/how-to-break-into-machine-learning)

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