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Top 4 AI Bootcamps: Options, Costs, and Alternatives

Discover the best AI bootcamps to accelerate your career in artificial intelligence. Explore top programs and learn about DataCamp's flexible alternatives.
Jun 2024  · 9 min read

An AI bootcamp is an intensive, short-term educational program designed to equip individuals with the skills they need to enter or advance in the field of AI. Unlike traditional degree programs that may take years to complete, bootcamps are typically designed as accelerated learning paths. The goal is to provide a fast-paced, focused curriculum that covers essential AI topics and tools, enabling participants to quickly gain practical skills.

If you're considering enrolling in a machine learning bootcamp, this guide is for you. In the following sections, I will provide a comprehensive overview of AI bootcamps, covering the most frequently asked questions and advancing a list of top machine learning bootcamps, as well as some top machine learning bootcamp alternatives offered on DataCamp.

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The 4 Best AI Bootcamps

Below, we’ve outlined a list of AI bootcamps and bootcamp-like options that offer high-quality curriculums and learning experiences:

1. Clarusway Machine Learning Course 

Clarusway’s machine learning program empowers students to become versatile AI professionals. You’ll not only master popular machine learning algorithms and frameworks with Python, but also delve into the fascinating world of Generative AI.

In addition to the online curriculum, you will have the opportunity to practice what you’ve learned with +10 hands-on projects and +3 capstone projects at the end of the course.

Who it's for: Suitable for those with a solid background in data analysis with Python, who can commit full-time for 13 weeks.

What you'll learn: The Machine Learning Program is meant to equip you with the advanced skills necessary to pursue a career as a machine learning engineer. 

Cost: $6,000, with flexible financing options. 

Length:  13 weeks

2. UC San Diego ML Engineering & AI Bootcamp

In this online, 9-month program, you will gain the skills and knowledge needed to excel in the exciting field of machine learning and AI. The course is designed to take you from a beginner to a confident practitioner. You'll start with an introduction to basic ML algorithms and quickly advance to topics like deep learning, large language models, and generative AI, as well as AI ethics.

The program comes with hands-on projects, a capstone project and human support throughout the course, including 1:1 mentorship, mentor calls and career services.

Who it's for: Suitable for those with a solid background in object-oriented programming (prospective students will complete a technical skills survey during the application process) who can commit at least 15 hours per week.

What you'll learn: The Machine Learning & AI Program is meant to equip you with the advanced skills necessary to pursue a career as a machine learning engineer. 

Cost: $13,950, with flexible financing options and scholarships

Length: 9 months.

3. 4Geeks Academy Applied Artificial Intelligence Course

This comprehensive Applied AI Course is tailored for professionals eager to leverage the power of machine learning and generative AI within their current roles. Over six dynamic weeks, you’ll gain a deep understanding of AI and its real-world applications. From understanding the various types of generative AI to mastering key elements like context, prompts, and models, this course will equip you with essential tools and best practices to optimize AI integration.

The program comes with a range of hands-on projects and interactive sessions, and can be done online, hybrid or in person. It includes lifelong coding mentorship, unlimited career support, and a job guarantee within 6 months.

Who it's for: Everyone can access this bootcamp, as no previous experience is required. 

What you'll learn: AI fundamentals and a deep dive into generative AI and how to leverage its power. 

Cost: $3,500, with flexible financing options and scholarships

Length: 6 weeks.

4. Springboard Machine Learning & AI Bootcamp

Springboard Machine Learning & AI Bootcamp is designed for those interested in becoming machine learning engineers. Developed in partnership with the University of Massachusetts Global, this online, 9-month program is structured to take you from a beginner to a confident practitioner in the field of machine learning. You'll embark on your journey with an introduction to fundamental ML algorithms and quickly progress to cutting-edge topics like large language models and generative AI.

The program comes with regular 1:1 guidance from an industry mentor, hands-on mini-projects and a capstone project, and career support services. 

Who it's for: Suitable for those with a solid background in data analysis with Python, who can commit 15-20 hours per week.

What you'll learn: The skills to become a solid confident. learning engineers

Cost: $9,000, with flexible financing options and scholarships

Length. 9 months.

Bootcamps compared

We’ve collated the information about the top AI bootcamps into a table for quick reference: 

Course Program Focus Duration Time Commitment Cost Financing Options Background Required Hands-On Projects Career Support Learning Goals Format
Clarusway Machine Learning Course Versatile AI professionals, ML algorithms, frameworks, Generative AI 13 weeks Full-time $6,000 Yes Solid background in data analysis with Python +10 hands-on projects, +3 capstone projects 54+ hours of Career Management Services, including life coaching, resume building, LinkedIn training, interview prep Advanced skills for a career as a machine learning engineer Online
UC San Diego ML Engineering & AI Bootcamp ML and AI skills from beginner to practitioner, AI ethics 9 months 15 hours/week $13,950 Yes Solid background in object-oriented programming Hands-on projects, capstone project Career services, 1:1 mentorship, mentor calls Advanced skills for a career as a machine learning engineer Online
4Geeks Academy Applied Artificial Intelligence Course Applied AI, generative AI integration 6 weeks Flexible $3,500 Yes No previous experience required Range of hands-on projects Lifelong coding mentorship, unlimited career support, job guarantee within 6 months AI fundamentals, generative AI, real-world applications Online, hybrid, or in person
Springboard Machine Learning & AI Bootcamp Machine learning engineers, ML algorithms, large language models, generative AI 9 months 15-20 hours/week $9,000 Yes Solid background in data analysis with Python Hands-on mini-projects, capstone project Regular 1:1 guidance from industry mentor, career support services Skills to become a solid and confident machine learning engineer Online

Why Choose an AI Bootcamp?

There are many reasons why people choose an AI bootcamp over a traditional degree program:

  • Fast certification. Bootcamps students can obtain a professional certification in a short amount of time, typically in a matter of weeks or months.
  • Career support. Bootcamps offer career services such as resume assistance, mock interviews, networking events, and partnerships with employers to help during job search.
  • Flexible schedules. Bootcamps offer flexible options to fit different schedules, including part-time, online, or in-person. This allows learners to conciliate their personal circumstances while still gaining new skills.
  • Cost-effective. Bootcamp tuition is typically much lower than the cost of a four-year degree in computer science or data science, which can make it a more financially accessible option for some individuals. However, costs still tend to be higher than some other alternatives. 

Choosing The Right AI Bootcamp For You

Although less costly and lengthy than traditional degrees, an AI bootcamp can still be a big investment of time and money, so it's important to determine if it's the right fit for your goals. 

Before committing to a bootcamp, you should build some of the relevant skills first. A great option for this is our AI Fundamentals Skill Track, a 6-course program that will help you discover the basics of AI, including actionable knowledge on popular AI topics like ChatGPT, large language models, and generative AI.

If you are convinced to enroll in an AI bootcamp, you should always consider the following factors: 

  • Your goals. Are you considering a career change or looking to enhance your skills? Define your goals to determine if a bootcamp, degree, or online learning is the best option for you. There is no one-size-fits-all approach to data science, so it's important to assess your specific situation before deciding on an intensive bootcamp.
  • Curriculum coverage. AI is a demanding domain that –if you want to learn the technical aspects and how to build your own models– requires a background in Python, SQL, and statistics. If you don’t have this background, you should first focus on building fundamental programming skills.
  • Learning format. Depending on your circumstances, an online program may be better than an in-person program.
  • Time schedule. Depending on your situation, a part-time program may be better than a full-time program.
  • Mentorship opportunities. Some bootcamp offer mentorship opportunities. This is a cool feature, especially if you are switching careers and need someone to guide you through this new field and market.
  • Job guarantees. Some bootcamp have arrangements with companies to provide internships and even jobs after completing the bootcamp. 

What Will You Learn?

While the curriculum for machine learning bootcamps can vary depending on the program, all of them should cover at least the following topics:

AI and machine learning fundamentals

Covering what AI and machine learning are, how they’re relevant in today’s economy, and how the field has evolved.

Types of machine learning

When speaking about AI, we are most of the time speaking about machine learning, a subset of AI that studies how machines can learn from data without being explicitly programmed. Machine learning can be classified into three types:

  • Supervised learning is the most common type of machine learning. In this approach, the model is trained on a labeled dataset. 
  • Unsupervised learning, on the other hand, involves training the model on an unlabeled dataset. The model is left to find patterns and relationships in the data on its own.
  • Reinforcement learning is where an agent learns to make decisions by interacting with its environment. The agent is rewarded or penalized (with points) for the actions it takes, and its goal is to maximize the total reward.

Types of problems in machine learning

Most common machine learning problems can be classified into three groups:

  • Classification. Predicting an event or class of the data, for example, classifying an email as spam or not.
  • Regression. In regression problems, the output is continuous, for example predicting the temperature in a city for the next day.
  • Clustering. In clustering problems, the goal is to classify data into n-number of classes, where n is not predefined, for example, clustering customers into similar groups based on their demographics, interests, and purchase history.

Common machine learning algorithms

 An overview of the most common learning algorithms for each machine learning problem and how to implement them with popular Python packages like scikit-learn.

Deep learning fundamentals

What deep learning is and how this branch of machine learning is rapidly changing the world.

Neural networks

What neural networks are, and how to use and build them with popular frameworks, such as PyTorch and TensorFlow.

This is the minimum content an AI bootcamp should have. However, artificial intelligence is a rich and rapidly evolving field. Depending on your interest, you could also look for more specialized programs. For example, you may be interested in a program focused on natural language processing or image processing. 

You may even be looking for a bootcamp that focuses on generative AI, a branch based on deep learning that is booming following the launch of ChatGPT.

The Bootcamp Experience: What to Expect

Every AI bootcamp offers a different learning experience. However, there are certain elements you can expect in most of them.

Below, you can find a detailed walkthrough of a typical bootcamp journey:

First contact with the support team 

Before applying for a bootcamp, you will normally have a preliminary conversation with someone from the support team, who will give you additional details on the program. This is a great moment to get an idea and a vibe of what you can expect. Don’t be shy; ask all your questions and share your potential concerns. 

Technical skill survey

An important step before applying for a master's is the technical skill survey. This is especially important in these bootcamps, for AI is a complex topic that requires a minimum level of programming, statistics, and so on. If you are just starting your data science journey, maybe, an AI bootcamp is too ambitious for now. 


Time for submitting your application. The process should be straightforward. Once you sign the contract, you can expect a welcoming box or email with all the details of the program (dates, curriculum, contacts, available services, and so on).

The bootcamp starts

Once the bootcamp starts, be ready for a hectic learning experience. Bootcamps are designed as accelerated learning pathways. Things will go very fast in the following weeks or months. You will meet new topics and instructors every week. With little time to digest all the information and complete the cascade of assignments, it’s possible that you will feel overwhelmed at some point. Don’t worry. This is part of the bootcamp experience, and, at the end of the day, it works. 


The time during the bootcamp can be stressful. Luckily, you won’t be alone. In addition to your fellow students, you will normally be assigned a mentor to support you along the way. This is the contact person for both technical and non-technique inquiries. Rely on them, they will be very happy to help.

Career support

Some programs also offer career support. This translates into job hunting seminars, tips for CV drafting, direct access to job interviews or one-to-one sessions with an HR specialist with close bonds with the industry.  

Final project

The best way to learn machine learning is by doing. Most bootcamp include a final project where you will have to solve a specific AI project. This is a great moment to apply what you have learned in the previous weeks.  

Financing Your Education

AI bootcamps are less expensive than a traditional 4-year degree, but they can still represent an important investment. Every student comes with unique personal circumstances. Your economic situation and needs are something you should definitely speak about in your first conversation with the support time. They will do their best to find a way to accommodate your needs. 

If you are unable to pay the complete tuition cost at once, the bootcamp probably offers you other financing options, including upfront payments, monthly installments, tuition deferral options, or student loans.

Moreover, many bootcamps offer scholarships and financial aid, especially for underrepresented groups in tech. Again, don’t miss the opportunity to discuss potential funding that may apply to you before submitting your application.

Alternatives to Machine Learning Bootcamps

While AI bootcamps offer an intensive route to skill acquisition, they're not the only (or always the best) educational pathway available. Here are some alternatives: 

  • Online platforms like DataCamp. Ideal for those seeking flexibility and lower costs. DataCamp offers specialized tracks in areas like data analysis and AI, complete with interactive exercises, real-world projects, and a thriving community. This allows for self-paced, comprehensive learning at a fraction of the cost of a bootcamp. 
  • University degrees. Suitable for those looking for a deeper, more theoretical understanding of data science and AI. University programs offer a broad curriculum and the opportunity for specialized research. 
  • Self-study. Best for highly motivated individuals who are comfortable learning from books, online tutorials, and free courses. This is generally the most budget-friendly option but requires a high level of self-discipline.

Top DataCamp Career Tracks - The Perfect AI Bootcamp Alternative

Some of the best alternatives to AI bootcamps are DataCamp’s career and skill tracks. These educational programs can provide you with the skills, knowledge, and hands-on experience to kick-start your career in AI.

Not only are these tracks self-paced, but they are completely free as part of a DataCamp Premium subscription, starting from as little as $14 per month. You can even gain a AI Fundamentals certification, giving you an industry-recognized qualification. 

Earn a Top AI Certification

Demonstrate you can effectively and responsibly use AI.

1. AI Fundamentals Skill Track

Conquer AI frontier and realize the power of generative AI and complex large language models. Build a strong foundation with AI to propel you into the new AI-powered landscape. Throughout our AI Fundamentals Skill Track, you will gain actionable knowledge on popular AI topics like ChatGPT, large language models, generative AI, and more.

2. Machine Learning Fundamentals with Python Skill Track

Discover the basics of machine learning fundamentals and explore how machine learning is changing the world with our Machine Learning Fundamentals with Python Skill Track. If you’re new to the discipline, this is an ideal place to start. Made of 21 courses, you’ll cover the machine learning basics with Python, starting with supervised learning with the scikit-learn library. 

You’ll also learn how to cluster, transform, visualize, and gain extra insights from data using unsupervised learning and Python’s scipy package. As you progress, you’ll explore linear classifiers for machine learning in Python, including logistic regression and support vector machines. 

You’ll finish the track by covering the fundamentals of neural networks and deep learning models using PyTorch. By the time you’re finished, you’ll understand the essential machine learning concepts and be able to apply the fundamentals of machine learning with Python.

3. Machine Learning Scientist with Python Career Track 

With a Machine Learning Scientist with Python career track, you can master essential skills and tools needed for a career as a machine learning engineer/scientist.

You will learn to process and extract features from data, train models, assess performance, and tune hyperparameters with popular Python packages like Scikit-learn, Spark, and Keras. By the end, you will gain practical experience applying machine learning to real datasets across domains, including natural language and image processing.

4. Machine Learning Engineer Career Track

Step into the cutting-edge field of machine learning engineering with this comprehensive track designed for aspiring professionals. Our Machine Learning Engineer career track teaches you everything you need to know about model deployment, operations, monitoring, and maintenance. 

In this track, you will learn the fundamentals of MLOps. You will work interactively with key technologies like Python, Docker, and MLflow. You will learn in detail about concepts such as CI/CD, deployment strategies, or concept drift. The track includes interactive courses and real-world projects that help you facilitate the skills learned. 

Upon completing this track, you'll emerge as a well-rounded machine learning engineer with all the skills required for a junior machine learning engineer role. Given the high complexity of the topic, prior knowledge of concepts, including data manipulation, training, and evaluating machine learning models using Python, is expected from learners who enroll in this track.


Choosing the right AI bootcamp can help when starting a career in this fast-growing field, but it’s not the right choice for everyone.

In this blog, we have discussed why AI bootcamps can be useful, their pros and cons, comparisons between traditional degrees and online courses, and how to make the most out of the program.

We’ve also looked at some top AI bootcamps, as well as some more viable alternatives to help you jumpstart your AI career. 

Our mission at DataCamp is to democratize data and AI skills for everyone. We help individuals acquire the necessary skills to leverage the power of data and AI. That’s why we offer accessible, top-class education for all. Check out our dedicated AI materials to keep learning: 

AI Bootcamp FAQs

What is a machine learning bootcamp?

A machine learning bootcamp is an intensive, accelerated training program that explores machine learning fundamentals, concepts and techniques and equips learners with career-ready skills. AI bootcamps focus on practical learning via portfolio-ready projects.

Is a machine learning bootcamp for me?

Enrolling in a machine learning bootcamp can be worth it depending on various factors, such as your professional goals, time availability and budget.

Why should I enroll in a machine learning bootcamp?

If you want to enter the world of machine learning and AI without taking a traditional 4-year degree, a bootcamp may be a good option for you.

Will a machine learning bootcamp will help me find a job in the industry?

Besides the education curriculum, bootcamps normally also offer career support services and, sometimes, job guarantees.

What are other alternatives to machine learning bootcamps?

Some of the alternatives to bootcamps are online platforms like DataCamp, University degrees, and self-study.

Photo of Javier Canales Luna
Javier Canales Luna

I am a freelance data analyst, collaborating with companies and organisations worldwide in data science projects. I am also a data science instructor with 2+ experience. I regularly write data-science-related articles in English and Spanish, some of which have been published on established websites such as DataCamp, Towards Data Science and Analytics Vidhya As a data scientist with a background in political science and law, my goal is to work at the interplay of public policy, law and technology, leveraging the power of ideas to advance innovative solutions and narratives that can help us address urgent challenges, namely the climate crisis. I consider myself a self-taught person, a constant learner, and a firm supporter of multidisciplinary. It is never too late to learn new things.


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