There are classes for all age groups, both in-person and online, so you (or your children) can deepen your understanding of machine learning and prepare for a high-paying career in the field.
Key Takeaways
- Machine learning involves using data and algorithms to enable AI to imitate how humans learn.
- Classes for machine learning are available for young children, teens, college students, and adults that wish to enhance their technical skills and creative potential.
- Noble Desktop offers a Data Science Certificate program that teaches the fundamentals of both machine learning and programming.
- Other institutions like Harvard and Stanford offer equally worthy courses that can be attended from the comfort of your own home.
- High school students can start their journey towards a career in machine learning with courses from NextGen Bootcamp.
- Even elementary-aged children can learn the basics of machine learning with the help of courses from companies like Outschool and Create & Learn.
- Summer can be an ideal time to study machine learning, thanks to more available free time for teens, college students, and even adult professionals.
- You will want to consider what type of class (in-person or online) and what subjects you wish to study so that you can pick the ideal course for you.
Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable AI to imitate how humans learn, gradually improving its accuracy. More specifically, machine learning uses neural networks and deep learning to train algorithms on sets of data to achieve an expected outcome. The more data a machine learning algorithm consumes, the better it gets at finding trends and patterns in that data.
There are many types of machine learning. Supervised learning uses labeled datasets to train algorithms to predict outcomes and recognize patterns. An algorithm may be fed images of birds that include tags for each species of bird so that it will learn to properly identify the bird when fed a new photograph. Reinforcement learning trains software to make decisions that achieve optimal results by mimicking the trial-and-error process that humans use. Unsupervised learning learns from data without human supervision. Models are given unlabeled data and allowed to discover patterns and insights without explicit guidance or instruction.
As a machine learning expert, you will research, build, and design the artificial intelligence responsible for machine learning, and maintain and improve existing artificial intelligence systems. An important communicator between other data science team members, you will work directly with the data scientists who develop the models for building AI systems and the people who construct and run them. Machine learning can be a very rewarding and lucrative field, so check out the below classes to get started.
For Adults & College Students
There is a high demand for machine learning experts across a variety of industries, so there are plenty of career opportunities and even long-term career stability. And as companies continue to shift more and more towards a digital-first approach, highly trained teams of Machine Learning Engineers will be even more essential. Plus, the average salary is $128,000 - $165,000, well above the national average. As a Machine Learning Engineer, help further develop one of the most valuable and versatile tools. Below are a handful of classes that can help you master machine learning this summer.
Noble Desktop
Python Data Science & Machine Learning Bootcamp
This in-depth course starts with learning Python programming fundamentals and using Numpy, Pandas, and Matplotlib to analyze data. You will then create predictive models from the data using machine learning packages, such as Sci-Kit Learn. You will also use Python to automate daily tasks like aggregating, updating, and formatting data. Finally, you will use Matplotlib, Seaborn, Plotly, and Dash Enterprise to create data visualizations and interactive dashboards that you can deploy to GitHub, where they will be easily accessible for potential employers to review. This course comes with setup assistance, 1:1 mentoring, and a free retake, should you need to revisit any content. Payment plans are available to make this training more accessible.
Data Science Certificate
This certificate program can help you develop complementary skills and further your machine learning expertise. Encompassing all of data science, you’ll learn how to manipulate databases and perform data analysis, preparing you for entry-level data science and Python engineering positions. You’ll learn to read and write complex database queries, prepare and clean data for analysis, and use Python to automate everyday tasks such as aggregating, updating, and formatting data. You’ll also master machine learning models by cleaning and balancing your data with Pandas, applying machine learning algorithms with the scikit-learn library to solve real-world problems, and analyzing the results to detect areas for improvement. Real-world examples help you apply these skills and prepare you for entering the workforce.
Harvard University
Data Science: Machine Learning
Part of Harvard’s Professional Certificate Program in Data Science, this course teaches popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will master training algorithms using training data, allowing you to predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. Ideal for beginners, this online course offers the ultimate learning flexibility with its self-paced format.
Stanford University
Machine Learning
This live online course provides a broad introduction to machine learning and statistical pattern recognition. You will learn about supervised and unsupervised learning, learning theory, and reinforcement learning and control. This class also explores recent applications of machine learning and design, helping you gain experience developing algorithms for machines. This course requires students to have a bachelor’s degree with an undergraduate GPA of 3.0 or better, the ability to write a non-trivial computer program in Python/NumPy (CS106B) or equivalent, and mastery of probability theory (CS109 or STATS116) and multivariable calculus and linear algebra (MATH51).
For High School Students & Teenagers
Deciding on a field of study and preparing for college can be overwhelming. A course in machine learning can help expose you to one potential field that can be fulfilling, challenging, and lucrative. Studying machine learning can help students develop in-demand skills that can be used in many industries. Machine learning is multidisciplinary, and can involve a variety of roles, such as computer science, robotics and cognitive science. Plus, machine learning also involves transferable skills like adaptability and continuous learning, critical thinking and problem-solving.
NextGen
Computer Science Summer Certificate Program
Held live online, this summer certificate program teaches everything from the basics of programming to more advanced programming and data science using Java and Python. Java and Python are two of the most popular languages taught at high schools and universities, so mastering them can help students develop the most in-demand skills for today’s workforce. You’ll work on real-world projects in small class sizes, offering you a more intimate learning environment and the chance for 1:1 attention from an expert instructor.
Python Data Science & AI Machine Learning
Held live online, this hands-on Python course begins with the fundamentals of Python code and then transitions into more complicated programming tasks. The course also focuses on data science and teaches you to use Pandas, Matplotlib, and Sci-Kit to input, analyze, and graph data. Ideal for high school and college students with a strong interest in coding, prior coding/programming experience is not required, but students must be comfortable with computer basics.
Java Programming Summer Program
This is a beginner course that starts with the basics of Java before moving on to high-level programming topics like object-oriented programming and recursion. Plus, you will also gain a head start for AP Computer Science as this class teaches all the topics covered on the AP exam. You’ll showcase all you’ve learned by creating an original portfolio-ready product using the Java language. This course will provide you with a strong programming foundation for future machine learning work.
FinTech Summer Program
This course is a combo of financing, stock market investing, and Python programming, skills that are extremely useful for machine learning. Students will start with mastering Microsoft Excel, an essential business software, while conquering finance and business concepts. You’ll get familiar with data entry, charts, and essential shortcuts before moving on to Pivot Tables and VLOOKUP. You’ll also learn to automate your workflow by recording macros in Advanced Excel.
The second part of the training focuses on the fundamentals of Python code and more complicated programming tasks. You'll learn data science concepts using Pandas, Matplotlib, and Sci-Kit learn, including how to input, analyze, and graph data.
NYU
Summer Program for Machine Learning
This in-person training introduces high school students to the computer science, data analyses, mathematical techniques, and logic that drive the fields of machine learning (ML) and artificial intelligence (AI). Topics include fundamental knowledge behind video and image recognition technologies; interactive voice controls for homes; autonomous vehicles; real-time monitoring and traffic control; current diagnostic medical technologies, and other technologies that are a part of our daily lives. You’ll learn model development through cross validation, linear regressions, and neural networks, and will also develop an understanding of how logic and mathematics are applied both to "teach" a computer to perform specific tasks on its own and to improve continuously at doing so along the way. A strong emphasis is put on learning the principles of engineering problem solving, and how these techniques can solve societal challenges.You should have successfully completed Algebra 2 or equivalent and have had some programming experience in any language prior to attending this course.
Teens in AI
Understanding AI
This beginner-friendly course is open to youth 11-19 years old. You’ll explore AI and various machine learning in several ways. You’ll learn to define real-world applications of AI and ML, build a face-sensing project with Scratch using computer vision, train a Machine Learning Model to recognise and identify objects in images using Google Teachable Machine, and create a Voice Recognition Model with MIT App Inventor.
Image Processing in AI
Teens in AI also offers intermediate courses for youth. This course will cover the foundations of artificial neural networks and how to apply them in image processing. Students will build a deep learning model that can be used to filter and denoise images. You’ll also get introduced to various methods for image de-noising and apply Encoder-Decoders, and use applied mathematics skills such as probability and statistics to solve real problems.
For Kids & Preteens
Calling all problem-solving kids! Machine learning lets you flex your problem-solving skills by working with different teams, various goals, and evolving best practices, finding innovative ways to make them all work together seamlessly. Plus, design projects are an opportunity for kids to build confidence, express creativity, and learn a marketable skill that could lead to a fulfilling future.
Outschool
Coding Artificial Intelligence & Platformers on Scratch
In this exciting coding class, students will learn about artificial intelligence concepts, creating games where users play against the computer, and using gravity for designing platformer games. Students will learn how to code two different games on Scratch using AI concepts, code a cool platformer, learn about booleans and logic, and code a cool self-driving car.This course is offered to students aged 7-12 and be completed on a self-paced schedule.
Create & Learn
Python for AI
This live online class covers the fundamentals of Python coding for kids and teenagers and places a strong emphasis on the elements of Python most relevant to Artificial Intelligence, like data structures and libraries. Students will learn how to animate and draw with Python, and will build their own story project to create a cool animation. The first part of the course covers core concepts of Python syntax, loops, data types, and variables. The second half of the course explores more advanced elements like dictionaries and files, and how to employ Python’s powerful modules to build games, stories, and real-world data projects. Finally you’ll learn data structures such as 2D arrays, as well as using new modules and the API for an AI system.
AI Explorers Class
Designed for kids grades 4-7, this course offers hands-on experience with cutting-edge artificial intelligence and machine learning products. You’ll learn how AI works and real world AI applications, understand what AI can and can not do, and build your own AI system. You’ll also master cutting-edge AI applications for image recognition, chatbots, and machine learning, all in an age-appropriate way. Finally, you’ll build a small image recognition program in the class. Small class sizes, world-class curriculum, and team projects, allow ample opportunities to exercise your creativity, critical thinking, and communication skills.
Why Summer is the Perfect Time to Learn Machine Learning
Summer is a good time to learn machine learning for a variety of reasons. For students, summer offers a break from their regular schooling, providing more time to focus on new skills. Parents may find that their kids need focused activities over the summer to stay motivated (and out of trouble) and an in-person program is extra helpful for those that cannot stay home through the summer months with their children.
High school students can use a summer course to test out potential fields of study for college. Being able to pinpoint their interests can set them on an expedited track for collegiate and career success. Additionally, studying machine learning offers prospective college students and soon-to-be professionals valuable life skills. Strategy, problem solving, critical thinking, collaboration, and communication are necessary traits for both machine learning experts and life in general. Learning in a structured environment like a summer course will provide them with ample opportunity to practice and further develop these skills.
For professionals, summers often result in slower recruiting practices as people go on vacation and offices shift to summer schedules. This slower time gives you the chance to network, get educated, and improve your design skills. Online courses can be a great way to learn about the latest tools, trends, methods, and techniques. And, with many of these classes taught by instructors who work during the school semester for universities, there may be more options available to choose from over the summer months.
How to Choose the Right Summer Machine Learning Course
There are several factors to consider when choosing a summer machine learning course. The first factor you’ll want to consider is what learning environment is best suited for you. Classes are offered in three main ways: in-person, live online, or virtually on demand. Do you thrive in a collaborative environment where you can interact with your peers and receive in-the-moment feedback from your instructor? Or, do you prefer a more solitary learning environment, perhaps one where you can learn at your own speed? Do you live in a bustling city with ample in-person classes, or are you located in a more rural region? Your answers to these questions will help you choose the right offering for you.
Summer machine learning programs will also vary in length. You’ll want to consider how much time you can commit to your education and what level of flexibility your schedule requires. Are you looking to immerse yourself in an intensive program like a bootcamp? Do you work full-time and need your classes to be scheduled for evening and weekend hours? Programs can vary from a few days to a couple months, so you’ll need to decide if you can only take a week away or can commit your entire summer to learning.
In-person training offers face-to-face interaction with both your fellow students and your expert instructor. This collaborative environment is available for both full-time and part-time classes and provides structure and accountability, two things that can help you succeed. Online courses may lack this in-person interaction, but they make up for it with increased flexibility. Live online classes still offer instant feedback from your instructor and interactions with other students, while on-demand virtual courses allow for the most flexibility since you can decide where and when you learn.
It’s important to consider which subjects you wish to study as well. Machine learning encompasses many things and each course may differ slightly in what it includes. For instance, there are many essential tools used by machine learning experts. You may want to choose specific ones to learn, like Microsoft Azure, Tensor Flow, and OpenNN. These are only a sampling of tools available, so make sure you choose a course that covers the ones you wish to master.
The good news is that, no matter which option you choose, you can obtain a quality education with a top-notch curriculum and expert instructors.