Machine learning (ML) is rapidly transforming industries, such as healthcare, finance, and marketing, with its ability to automate processes and predict trends. We can unlock new efficiencies with machine learning, making it one of the most sought-after skills in today's technology-driven landscape.

For students new to this exciting field, obtaining a certification offers a structured and hands-on way to explore ML and its applications. These certifications help you understand ML fundamentals, build real-world models, and gain valuable experience in coding, data analysis, and problem-solving.

Getting certified in ML can benefit students in several ways. First, it strengthens college applications by setting you apart from other applicants in a competitive environment, demonstrating your initiative to learn cutting-edge technology. Second, these certifications enhance technical skills, whether you're a beginner or have some programming background. ML courses often use popular languages like Python, R, and TensorFlow, which are in high demand across industries. Third, ML certifications allow you to explore your interest in artificial intelligence (AI), as ML is a core component of AI that includes neural networks, deep learning, and data science.

Many available certifications are designed specifically for beginners, eliminating the need for prior experience in coding or data science. These programs often allow you to progress at your own pace, offering interactive lessons, real-world projects, and supportive communities that help you apply what you've learned to practical challenges while building a strong foundational understanding of the field. 

In this article, we’ve compiled a list of 10 beginner-friendly machine learning certifications that will help you build a solid foundation, gain hands-on experience, and enhance your credentials as you prepare for the future of AI and ML.

 

1. Google’s Machine Learning Crash Course

Cost: Free

Eligibility: Open to everyone; however, applicants should ideally have some Python experience or be familiar with concepts like variables, linear equations, etc. See more info here. 

Application Deadline: Open enrollment

Program Dates: Self-paced

Google’s Machine Learning Crash Course (MLCC) is designed to provide a swift yet comprehensive introduction to machine learning principles. The course is divided into 25 lessons, each featuring video lectures, real-world case studies, and hands-on exercises. Key topics include supervised learning, unsupervised learning, neural networks, and TensorFlow basics. As a participant, you will engage in interactive visualizations and practical coding exercises using TensorFlow. This will allow you to build and train simple ML models. 

The estimated time commitment for this course is 15-20 hours. It is highly accessible and offers practical experience with one of the most popular ML libraries, making it perfect for students who want to gain foundational ML knowledge.

 

2. IBM: AI Engineering Professional Certificate

Cost: Fee varies based on how long you take the course (financial aid available)

Eligibility: Open to  participants with experience in: 

• Python, Data Analysis, Visualization techniques 

• High school math 

• The Fundamentals of Generative AI

Application Deadline: Rolling

Program Dates: Self-paced | 1-6 months

The IBM AI Engineering Professional Certificate comprises six comprehensive courses covering essential ML and AI topics. You will explore supervised and unsupervised learning, deep learning, and AI applications using Python as a participant. The curriculum includes modules on neural networks, natural language processing, and reinforcement learning. Each course features a mix of video lectures, quizzes, and hands-on projects using tools like TensorFlow, Keras, and PyTorch. The final capstone project allows students to apply their knowledge to a real-world problem, showcasing their skills upon completion. 

IBM’s structured approach and comprehensive coverage provide a deep dive into AI engineering, making it ideal for students eager to advance their technical prowess. The entire certificate can be completed in approximately six months, with flexible pacing to accommodate different learning speeds. 

 

3. IBM: Machine Learning with Python

Cost: Free (paid upgrade available with certificate)

Eligibility: Open to all; however, knowing basic Python for data science is recommended. 

Application Deadline: Rolling

Program Dates: Self-paced | 5 weeks 

Offered by IBM on EdX, the Machine Learning with Python course is tailored for beginners seeking practical ML experience. The program covers fundamental algorithms such as regression, classification, clustering, and dimensionality reduction. During this program, you will learn to utilize Python libraries like NumPy, pandas, and scikit-learn through hands-on projects that involve real-world datasets. Each module includes interactive assignments and assessments to reinforce learning. 

The course spans approximately five weeks, with a recommended commitment of 2-4 hours per week, allowing you to progress at your own pace while gaining valuable ML insights. It is ideal for students who wish to develop both coding and machine learning skills simultaneously.

4. Stanford University’s Supervised Machine Learning

Cost: Free (paid upgrade available with certificate)

Eligibility: Open to all; however, it is recommended participants have experience in: 

• Basic coding (for loops, functions, if/else statements) 

• High school-level math (arithmetic, algebra)

Application Deadline: Rolling

Program Dates: Self-paced | 1-6 months

Taught by the esteemed Professor Andrew Ng, Stanford University’s Machine Learning course on Coursera is one of the most renowned ML courses. The program delves into a wide array of topics, including linear and logistic regression, support vector machines, neural networks, and unsupervised learning techniques. Each week comprises video lectures, reading materials, and programming assignments using MATLAB or Octave. The course also includes quizzes and a final project to apply learned concepts. 

The course’s comprehensive curriculum and association with Stanford provide a strong academic foundation and will enhance your resume and college applications. With over 60 hours of content, it offers an in-depth understanding of machine learning foundations, making it suitable for students who are committed to thoroughly exploring the subject.

 

5. DeepLearning.AI: AI for Everyone Certificate Course

Cost: Free

Eligibility: Open to all high school students

Application Deadline: No deadline

Program Dates: Self-paced | 2-3 hours/week for 3 weeks

DeepLearning.AI: AI for Everyone Certificate Course is a great introduction to AI and its limitations. This course teaches concepts such as neural networks, machine learning, deep learning, and data science. 

Over 3 weeks (or more), you will have hands-on experience, learning about what it’s like to build machine learning and data science projects. You’ll also learn about how to work with an AI team and how to build an AI strategy within a company, which will prove useful in your future career. This course will also give you the chance to navigate the ethical and societal discussions surrounding AI.  

 

6. Harvard’s Introduction to AI with Python

Cost: Free (verified certificate available after purchase)

Eligibility: Open to applicants who have at least one year of experience with Python

Application Deadline: Rolling 

Program Dates: 7 weeks

Harvard’s Introduction to AI with Python is part of the renowned CS50 series, offering a beginner-friendly approach to AI and machine learning. The course covers essential topics such as search algorithms, optimization techniques, and natural language processing, all implemented using Python. As a participant, you will engage in weekly problem sets that involve coding assignments and projects, allowing you to apply theoretical concepts to practical scenarios. The final project will require you to design and implement your own AI application, fostering creativity and technical proficiency. 

The program is self-paced, typically taking around 12 weeks with a recommended commitment of 6-8 hours per week. It will equip you with the knowledge and practical skills needed to excel in AI and ML.

  

7. University of London: Machine Learning for All

Cost: Free

Eligibility: Open to all high school students

Application Deadline: No deadline

Program Dates: Self-paced

The Machine Learning for All course is great for beginners to machine learning as it aims to help students understand the basic idea of machine learning, even without a background in math or programming. During the course, you will get to explore the user-friendly tools developed at Goldsmiths, University of London, and gain hands-on learning experience via a machine learning project—training a computer to recognize images.

Please keep in mind that this course aims to teach you about machine learning without you needing programming knowledge. This means it won’t cover programming-based tools like Python or TensorFlow. 

 

8. Udemy’s Machine Learning A-Z™: Hands-On Python & R in Data Science

Cost: Approximately $20 (pricing varies)

Eligibility: Open to all; it is recommended that participants have some knowledge of high school maths.

Application Deadline: Rolling

Program Dates: Self-paced 

Udemy’s Machine Learning A-Z™: Hands-On Python & R in Data Science is a highly-rated, project-based course that introduces students to machine learning using both Python and R programming languages. The course is structured into multiple sections, each focusing on different ML algorithms such as linear regression, logistic regression, decision trees, and deep learning. You will get to work on real-world datasets, implementing and evaluating various models to solve practical problems. 

The course includes video lectures, coding exercises, and quizzes to reinforce learning. Lifetime access to the course materials allows students to learn at their own pace and revisit content as needed. Its affordable pricing, comprehensive coverage of both Python and R, and practical focus make it an excellent choice for students aiming to apply ML concepts to real-world projects.

 

9. Fast.ai Practical Deep Learning for Coders

Cost: Free

Eligibility: Basic programming experience is recommended

Application Deadline: No deadline

Program Dates: Self-paced

Fast.ai’s Practical Deep Learning for Coders is an intensive course designed to equip students with the skills to build and deploy deep learning models efficiently. The curriculum emphasizes practical coding over theoretical concepts, allowing you to rapidly implement advanced models using the PyTorch library. Topics covered include convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transfer learning. Each lesson includes coding exercises and projects that encourage you to experiment and apply your knowledge to real-world scenarios. 

The course is self-paced, typically taking around 7 weeks to complete with a recommended commitment of 10-15 hours per week. Although it requires some prior programming experience, its hands-on approach and focus on practical applications make it accessible for motivated beginners eager to dive into deep learning.

 

10. Microsoft Azure Machine Learning

Cost: Free 

Eligibility: Open to all students; however, having general programming knowledge is recommended.

Application Deadline: No deadline

Program Dates: Self-paced

The Microsoft Azure Machine Learning course is designed to teach you how to use Azure Machine Learning to create and publish models without writing code. While the course is open to everyone, it does require you to be familiar with basic computing concepts and terminology. This course is also a part of other programs that explore Microsoft Azure AI Fundamentals. 

During this course, you will learn more about the capabilities of no-code machine learning with Azure Machine Learning Studio. You will also learn more about important machine-learning concepts and how to go about identifying tasks when creating a machine-learning solution. 

Conclusion

Embarking on the journey to master machine learning as a high school student can set you apart in both academic and future career pursuits. These certifications offer structured learning paths, practical experience, and recognized credentials that enhance your resume and college applications. By investing time in these programs, you’ll not only gain valuable skills but also demonstrate your commitment to understanding and applying machine learning in real-world scenarios. 

 

Are you looking to start a project or research paper in the field of AI and ML? Consider applying to Veritas AI!

Veritas AI is an AI program designed for high schoolers. It’s founded and run by Harvard graduate students. The program aims to allow students to create unique projects in the field of AI. Participants will get to learn more about AI from researchers and experts and work 1-on-1 with mentors from Harvard, MIT, Stanford, and more. In just the past year, we’ve had over a thousand students learn with us! You too can apply!

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