How Difficult is it to Learn Python for Machine Learning?

Are you curious about learning Python for machine learning but worried that it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning Python for machine learning depend on factors like whether you already know the fundamentals of Python programming or you know another programming language. The difficulty of learning Python for machine learning also depends on the type of classes or training methods you choose.

No matter your current schedule or comfort level with Python for machine learning, plenty of tools are available to help make learning easier than you might think.

What is Python for Machine Learning?

Python is an open-source, general-purpose programming language first developed in 1991. Because Python is open-source, it is free and publicly available for anyone to use. Python can be used to build websites, develop software, conduct data analysis, and automate tasks using artificial intelligence (AI) and machine learning. 

Machine learning enables computer systems to automatically learn and adapt without manual input. The system does this by using statistical models and algorithms to detect patterns in data. Python’s libraries and frameworks, along with the coding language’s platform independence, popularity and community, concise and readable code, flexibility, and consistency, make it a perfect fit for machine learning. 

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

What Can You Do with Python for Machine Learning?

Python is a general-purpose programming language that you can use to create data visualizations, write programs for machine learning, analyze data, make file directories, build apps, and more. Because of its many applications in using and managing data, Data Scientists must have a thorough knowledge of Python programming and its uses. 

Python for machine learning is used to personalize and improve user experiences. Machine learning can automate customer support, detect fraudulent transactions, recommend products, refine search engine results, automate translation, recommend music, and predict the effectiveness of drug treatments. There are countless uses for machine learning and demand is only increasing, making now an opportune time to learn Python for machine learning.

What Are the Most Challenging Parts of Learning Python for Machine Learning?

The most challenging aspects of learning Python for machine learning depend on your starting point. Suppose you already know how to program using Python. In that case, learning Python for machine learning is simply adding one more tool to your existing toolkit using a programming language you already understand. If you do not have any experience programming in Python, you will need to start by learning the Python programming fundamentals before learning Python for machine learning. 

Python is considered an easy programming language, but knowing another language makes the process even easier. Applying the skills is the hardest part of learning any programming skill or language. It takes practice to master any programming skill. Hands-on experience is critical to learning Python for machine learning but can be challenging and frustrating if you run into questions or other hurdles. An instructor-led class, such as an in-person class or live online class, allows you to ask your questions as they come up, receive instant feedback from an instructor, and complete interactive projects through step-by-step guidance. 

How Does Learning Python for Machine Learning Compare to Other Languages?

Python, Java, and R are all popular programming languages used in data science. Each coding language has its benefits and drawbacks, which this section discusses in detail. This section also compares the difficulty of learning each of these programming languages. 

Java is a concurrent, general-purpose, class-based, and object-oriented programming language. Java works best for large-scale projects but does not perform as well as Python and R when used for statistical monitoring. Java is not considered complex or challenging to learn. 

Python provides the most flexible solution for workflow integrations, allowing you to integrate data analysis and statistical method features with a web app or production environment. Python libraries like scikit-learn and PyBrain can be used to build prediction engines and models that you can easily integrate into the production environment. Python is considered a beginner-friendly coding language, but as with any programming language, hands-on experience is essential to committing skills to memory. 

R was created for detailed statistical analysis. This coding language is ideal for data science reports thanks to its ability to create high-quality graphics and charts, but R does run slower than either Python or Java. R is considered a difficult programming language to learn. 

Whether you want to learn Java, Python, or R, having an instructor to guide you through real-world, hands-on projects can help you learn faster and solidify your skills. Learn more about machine learning and the professions that use it, and compare different machine learning skills with Noble Desktop’s machine learning resources. 

How to Learn Python for Machine Learning

When it comes to learning Python for machine learning, you have several class types and training methods to choose from. 

On-demand classes consist largely of pre-uploaded materials such as videos and written content. One advantage to this training method is that it is incredibly affordable with some courses available for free and subscriptions ranging from $30 to $60 per month. On-demand classes are also known as self-paced classes because you determine when and at what pace you learn, making it extremely flexible. The primary drawback to on-demand classes is lack of access to an instructor. When you have questions, you may have to wait several hours or days to receive an emailed response. This can bring your learning to a halt in the meantime.

Other Python for machine learning training options include in-person and live online classes. Both in-person and live online classes are taught at specific class times by expert instructors. Although these classes are more expensive than on-demand classes, they also come with many benefits that on-demand classes do not. Instructor-led courses connect you to a teacher and classmates in real-time, allowing for collaboration and instant feedback. You will also have an expert guide to lead you through interactive projects step by step. Some in-person and live online classes also offer mentorship, job search assistance, flexible payment plans, and other benefits that make it a worthwhile long-term investment, especially if you want to start a new career.

Learn Python for Machine Learning with Hands-on Training at Noble Desktop

Noble Desktop offers instructor-led courses that provide hands-on experience using Python for machine learning. You can take a specialized course such as the Python for Machine Learning course or learn this skill through the Data Science Certificate program. 

The Python for Machine Learning course requires students to have a strong foundation in Python programming and its data science libraries NumPy and Pandas prior to enrollment. If you do not have previous experience with Python programming, you can satisfy this prerequisite by first taking Noble’s Python for Data Science Bootcamp.

If you want to learn Python for machine learning as part of a broader curriculum, especially if you want to launch a new career in data science, the Data Science Certificate program covers essential skills in a single program. This program includes small classes, 1-on-1 mentoring, setup assistance, a free retake, flexible payment options, hands-on experience, and job search assistance.

Explore live online Python for machine learning classes that connect you to expert instructors for real-time feedback and guidance.