# [How to Break Into Machine Learning](/content/blog/how-to-break-into-machine-learning/index.html)

_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.](/content/courses/intro-to-data-science/index.html)  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](/content/courses/intro-to-data-science/index.html) 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](/content/courses/intro-to-data-science/index.html).

**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, your 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.

###### Topics:    [Natural Language Processing](/content/blog/topic/natural-language-processing/index.html) [Machine Learning](/content/blog/topic/machine-learning/index.html) [Big Data](/content/blog/topic/big-data/index.html) [Careers](/content/blog/topic/careers/index.html) [Data Engineer](/content/blog/topic/data-engineer/index.html) [Data Science](/content/blog/topic/data-science/index.html) [Programming Tips](/content/blog/topic/programming-tips/index.html)