Explore the fundamentals of machine learning with Python, gaining experience training models, interpreting data, and automating intelligent predictions.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Machine learning (ML) is the science of getting computers to act without being explicitly programmed once initiated. Machine learning is so pervasive that it’s probably used throughout most people’s days without them even realizing it. In just the past few years, our society has seen the rise of self-driving cars, practical speech recognition, vastly improved web searching, and a better understanding of the human genome. When computers can use past data and inputs from programmers to begin to perform appropriate pre-planned actions on their own, then machine learning has been successful.
Machine learning is a subset of artificial intelligence, with computers taking the directions they’ve previously been given once and combining them with data analysis to predict what should be done next. The result can be seen in everyday applications such as movie recommendations, music playlists, and products suggested based on past preferences.
The level of complexity involved in machine learning projects depends on the task and the algorithm needed to accomplish it. Regardless of the scope of a task, a machine learning model is a computer looking at data and identifying patterns, then putting those insights to work to better complete the goal.
There are advantages to the advancements machine learning offers to the workplace. One example is how machine learning handles intelligent big data management. The sheer volume and variety of data that floods companies every minute of the day would be impossible to handle solely through human computation. The way machines can interact with technology to process and draw insights through machine learning makes doing business at an optimal level possible.
Above all, data science is one of the areas most connected with machine learning. It is imperative that those in data-rich fields, including FinTech, computer science, cybersecurity, ecommerce, and similar areas, can wrangle the amount of data that would be overwhelming to humans alone. Machine learning in data science fields is an essential part of the foundation for creating meaningful data-backed results. For those who work in any big-data realm, machine learning is now the cornerstone of what they do.
Phoenix has become a growing hub for AI and machine learning, especially within the tech-services sector. Firms like qBotica — a Phoenix-based AI/ML company — build intelligent automation, data processing, and ML-driven enterprise solutions for clients across healthcare, supply chain, retail, and more. Meanwhile, companies like SkyTech Mobile provide mobile and web app development, embedding machine-learning capabilities for custom business applications. This local demand has created opportunities for ML Engineers, Data Scientists, and AI-infrastructure specialists in Phoenix, helping businesses leverage predictive analytics, automation, and smarter decision-making.
Healthcare and health-tech in Phoenix also rely heavily on ML and data science. For example, Datavant — headquartered in Phoenix — works in health information technology, managing large datasets and enabling data exchange and analytics for clinical research, hospitals, and providers. Additionally, other regional health-tech startups apply machine learning to telemedicine, diagnostics, and patient data analysis, demonstrating how ML can improve patient care, streamline operations, and support research across Arizona’s growing healthcare ecosystem.
Beyond stand-alone tech firms and healthcare, Phoenix’s broader industries — including finance, logistics, retail, and real estate — are beginning to adopt ML-driven platforms to optimize operations and services. Local consultancies (and development firms) tailor AI and ML solutions for tasks like demand forecasting, risk assessment, automation, and natural language processing — enabling businesses in many sectors to modernize.
A resident can still find quiet neighborhoods in Phoenix, with reasonable housing prices. Many communities boast a higher-than-average number of charter or specialty schools, especially in STEM, with pocket parks sprinkled throughout that make family life exceptional. The median home price in the area is $320,000, which is around the typical amount for a well-appointed single-family house in an attractive city. The area is extremely welcoming for those who like to be active, with a balance of outdoor activities, but also places a large emphasis on what can be done indoors when the temperatures heat up.
Typical jobs associated with machine learning include machine learning engineering, data science, human-centered machine learning design, computational linguistics, and software development. Any task that relies upon a set of data points or rules can be automated using machine learning, even things such as responding to customer service calls and reviewing resumes.
There is a vast range of job opportunities available to professionals with skills in machine learning. Besides industry-specific knowledge or experience, traits that those in roles involving machine learning tend to share include expertise in applied mathematics, physics, data modeling and evaluation, natural language processes, and reinforcement learning. It is a field that can be difficult to understand as it evolves and grows rapidly, so those in ML professions must be willing to keep up with advancements and changes.
Salaries for those in machine learning are substantial, making their money go further. Examples include earnings for Machine Learning Engineers, who earn around $64,000 per year and increase with experience and training, and Data Engineers, who average about $98,000 annually.
Machine learning expertise raises a tech professional’s career to amazing heights. Having the ability to learn machine learning through live, online classes offers direct machine learning knowledge for data science, finance, cybersecurity, and other data-related careers. Live virtual courses allow students to have the freedom of choosing where they’d like to take their courses, perhaps at home or the office. Having a space that is distraction-free when taking immersive Python bootcamps is an ideal option. Students even have the option of sharing their computer screen with the instructor for extra assistance.
An understanding of data science is necessary for a career in machine learning. Noble Desktop offers a Data Science Certificate that allows students an opportunity to gain all the information they need to move into this lucrative career. In this program, students discover how to analyze tabular data with NumPy and Pandas, create graphs and visualizations with Matplotlib, make predictions with linear regression, and apply machine learning algorithms to data. Additionally, students will learn how to clean and balance data in Pandas, evaluate the performance of machine learning models, combine information across tables with join statements, and explore advanced techniques such as subqueries and stored procedures.
The Python for Machine Learning bootcamp expands students’ skills in Python to encompass a comprehensive understanding of machine learning and algorithms that can independently learn patterns and make decisions. Students start by learning linear and logistic regressions and move forward with algorithms with different theoretical bases, such as k-nearest neighbors, decision trees, and random forests. Additionally, students gain abilities in statistical concepts such as bias, variance, and overfitting, as well as how to measure the accuracy of models and tips for choosing effective features and algorithms.
Noble’s Python Programming Certificate for Data Science and Machine Learning teaches the essential programming skills to manipulate databases and perform various levels of analysis on the data to be able to enter the workforce upon completion. Students learn how to use NumPy, Pandas, and Matplotlib to analyze data and create predictive models from the data using machine learning packages such as scikit-learn. Additionally, students learn how to read and write complex queries in a database, which is a necessary skill since a significant amount of daily effort in the workplace includes spending time cleaning data so that it can be imported and analyzed in Python.
There are other options for exploring topics that involve machine learning: NYIM Training and NYC Career Centers. These schools offer programs in data science, Python, and machine learning.
An opportunity for high school students to get ahead and prepare for a career that will continue to skyrocket is to learn about machine learning in NextGen Bootcamp’s Summer Data Science class for high school students. These comprehensive bootcamps provide skills that students will be able to apply immediately in the real world upon completion. What students master in these bootcamps is an invaluable investment in their future.
Artificial intelligence is one of the fastest-developing areas in technology today. It makes sense to boost your team’s abilities in the field by providing a focused look at machine learning by investing in an onsite corporate machine learning training session. Noble Desktop, the creator of this tool, will send an expert instructor to the workplace to provide training, or a live video conferencing session can be arranged. If it is more convenient for employees to attend sessions on their schedules, vouchers can be purchased for them to enroll in a public open enrollment session. There are discounts on the purchase of multiple vouchers. Contact Noble Desktop for more information.
Phoenix professionals have a lot of opportunities when it comes to expanding their knowledge in fields like tech, design, business, and data. Anchored by major businesses in downtown Phoenix and the surrounding areas, such as Tempe, Mesa, and Scottsdale, the city benefits greatly from a diverse economy that spans industries ranging from local government and education to tech and finance, all while maintaining strong connections to the global market. As such, skills like machine learning are especially desirable for Phoenix-based employers who need professionals capable of building intelligent models that can enhance productivity and streamline workflows. Through Noble Desktop’s network of professional instructors, students in Phoenix can access high-quality training that helps them meet the industry standard. With decades of combined experience, these instructors go beyond the basics and blend practical guidance with real-world insights. This helps students not only acquire technical skills but also the confidence to make it in their professional careers.
Dr. Deanne Larson is a data science and analytics expert with 20 years of experience and counting. She has worked as an advisor for Fortune 500 companies and FinTech organizations through her firm, Larson & Associates LLC. Throughout her career, she has been drawn to work with AI-driven strategies and initiatives, machine learning implementation, and customer data platforms. Additionally, Dr. Deanne has worked on fraud detection models and managed global personalization platforms that have served hundreds of millions of customers. She is also a knowledgeable instructor, having worked for City University of Seattle, where she mentored doctoral students and developed curricula in data and business analytics. Dr. Deanne currently teaches courses in subjects ranging from data and project management to AI and Python.
After 34 years of federal service, Geno Sexton retired in 2012 and now works as a Phoenix-based project management instructor. Throughout these decades, Geno worked for eight years in the Senior Executive Service, culminating as Deputy Chief Human Capital Officer and HR Director at the U.S. Department of Labor, where he led 120 HR professionals serving 18,000 employees. He was named Deputy CHCO of the Year in 2011 and served as the first HR Officer at the new DHS Headquarters. In his distinguished career, Geno has made a real impact and currently teaches project management courses. He is a Vietnam-era Army veteran, holds a Bachelor’s degree in Journalism and Mass Communications and a Master’s degree in Government from New Mexico State University, and is a proud bilingual and ESL-certified instructor.