Why Learn Python for Machine Learning?

Python is a general-purpose programming language with various data science uses. Machine learning is a subset of artificial intelligence (AI) concerned with developing methods that automatically improve the performance of specific tasks based on data. Have you ever explored everything Python for machine learning can do? The data gathered by machine learning identifies your organization’s successes, trends within the organization, opportunities for improvement, patterns within the industry, projections, and more. Here, you’ll learn more about the careers that benefit from Python for machine learning and how it can enhance your professional life. Regardless of your goals, learning Python for machine learning is a valuable and impressive skill to add to your resume. 

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.

Common Professional Uses for Python for Machine Learning

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.

Understand Your Successes

Data is essential for understanding the health and successes of your organization. Without data, you cannot measure the effectiveness of marketing and advertising campaigns, calculate quarterly earnings, determine market share, track budgets, or determine any other measurement. Data Scientists gather, sort, and manage this data, but need to manually assemble all the data needed to accurately depict the present state of the business. This is where machine learning comes in. Machine learning enables gathering huge quantities of data from disparate sources.

Machine learning enables systems to get “smarter” by constantly learning and improving. The more it completes the same task, the more accurate machine learning’s findings become. Machine learning can save Data Scientists time by automatically gathering data, cleaning data, and discovering insights, with no manual input.

Machine learning can also optimize the creation of data visualizations like graphs and charts so you can better understand the data presented. These data visualizations can also help to identify opportunities for your organization.

Identify Opportunities and Plan for the Future

Machine learning can identify patterns and opportunities that allow your business or organization to get the edge over the competition. You can build machine learning methods that detect trends within the organization and industry. When you understand what your organization does well, what it can improve upon, and how this compares to other organizations within your industry, you can leverage these insights to plan for the future and make your organization stand out.

Another way machine learning helps you plan for the future is through forecasting. Machine learning can use the mass amounts of data gathered and the insights drawn from it to make automatic, real-time predictions. You can do more than just project next quarter’s earnings. You can analyze industry-wide trends and projected changes in the industry to plan accordingly. This allows your organization to be proactive rather than reactive and stay several steps ahead of the competition. 

How does machine learning go about making these predictions? The AI is trained by repeatedly completing specific tasks. The historical dataset resulting from this training is applied to new data to determine the likelihood of an outcome. This allows organizations to make accurate assumptions regarding future events. Examples include predicting the increasing or decreasing value of stock on Wall Street, determining the next time to send a customer email from a Content Management System based on past engagement, or a social media scheduling tool publishing content based on when followers are most likely to see it. 

You can go beyond making predictions for limited decisions by using these insights to plan for big-picture changes, like shifts in consumer spending. Rather than using machine learning to determine the send time of a single email, you can use it to map an entire customer journey. Rather than let a social media scheduler predict the best time for a single post, use these insights to plan a yearly social media strategy. 

Machine learning also allows for revised predictions based on new information. This way, you always act on the latest information available, allowing for increased accuracy and relevancy in your plans. 

Retain Customers with Personalized User Experience

Want to recommend products based on a customer’s purchase history? Want to predict how users will respond to a certain website design? Want to increase customer engagement and retention? Machine learning can help with that. 

When you personalize interaction with a user or custom, you improve the odds of them having a positive experience. Positive experiences directly impact customer retention, repeat purchases or repeat business, and the likelihood that they will recommend your product or organization to others.

Why Use Python over Similar Languages?

There are multiple programming languages used for machine learning. You may be wondering what makes Python worth using compared to these languages. 

Python is an open-source programming language with over 30 years of use, documentation, and improvement behind it. Python also enjoys a large and active community of contributors, making it easy to learn best practices and suggested uses from others. Python is also considered an easy programming language to learn, especially compared to the more complex R programming language sometimes used for machine learning.

When it comes to machine learning, Python offers the most flexibility for workflow integrations. You can integrate statistical method features and data analysis features with a production environment or web app. Python also offers several free, open-source libraries used for machine learning. These libraries include NumPy, SciPy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, Pandas, and Matplotlib. You can use libraries like PyBrain and scikit-learn to build prediction engines and models that can easily be integrated into the production environment. 

The flexibility of Python and its libraries, its many uses for machine learning, and its relative ease of use, along with Python’s global popularity and its being free, make it a popular machine learning programming language. 

What Makes Python so Popular?

As a general-purpose programming language, Python has many uses, especially for data science. Data science uses for Python include structuring programs, arrays and dataframes, data analysis, data visualization, linear regression, and machine learning. Python and its libraries are also free to download. Thanks to their being open-source, there is also a wealth of documentation and free training materials to help beginners start learning Python. 

Although Python is considered a very beginner-friendly programming language, it can be challenging for those without prior programming experience. Instructor-led classes like Noble Desktop’s Python for Data Science Bootcamp cover fundamental Python programming skills with emphasis on data science applications.

Python has so many uses that it even forms the foundation of certain careers. Noble Desktop’s Python Developer Certificate program prepares students for a career in Python development. This live online program covers Python programming fundamentals and Python web development with Django. 

What Do I Need to Know to Learn Python for Machine Learning?

Before tackling Python for machine learning, you must thoroughly understand Python programming fundamentals. You can explore some free classes online to gain a high-level overview of the programming language, but mastering Python requires hands-on experience. In-person and live online Python training like Noble Desktop’s Python for Data Science Bootcamp teaches fundamental Python programming skills through a combined learning approach. An expert instructor guides the class through hands-on activities in real-time, allowing students to collaborate, receive personalized feedback, and have questions answered instantly. 

You can also choose to take a class that starts off with Python programming fundamentals and then advances to Python for machine learning. Noble’s Python for Data Science & Machine Learning Bootcamp covers Python programming fundamentals and Python for machine learning, automation, and data visualization. 

If you want to learn even more about data science or start a career as a Data Scientist, you can enroll in Noble’s Data Science Certificate program. Certificate programs teach a wealth of data science knowledge in a single course, which streamlines your data science training. Noble’s program includes 1-on-1 mentorship, flexible payment plans, a free retake, and job search assistance. Job preparation, such as resume and portfolio reviews, along with personalized feedback from your mentor, can make all the difference when you go to find your first data science job. 

How to Start Learning 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. 

Read the full guide on how to learn Python for machine learning.

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 for machine learning can transform the way your organization works, help plan for the future, and keep you several steps ahead of the competition. 
• Learning Python can also prepare you for a career as a Python Developer or Data Scientists. 
• Python has been a free, open-source, general-purpose programming language for more than 30 years. Its long history means it enjoys an active community of developers and a wealth of documentation on its many uses.
• Python libraries are often used for machine learning and are free and open-source. 
• Python is a beginner-friendly programming language but can be challenging for those without a programming background.
• You can receive comprehensive Python for machine learning training through an in-person or live online course with Noble Desktop.