What is Python for Machine Learning?

Python is a general-purpose, open-source programming language that has been in use for over 30 years. It is one of the most popular and commonly-used programming languages for machine learning. In this overview, you’ll learn more about Python for machine learning, what it can do, who uses it, and how to learn it to determine how to add this skill to your professional toolbox.

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.

How Do You Get Python for Machine Learning? How Much Does it Cost?

Python is an open-source programming language, meaning anyone can download and use it for free. When using Python for machine learning, programmers often use the following Python libraries: NumPy, SciPy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, Pandas, and Matplotlib. All of these libraries are free and open-source. 

You can download the latest version of Python at the Python Software Foundation’s website and find Python libraries for machine learning at the following links: 

• NumPy
• SciPy
• Scikit-learn
• Theano
• TensorFlow
• Keras
• PyTorch
• Pandas
• Matplotlib

Python and Python libraries have years of documentation behind them and large communities of developers. These resources can help you learn how to use Python for machine learning.

What Are the Benefits of Learning Python for Machine Learning?

Machine learning is an increasingly important component in personalizing and improving user experience and automating tasks. Machine learning algorithms are used to make product recommendations to shoppers on Amazon, manage trading on Wall Street, and choose what plans next in your Spotify playlist. 

Learning Python for machine learning can advance your current career or help you start a new one. Employers look for this highly desirable skill when hiring Data Analysts and Data Scientists, so learning Python for machine learning can help your resume stand out. If you learn Python for machine learning through an instructor-led course with hands-on projects, you can add those projects to your professional portfolio to showcase your abilities to potential employers.

Python for Machine Learning Careers

Machine learning is a coveted skill with applications in technology, finance, and healthcare. Python for machine learning can open the door to one of these exciting careers: 

• Machine Learning Engineer 
• Robotics Engineer 
• Software Developer
• Data Scientist 
• Cybersecurity Analyst 
• Artificial Intelligence (AI) Engineer 

A Machine Learning Engineer creates machine learning models, optimizes data delivery, and manages complex datasets. Machine Learning Engineers may choose to build machine learning components with Python programming due to Python’s popularity, simplicity, and many uses. The machine learning components reveal patterns and make predictions to help companies plan for success. Machine learning is also a critical skill for Robotics Engineers. 

Software Developers use Python for machine learning to improve the user experience in multiple ways. Python for machine learning can enable chatbots to communicate with users helpfully and naturally, recommend products or content based on a user’s interests, and predict how users will react to application features. 

Data Scientists use machine learning to write algorithms that detect trends. The Data Scientist then uses this information to make recommendations to stakeholders. 

Understanding machine learning benefits Cybersecurity Analysts when tasked with collecting and studying large quantities of data to detect potential security risks. Machine learning can make gathering, managing, and understanding this data easier.

AI Engineers use machine learning (a subset of AI) to build or modify machine learning models, develop neural network applications, and improve image recognition.

How to Learn Python for Machine Learning

There are different ways of learning Python for machine learning, including live classes, on-demand classes, and free resources. This section explores each of these options in detail. 

Live classes, or instructor-led courses, include in-person classes and live online classes. With both methods, an expert instructor guides the class in real-time through lectures, examples, and hands-on assignments. Live classes provide the opportunity to instantly have your questions answers and to receive immediate feedback. You can also collaborate with classmates, walk through real-world projects with step-by-step guidance, and receive personalized instruction. You can find in-person and live online Python for machine learning classes using Noble Desktop’s Classes Near Me tool. 

You can also explore Python for machine learning topics through on-demand (self-paced) classes. These classes, also known as asynchronous classes, consist of on-demand videos and materials that you advance through at your own pace. The flexibility to completely control your schedule and the affordability of such courses make them a popular learning option. You can explore and compare on-demand machine learning classes to find the best fit for your needs.

If you aren’t ready to commit to a paid learning option, you can explore free resources like seminars, tutorials, guides, videos, and blog posts. Noble Desktop’s Python for Machine Learning Hub connects you to multiple free resources to help you get started. If you are new to data science, the free video Get Started in Data Science provides a high-level overview of the field. When you feel ready to dive deeper into a topic, you can use the Noble Desktop Classes Near Me tool to explore machine learning classes available online and in your local area. 

A Brief History of Python for Machine Learning

Guido van Rossum created Python in the early 1990s as a general-use programming language emphasizing readable code. It is named after the British comedy troupe Monty Python of Monty Python and the Holy Grail fame. Today, the nonprofit Python Software Foundation holds the intellectual rights to the Python programming language, though it remains free and publicly available to all. Thanks to Python’s being open-sourced, thousands of programmers have contributed to its use and improvement over the last 30 years. The Python Software Foundation’s website offers access to the Python programming community, links to download the latest version of Python, documentation, learning guides, and more.

Several Python libraries for machine learning began in the early 2000s and continue the Python tradition of being free and open-source.

The respected Python library NumPy, used by scientists and engineers today, had humble beginnings as the product of mostly graduate students, some without any formal computer science training. This library, first created in 2005, builds upon the Numarray and Numeric libraries. 

Another popular Python library, Pandas, began in 2008 as the work of AQR Capital Management. It was made open-source in 2009 and became a NumFOCUS sponsored project in 2015. Pandas aims to be the most flexible and powerful open-source data analysis/data manipulation tool. 

Comparable Programming Languages

Python is a popular coding language for machine learning, but there are comparable applications and programming languages to consider as well. 

R programming is a language driven by data visualization. This open-source programming language works well for machine learning thanks to its emphasis on statistical computing. Analysts, Data Miners, and Statisticians favor R because it is a graphics-based language suitable for professionals without much coding knowledge. Data Scientists use R to implement machine learning methodologies like regression, classification, and decision tree formation. R can be used with different operating systems including OS X, Linux, and Windows. 

Some programmers use Java for machine learning. Java frameworks like Rapid Miner and Weka support decision trees, regression techniques, and machine learning algorithms. 

The language Julia creates the effective model analytics required to develop machine learning applications. Developers prefer it for its easy syntax. 

C/C++ also enjoy popularity as coding languages thanks to its power and versatility. C/C++ libraries like Torch and TensorFlow are among the top tools for machine learning. 

How hard are these and other languages to learn compared to Python? Python is considered a relatively easy language to learn, especially for those with programming knowledge. Other programming languages that are considered accessible for beginners are Java and Julia. R is considered a more complicated programming language to learn. 

Tips for Mastering Machine Learning Languages

Mastering machine learning languages requires some prerequisite skills and knowledge. 

To use Python for machine learning, you must first thoroughly understand Python programming basics. Learning Python and other beginner-friendly programming languages has the additional benefit of making it easier to learn more complex programming languages, like R, down the line. On-demand video classes can provide a high-level overview of programming languages and their uses, but hands-on experience is what will help to truly develop your skills and commit the knowledge to memory. It helps to have real-time feedback and guidance from an instructor during these hands-on assignments. You can take courses specifically focused on teaching Python fundamentals, such as Noble Desktop’s Python for Data Science Bootcamp. This class is a necessary prerequisite for Noble’s Python Machine Learning Bootcamp. You can also opt to take these two courses as a package deal with the Python Data Science & Machine Learning Bootcamp. 

Keep the following tips in mind to succeed in learning Python or any other machine learning language: 

• Know your goals. Concrete goals will help you stay focused and motivate you to learn new skills. Define what skills you want to learn and what you want to achieve with those skills. 
• Have patience. Developing new skills takes time.
• Make it a habit. Commit to learning at a specific time at regular intervals. Ideally, this will be a daily task.
• Choose a pace that works for you. Choose a training schedule and pace that works for you to avoid burnout. This means finding a training method that you can reasonably balance with other commitments.
• Practice makes perfect. Whether learning a simple or complex skill, practice is the key to committing it to memory. Hands-on activities help you retain knowledge, learn by doing, and can act as part of your professional portfolio. 
• Don’t be afraid to ask for help. Trying to go it alone can prove frustrating and time-consuming. Your instructor can provide helpful feedback, answer your questions, and help you through different challenges as you learn.
• Consider 1-on-1 mentorship. Personal instruction helps to ensure you get the answers you need. This isn’t limited to programming questions. Mentoring can help you plan for your future career and map a path to achieving your goals.
• Learn from classmates. Collaborating with others can help you see how others approach a particular problem. You can learn from each other’s approaches and inspire others to think in new ways. 

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.

Key Insights

• Python is a free and open-source programming language that you can use at no cost.
• Python is considered a relatively easy programming language to learn. 
• Python for machine learning is more complicated than just learning the basics of the language. You will need to thoroughly understand Python programming fundamentals before learning to use Python for machine learning.
• Other programming languages used for machine learning include R, Java, and C++. 
• Tips for learning Python and other machine learning programming languages include knowing your goals, having patience, practicing regularly, receiving personal guidance, asking questions, and learning from others. 
• You can receive comprehensive Python for machine learning training through an in-person or live online course with Noble Desktop.