How Difficult is it to Learn Machine Learning?

Are you curious about learning machine learning (ML) but worry it might be too difficult? Of course, the challenges that come with learning a new skill are somewhat subjective. The degree of difficulty in mastering machine learning depends on factors like:

• Lack of experience with ML algorithms
• Familiarity with programming languages like Python or R
• Your availability and schedule

No matter your current commitments or knowledge of machine learning, you can find plenty of tools to help make machine learning more manageable than you might think.

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.

What Are the Most Challenging Parts of Learning Machine Learning?

Machine learning is a subset of artificial intelligence (AI). The use of ML algorithms in business, government, and other sectors has expanded rapidly in the 2020s and promises continued growth.

Because you can apply this discipline to so many areas, the challenge in learning it depends on how and where you intend to use it. If you’re at the beginning of your ML journey, here are the biggest challenges to solve:

• Understanding basic machine learning concepts - Do you already know about ML algorithms or top uses for machine learning? Depending on the sector involved, you’ll most likely need some data science fundamentals, familiarity with languages like Python, and data visualization tools. Familiarize yourself with the basics before you enroll in formal training.

• Applying sufficient programming skills for data analysis - Your machine learning focus may be anything from data visualization with Tableau to analysis using Python. You might not need to learn a programming language before attending an ML class, but be prepared to learn one or more during training.

• Focusing on a specific goal for a particular sector - This factor trips up many students new to the topic of machine learning. To better understand the importance of targeted training, consider the following examples:

• A student enrolls in a machine learning bootcamp with no prior knowledge of the subject but intending to become a Financial Analyst. A novice like this might benefit most from a course like Noble Desktop’s FinTech Bootcamp.
• Another bootcamp participant already works as an Analyst but wants to transition to data science and Python. This data pro might benefit most by getting ML training through Noble’s Data Science Certificate, which includes machine learning as part of a broader data science curriculum.

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.

Making It Easier

Despite any challenges that come up along the way, studying machine learning is well worth your time. The degree of difficulty involved will depend on numerous factors, such as:

• Is ML the primary focus of your career?
• Is ML training a piece of a data science career?
• Do you need additional training before studying machine learning, or can you learn it as part of a broader curriculum?

Making it easy in this context means knowing what you’re getting into before you get into it. Whereas a Data Scientist may need to study machine learning, a Machine Learning Engineer may require specialized skills and knowledge not necessarily critical to all data science positions. These can include:

• Machine learning and deep learning models
• Software engineering
• Data structures and data modeling
• Statistics
• Metadata version control

Other Considerations

If you enroll in a bootcamp or certificate program that features machine learning or includes it as part of a broader curriculum, you can get the tools you need in a measurable timeframe. That’s an essential consideration, especially for those already in a data-centered field or busy professionals with family and other obligations.

Keep all the above factors in mind when seeking training. A bootcamp can deliver an in-depth education in ML that aligns with your current skill set, knowledge, and goals. Check out all the options from Noble Desktop in the machine learning area, whether you plan to work in FinTech, Python machine learning, or as a Data Scientist.

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.