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Learn Machine Learning

A Comprehensive Guide to Start Learning Machine Learning

Machine learning (ML) is one of the most important branches of artificial intelligence or AI. Voice assistants like Siri, product recommendations on popular websites, and even medical diagnostic tools rely on machine learning algorithms to provide information.

Machine learning (ML) is a subset of artificial intelligence (AI) and a complex, multidisciplinary field. Top ML roles include Data Scientists, Machine Learning Engineers, and Natural Language Processing (NLP) Scientists.

If you’ve always wanted to learn machine learning but can’t figure out how to get started, this guide is for you. Here, you’ll learn more about the various ways to learn machine learning, free resources to take advantage of, and the types of careers that commonly use machine learning.

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.

Careers that Use Machine Learning

While Data Scientists and Data Analysts use machine learning to collect and interpret data, Business Intelligence (BI) Analysts and Financial Analysts may analyze data sets differently and for different reasons.

Machine Learning Engineers, Software Developers, and Software Engineers also benefit from machine learning algorithms. They may develop everything from applications to platforms. These high-level professionals typically have a solid understanding of numerous disciplines—from programming languages to computer architecture.

Machine learning is vital in computational linguistics, NLP science, and design, along with top industries like banking, retail, and healthcare.

Why Learn Machine Learning?

Machine learning programs have become so common that you most likely interact with them daily. Consider Amazon’s “Compare Similar Items” feature, the “Because You Watched” recommendations on Netflix, and the recommended reels on your Instagram feed. All of these tools come from machine learning algorithms.

If you’re planning to start a career in data science or analytics, ML can be a core segment of your education. Anyone with a title like Data Scientist or Data Engineer should be familiar with machine learning concepts. A Machine Learning Engineer or Machine Learning Architect must have a specialized skill set in subjects like deep learning, data modeling, and natural language processing (NLP).

Read more about why you should learn machine learning

How to Learn Machine Learning

Students seeking a machine learning education benefit most from live training, either in-person classes or those held live online via teleconferencing. Live classes keep participants engaged, and they often gain from networking with fellow attendees. Bootcamps and certificate programs, like the Python Machine Learning Bootcamp from Noble Desktop, offer immersive training in a dynamic learning environment.

For those who are not yet ready to commit to a comprehensive training program, an excellent starting point is on-demand or self-paced machine learning training. Topics include programming languages like Python and R, Microsoft Azure, and open-source libraries like TensorFlow and PyTorch. While you won’t have an instructor holding you accountable for assignments, you can benefit from learning on your schedule at any hour. Some on-demand courses are either free or available through a platform subscription plan.

Are you entirely new to machine learning? Check out a few free online resources, like those available through the Noble Desktop Learn Hub. You’ll find blog posts, tutorials, and articles relevant to machine learning, including data analytics, data visualization, and Python programming. You’ll find a wealth of video seminars on the Noble site featuring related topics like SQL and Python, among others.

Read the full guide on how to learn machine learning.

Free Introductory Machine Learning Course Online 

Not ready to take a full-length machine learning course? If you’re not able to commit to a full-length bootcamp or certificate, you should consider the many free online resources you can use to start studying machine learning.

One of the most important areas of study for those new to machine learning is technical proficiency in a free programming language like Python. If you don’t already have Python experience, it can be helpful to learn more about it before you dive into the study of machine learning. 

Noble Desktop hosts an online seminar entitled Intro to Python Fundamentals. In this free introductory course, you’ll learn about the practical uses of Python. The curriculum walks new programmers through every step to get started in Python programming—from how to install Python to how to write code.

Other free online courses include Introduction to Embedded Machine Learning from Edge Impulse, Artificial Intelligence: Ethics & Societal Challenges from Lund University, and the University of London’s Foundations of Data Science: K-Means Clustering in Python.

Read about more free machine learning videos and online tutorials.

Level of Difficulty, Prerequisites, & Cost 

Studying machine learning (ML) can be a lifelong pursuit. The challenges of learning this topic depend on how much you need to learn and where you will apply it. 

You may have more challenges learning ML if you lack experience with algorithms or have little familiarity with programming languages like Python. Consider learning these through a course that features them or includes them as part of a broader computer or data science curriculum.

Machine learning training costs range from free to around about $1,895 to $4,495 for a bootcamp or certificate program. Some of these courses include intensive ML training and can be completed in a few months or weeks.

Read about how difficult it is to learn machine learning.

How Does Machine Learning Compare to Other Fields?

In many ways, you can consider machine learning (ML) a subcategory within a larger category. Some experts view machine learning as a branch of artificial intelligence (AI) and artificial intelligence as a subcategory of computer science. Others specify machine learning as a branch of data science and may differentiate it in other ways.

However you categorize it, machine learning as a field can apply to numerous disciplines. Because there is so much overlap between the two, consider how ML compares to the broader data science field.

Pure Data Scientists may not need intensive machine learning training, although many do. Data science and machine learning typically overlap in areas like:

  • Expertise in programming languages like Python and R
  • Cloud training in Azure and Amazon Web Services, among others
  • Familiarity with version control systems like Git and hosting services like GitHub
  • Tech stacks may include metadata storage with Comet ML or Neptune.ai, among others

The challenges and costs associated with learning data science can be comparable to learning ML, depending on your goals and current skill set. You can train for data science, machine learning, or both through Noble Desktop’s data science resources.

How to Decide the Best Way to Learn Machine Learning

Your search for the best way to study machine learning begins with reflecting on your goals. Consider how and where you plan to apply the skills and knowledge you get from a machine learning course. If you’re a beginner and can’t or don’t want to spend money on training, review some free online articles or seminars. Noble Desktop’s Learn Hub is an excellent place to get introductory materials on data science, including machine learning.

Those who want to develop a working knowledge of machine learning, plug a skill gap, or level up from a current position should look for a machine learning bootcamp. You can use Noble’s search tool to find courses like a Python Machine Learning Bootcamp, FinTech Bootcamp, or Python for Data Science Bootcamp.

If you need to master machine learning for a new career or want to pivot to a new industry, consider enrolling in an immersive certificate program. Noble Desktop’s Data Science Certificate and Data Analytics Certificate include machine learning as part of a broader curriculum. Both are open to beginners.

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.

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