What to Learn After Machine Learning

Machine learning is a subset of artificial intelligence (AI) and one of the 21st century’s most exciting fields. If you are already familiar with machine learning algorithms, you might consider a broader AI education and study areas like natural language processing (NLP).

We’ll cover these topics in more depth below so you clearly understand what skill you want to tackle next.

What is Machine Learning?

Machine learning (ML) is one of the best-known subcategories of artificial intelligence (AI). This complex and multidisciplinary field can require training in programming languages like Python, databases like MySQL, and natural language processing (NLP). Common careers that require machine learning skills include Machine Learning Engineers, Data Scientists, and Business Intelligence (BI) Analysts.

Machine learning is often associated with Python programming and data science. Supervised, unsupervised, and reinforcement learning are the top three models of ML algorithms. Popular uses of ML in daily activities include voice recognition tools like Siri, recommendation lists from Amazon or Netflix, and user engagement icons on platforms like Instagram and TikTok. 

Read more about what machine learning is and why you should learn it. 

What Can You Do with Machine Learning?

Machine learning algorithms dominate today’s internet. Websites gather information based on everything you do online, from your search patterns to previous purchases, social media posts, and whether or not you abandon a product in a cart. As ML algorithms continue to influence our personal and professional lives, more and more businesses use them to streamline processes and determine customer and client journeys. The following are a few of the most popular machine learning applications.

• Social media - Meta Platforms (formerly Facebook) was one of the first well-known companies to use ML to measure user activities. Examples of how they analyze statistical activity include their user engagement, chatbots, and content filtering features. Other top social media platforms using ML extensively include Twitter, Pinterest, and TikTok.

• Product Recommendations - If you’ve ever bought a product from Amazon or subscribed to a streaming service, you’ve probably seen the You May Like feature. Companies ranging from Apple to Netflix use machine learning algorithms to customize your experience.

• Natural Language Processing (NLP) involves text analytics and functions combined with machine learning. Analyzing text includes basic steps like identifying the language and more complex steps like syntax parsing and sentiment analysis. ML is essential to text analytics and NLP solutions.

Artificial Intelligence

As a subcategory of artificial intelligence, machine learning can be a career on its own. You might study ML as part of an AI curriculum if you plan a career in robotics or natural language processing (NLP). However, if you want to be a Machine Learning Engineer or Data Engineer, you can likely start with ML and pick up broader artificial intelligence studies later.

One aspect to consider is that of deep learning, or DL. While machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning. This type of algorithm tries to emulate human neural networks. The study of deep learning can include areas like natural language processing (NLP) and facial recognition.

Natural Language Processing (NLP)

The study of machine learning leads some students to natural language processing (NLP) or includes it as part of an artificial intelligence curriculum. While NLP is essential to data science, it won’t necessarily be part of your machine learning education. You can get into the ML field by building skills like Python, especially if you enroll in a bootcamp or certificate like the Python Data Science & Machine Learning Bootcamp from Noble Desktop.

However, by the time you find an entry-level position in the field, you’ll need to have familiarized yourself with basic NLP techniques like sentiment analysis, keyword extraction, and tokenization. They’re essential if you study to be a Data Scientist (particularly an NLP Scientist) or Machine Learning Engineer.

TensorFlow: A Powerful Library

If you take a course like Noble Desktop’s Data Science Certificate, you’ll learn open-source libraries like Pandas, Matplotlib, and NumPy. TensorFlow is another powerful open-source library and a common requirement for many machine learning and artificial intelligence roles. Unless you learn to use TensorFlow as part of your ML training, consider adding it to the tools in your toolbox.

Learn Machine Learning with Hands-on Training at Noble Desktop

Noble Desktop offers a variety of bootcamps and certificates that feature machine learning, both in-person and live online via teleconferencing. Some include Python as a focus, others include machine learning as part of a broader data science curriculum, and others cover ML in a FinTech curriculum. All bootcamps and certificate programs feature small class sizes to maximize personal attention from expert instructors.

• Data Science Certificate - Noble’s Data Science Certificate program teaches participants data science fundamentals before advancing through machine learning, Python for automation, and Structured Query Language (SQL). This immersive certificate is open to beginners; you can retake it for up to one year at no additional charge.

• Python Machine Learning Bootcamp - Programmers already comfortable with Python and its data science libraries can get their machine learning training as part of the Python Machine Learning Bootcamp. Attendees can save by taking this shorter course as part of the Data Science Certificate program.

• Python Data Science & Machine Learning Bootcamp - This comprehensive bootcamp combines the same ML and Python training modules as the Data Science Certificate but does not include the Structured Query Language (SQL) bootcamp. It’s open to beginners and designed to prepare students for entry-level Python engineering or data science positions. 

For more information on Noble Desktop’s data science classes, including machine learning, check out all their full-time and part-time data science programs.

Key Insights

• Machine learning (ML) is a subcategory of artificial intelligence (AI). You can study machine learning on its own or as part of a broader AI curriculum.
• Top uses for ML include:
• Social media (user engagement, chatbots) 
• Product recommendations
• Natural Language Processing (NLP)
• If you don’t already learn them as part of your ML training, consider studying the following soon after:
• Artificial intelligence - areas like deep learning (DL) can be the perfect adjunct to an ML education
• Natural Language Processing (NLP)
• TensorFlow - a popular library required for many ML and AI roles
• You can study ML through an in-person or live online bootcamp or certificate program from Noble Desktop.