AI Bootcamp
Everyone’s heard of ChatGPT, but what truly powers these modern large language models? It all starts with the transformer architecture. This bootcamp demystifies LLMs, taking you from concept to code and giving you a full, hands-on understanding of how transformers work. You’ll gain intuitive insights into the core components—autoregressive decoding, multi-head attention, and more—while bridging theory, math, and code. By the end, you’ll be ready to understand, build, and optimize LLMs, with the skills to read research papers, evaluate models, and confidently tackle ML interviews.
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01Remote
You can take the course from anywhere in the world, as long as you have a computer and an internet connection.
02Self-Paced
Learn at your own pace, whenever it's convenient for you. With no rigid schedule to worry about, you can take the course on your own terms.
03Community
Join a vibrant community of other students who are also learning with AI Bootcamp. Ask questions, get feedback and collaborate with others to take your skills to the next level.
04Structured
Learn in a cohesive fashion that's easy to follow. With a clear progression from basic principles to advanced techniques, you'll grow stronger and more skilled with each module.
Understand the lifecycle of large language models, from training to inference
Build and deploy a fully functional LLM Inference API
Master tokenization techniques, including byte-pair encoding and word embeddings
Develop foundational models like n-grams and transition to transformer-based models
Implement self-attention and feed-forward neural networks in transformers
Evaluate LLM performance using metrics like perplexity
Deploy models using modern tools like Huggingface, Modal, and TorchScript
Adapt pre-trained LLMs through fine-tuning and retrieval-augmented generation (RAG)
Leverage state-of-the-art tools for data curation and adding ethical guardrails
Apply instruction-tuning techniques with low-rank adapters
Explore multi-modal LLMs integrating text, voice, images, and robotics
Understand machine learning operations, from project scoping to deployment
Design intelligent agents with planning, reflection, and collaboration capabilities
Keep up-to-date with AI trends, tools, and industry best practices
Receive technical reviews and mentorship to refine your projects
Create a robust portfolio showcasing real-world AI applications
In this bootcamp, we dive deep into Large Language Models (LLMs) to help you understand, build, and optimize their architecture for real-world applications. LLMs are revolutionizing industries—from customer support to content creation—but understanding how these models work and optimizing them for specific tasks presents unique challenges.
Over an intensive, multi-week curriculum, we cover:
The technical foundations of LLMs, including autoregressive decoding, positional encoding, and multi-head attention. The LLM lifecycle—from large-scale pretraining to fine-tuning and instruction tuning for niche applications. Industry best practices for model evaluation, identifying performance bottlenecks, and employing cutting-edge architectures to balance efficiency and scalability. This bootcamp includes hours of in-depth instruction, hands-on coding sessions, and access to a dedicated community for ongoing support and discussions. Additionally, you’ll have exclusive access to code templates, an expansive reference library, and downloadable resources for continuous learning.
Your expert guides through this bootcamp are:
Alvin Wan: Alvin is a Senior Research Scientist at OpenAI with over a decade of experience in AI, specializing in large language models and efficient AI design. Previously, he was a Senior Research Scientist at Apple, working on AI and large language models for Apple Intelligence. Alvin also worked on Tesla’s AutoPilot and holds a PhD from UC Berkeley, where his research has garnered over 3,000 citations. He brings a unique combination of industry expertise and cutting-edge research to this course, guiding you through the technical aspects of building, optimizing, and deploying LLMs.
Zao Yang: Zao is a co-founder of Newline, a platform used by 150k professionals from companies like Salesforce, Adobe, Disney, and Amazon. Zao has a rich history in the tech industry, co-creating Farmville (200 million users, $3B revenue) and Kaspa ($3B market cap). Self-taught in deep learning, generative AI, and machine learning, Zao is passionate about empowering others to develop practical AI applications. His extensive knowledge of both the technical and business sides of AI projects will be invaluable as you work on your own.
With Alvin and Zao's guidance, you’ll gain practical insights into building and deploying advanced AI models, preparing you for the most challenging and rewarding roles in the AI field.
Be able to build large language models, which can increase your salaries by $50k a year. Worth $500k over 10 years
Cheatsheet on generative AI interviews for FANGs, a $50k a year over a $500k value
A complete course on end to end streaming Langchain with a fully functional application for startups. $15k in value
Be able run consulting in AI $100k in annual value. Over 10 years. $1m
Be able to build an AI company $1M in annual value
Technical and business design review from Alvin and Zao about your project. $25000 dollars in value
$3.4M in value. Only for $8k. Available now for $7000 dollars in pre-payment. Payment plan for $2667 per month. This will be a $10k to $15k bootcamp in the future
Guaranteed help to complete your project
Our students work at
Bootcamp Syllabus and Content
Introduction to AI applications
8 Lessons
This module introduces foundational concepts and practical workflows for working with Large Language Models (LLMs). Topics include terminology (e.g., ChatGPT vs. LLM, inference phases, training stages, and model compression techniques), the LLM ecosystem (vector databases, inference APIs, and fine-tuning libraries), and the model lifecycle. Participants will build a simple LLM-based system from scratch, starting with “Hello World” inference using Hugging Face, and deploy an LLM API using Modal for serverless deployment.
- 01Introduction to AI applicationsSneak Peek
- 02Intro to AI modalitiesSneak Peek
- 03Examples of AI applicationsSneak Peek
- 04TechnologiesSneak Peek
- 05How to brainstorm AI applicationsSneak Peek
- 06Vertical applications and their example technologiesSneak Peek
- 07Indie hacker examplesSneak Peek
- 08Venture funded LLM examplesSneak Peek
Building a Shakespearean Language Model
7 Lessons
Building a Shakespearean Language Model
- 01What is a tokenizer? What makes a tokenizer "good"?Sneak Peek
- 02Build a baseline 'word-based' tokenizerSneak Peek
- 03Build a byte-pair encodingSneak Peek
- 04Compare with state-of-the-art tokenizers (e.g., Llama’s)Sneak Peek
- 05What is an embedding? What makes an embedding "good"?Sneak Peek
- 06Understand the semantic meaning of word embeddingsSneak Peek
- 07Train a word embedding on Shakespearean tokensSneak Peek
Building an n-gram language model
5 Lessons
Building an n-gram language model
- 01What is an n-gram?Sneak Peek
- 02Train a 2-gram modelSneak Peek
- 03Predict using 2-gram modelSneak Peek
- 04Train an n-gram modelSneak Peek
- 05Predict using n-gram model, in an autoregressive mannerSneak Peek
Building self-attention
2 Lessons
Building self-attention
- 01A minimal version of self-attentionSneak Peek
- 02Build a batched version of self-attentionSneak Peek
Building the feed-forward neural network
3 Lessons
Building the feed-forward neural network
- 01A minimal version of the feed-forward neural network (e.g., MLP)Sneak Peek
- 02Build a batched version of the MLPSneak Peek
- 03“Train” on sanity check dataSneak Peek
Assembling the transformer-based language model
3 Lessons
Assembling the transformer-based language model
- 01Add the remaining components (Skip connections, norms, positional encodings)Sneak Peek
- 02Use black box optimizer to pretrainSneak Peek
- 03Predict using transformer-based language model, autoregressivelySneak Peek
Evaluating and deploying a transformer-based language model
4 Lessons
Evaluating and deploying a transformer-based language model
- 01Run unsupervised evaluation using perplexitySneak Peek
- 02Export the model using torchscriptSneak Peek
- 03Plug in our custom model into our LLM APISneak Peek
- 04Open-sourced pre-trained models include Llama, Phi-3, Mixtral etc.Sneak Peek
Datasets
3 Lessons
Datasets
- 01Pre-curated pretraining datasetsSneak Peek
- 02Common tools for data curation (mech turks, vis tools)Sneak Peek
- 03how to add guardrailsSneak Peek
Low-Rank Adapters for Instruction Tuning
4 Lessons
Low-Rank Adapters for Instruction Tuning
- 01Build low-rank adapters for Shakespearean modelSneak Peek
- 02Download instruction tuning datasetSneak Peek
- 03Run low-rank adapter fine-tuningSneak Peek
- 04Open-sourced instruction-tuned models include DBRX, Pythia, Cerebrus etc.Sneak Peek
Retrieval-Augmented Generation (RAG)
5 Lessons
Retrieval-Augmented Generation (RAG)
- 01What is RAG?Sneak Peek
- 02Implement RAGSneak Peek
- 03Apply RAG to Shakespearean modelSneak Peek
- 04Apply RAG to pre-trained Large Language ModelsSneak Peek
- 05Integrate RAG into our APISneak Peek
The Future of Large Language Models
5 Lessons
The Future of Large Language Models
- 01How to use LlamaIndex to chain tools togetherSneak Peek
- 02The data wallSneak Peek
- 03The ultimate test of time (Use fundamentals to understand and use time to filter)Sneak Peek
- 04How to keep to dateSneak Peek
- 05Multi modal large language modelsSneak Peek
Machine learning operations
4 Lessons
Machine learning operations
- 01project scopingSneak Peek
- 02data needsSneak Peek
- 03modeling strategiesSneak Peek
- 04deployment requirementsSneak Peek
Agents
2 Lessons
Agents
- 01What are agents?Sneak Peek
- 02Design patternsSneak Peek
Resources
You’ll receive a comprehensive set of resources to help you master large language models.
Prompt engineering templates
AI newsletters, channels, X, reddit channels
Break down of LLama components
Break down of Mistral components
Bonus
Unlock exclusive bonuses to accelerate your AI journey.
Be able to build large language models, which can increase your salaries by $50k a year. Worth $500k over 10 years.
Cheatsheet on generative AI interviews for FANGs, a $50k a year over a $500k value.
A complete course on end to end streaming Langchain with a fully functional application for startups. $15k in value.
Be able run consulting in AI $100k in annual value. Over 10 years. $1m.
Be able to build an AI company $1M in annual value.
Technical and business design review from Alvin and Zao about your project. $25000 dollars in value.
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Meet the Bootcamp Instructor
Frequently Asked Questions
How is this different from other AI bootcamps?
Bootcamps vary widely in scope and depth, generally targeting individuals seeking clear, concrete outcomes. One of the main advantages they offer is the interactive learning environment between peers and instructors. In the AI space, bootcamps typically fall into several categories: AI programming, ML/Gen AI, foundational model engineering, and specific tracks like FAANG foundational model engineering.
Most bootcamps aim to provide specialized skills for a particular career path—like becoming an ML/Gen AI engineer. These programs often cost $15,000–$25,000, run over six months to a year, and involve a rigorous weekly schedule with around four hours of lectures, two hours of Q&A, and an additional 10–15 hours of homework. Traditional coding bootcamps designed to take someone from a non-technical to a technical role are similar in cost and duration.
In contrast, our program offers a unique approach by balancing practical AI programming skills with a deep understanding of foundational model concepts. Many other AI programming bootcamps focus exclusively on specific areas like Retrieval-Augmented Generation (RAG) or fine-tuning and do not delve into foundational model concepts. This can leave students without the judgment and first-principles reasoning needed to understand and innovate with AI at a fundamental level.
Our curriculum is crafted to cover AI programming while incorporating essential foundational model concepts, giving you a well-rounded perspective and the skills to approach AI with a strong theoretical foundation. To my knowledge, few, if any, bootcamps cover foundational models in a way that empowers students to understand the entire AI model lifecycle, adapt models effectively, and confidently pursue project ideas with guided support.
What should I look for in this AI Bootcamp?
This bootcamp offers a comprehensive curriculum covering the entire lifecycle of Large Language Models (LLMs). It balances hands-on programming with theoretical foundations, ensuring you gain practical skills and deep conceptual understanding. Highlights include:
- Direct mentorship from Alvin Wan (OpenAI, Apple) and Zao Yang (Farmville, Kaspa).
- Hands-on projects like building, deploying, and adapting LLMs.
- Access to industry-standard tools and frameworks like Huggingface, Modal, and LlamaIndex.
- Career-focused outcomes such as consulting opportunities, AI startup guidance, and advanced technical skills.
Who is this Artificial Intelligence Bootcamp ideal for?
This bootcamp is tailored for:
- Professionals aiming to implement AI solutions at work (e.g., RAG or private fine-tuning).
- Those interested in building vertical foundational models for specific domains.
- Aspiring consultants or entrepreneurs looking to leverage AI knowledge to create startups or offer services.
What are the eligibility criteria for this AI Bootcamp?
The main criteria are a willingness to learn and a commitment to actively participate. While a basic understanding of programming is helpful, the bootcamp assumes no prior AI or machine learning knowledge.
Are there any required skills or Python programming experience needed before enrolling?
Basic Python programming knowledge is recommended but not mandatory. The bootcamp starts from fundamental concepts and provides all the necessary support to help you succeed.
What is the course structure?
Total Weekly Time Commitment: Approximately 3 hours for structured activities, including 2 hours of lectures and a dedicated 1-hour Q&A office hours session. Hands-On Programming: Expect to dedicate 2–4 hours for practical programming exercises. Individual Project Work: The time spent on your project is up to you, so you can invest as much as you wish to build your skills. Optional Guidance Sessions: We may add an extra 1-hour session for optional guidance on selecting a niche or project topic. Recordings Available: All sessions will be recorded for those unable to attend live, ensuring that no one misses valuable content. Flexible Scheduling: We’ll schedule the live sessions to best accommodate the group.
Do I need any pre-requisite?
Need to be able to program. Need to have a commitment to be able to do the work and ask questions. Some python programming would help just some basic course. You don’t need to do a ML course. We assume nothing for the course.
Anything I need to prepare?
Ideally you think about the project that you want to create. Some people have AI at their work. Some people want to create a vertical foundational model.
Why should I take the Artificial Intelligence Bootcamp from newline?
This bootcamp stands out because:
- It combines hands-on programming with foundational model concepts, giving you a holistic understanding of AI.
- It includes real-world applications, guided projects, and personalized mentorship.
- It guarantees project completion with expert reviews from Alvin Wan and Zao Yang.
- Flexible scheduling, recordings, and a supportive learning environment make it accessible and effective.
To what extent will the program delve into generative AI concepts and applications?
The curriculum deeply explores generative AI, covering topics like tokenization, transformer models, instruction tuning, and Retrieval-Augmented Generation (RAG). You’ll also learn how to build applications in text, voice, images, video, and multi-modal AI.
Do you have something I can send my manager?
Hey {manager}
There's a course called AI Engineer Bootcamp that I'd love to enroll in. It's a live, online course with peers who are in similar roles to me, and it's run on Newline, where 100,000+ professionals from companies like Salesforce, Adobe, Disney, and Amazon go to level up when they need to learn quickly and efficiently.
A few highlights:
- Direct access to Alvin Wan, the expert instructor who worked on LLMs at Apple Intelligence and OpenAI.
- Hands-on working sessions to test new tactics and ideas. Unlike other classes, it teaches the fundamentals of the entire lifecycle of LLMs. This includes being able to understand LLMs and being able to adapt it to specific projects. The course provides a guarantee of being able to build a project. This can apply to a project at work.
- It also provides the latest thinking in the space on how to solve problems we're facing.
I anticipate being able to put my learnings directly into practice during the course. After the course, I can share the learnings with the team so our entire team levels up.
The course costs $4800 USD as an early bird discount or $5000 through a payment plan. If you like, you can review course details here, including the instructor’s bio:
https://newline.notion.site/AI-live-cohort-1303f12eb0228088a11dc779897d15bd?pvs=4
What do you think?
Thanks, {Your Name}
Do you have any financing?
We can provide a payment plan. In the future, we’ll have different payment plans, but the payment plan is flexible enough for you.
What are the career outcomes after completing the AI Bootcamp with newline?
Graduates can pursue careers such as:
- AI engineers with enhanced earning potential (average salary increases of $50k/year).
- Consultants specializing in AI for enterprises or startups.
- Entrepreneurs building AI-driven companies.
- Technical leads in developing and deploying advanced AI solutions.
Will I receive a certificate after completing the AI Bootcamp with newline?
Yes, you will receive a certificate of completion, demonstrating your expertise in AI concepts and applications.
Are there any hands-on projects incorporated into the AI Bootcamp curriculum?
Yes, the curriculum is highly project-focused. You’ll work on building and deploying LLMs, adapting models with RAG and fine-tuning, and applying AI to real-world use cases, ensuring practical experience and a portfolio of projects.
I have a timing issue? What can you do?
You can attend this one and also attend the next one as well. Otherwise, you’ll have to wait till the next cohort.
Do you have a guarantee?
We have a guarantee that we’ll help you be able to build your project. This is that we need to align on the project, the budget, and your time commitment. We’ll need your commitment to be able to work on the project. For example, rag based, fine tuning, building a small foundational model totally within the scope. If you want to build a large foundational model, the project will have to focus on the smaller one first. You’ll have to commit to learn everything needed for the course.
What is the target audience?
The goal is around 3 personas.
- Someone wants to apply RAG and instructional fine tuning for private on premise data at work
- Someone who wants to be able to fine tune a model to build a vertical foundational model
- Someone who wants to be able to use the AI knowledge for consulting and build AI startups.
Will you be covering multi-modal applications?
Yes. We’ll be covering this and learning how to learn within this space as well.
What kind of support and resources are available outside the AI Bootcamp?
You’ll have access to:
- Direct mentorship from Alvin Wan and Zao Yang.
- Resources like prompt engineering templates, cheat sheets, and curated datasets.
- Optional guidance sessions for project topics and niche selection.
- Recordings of all sessions for flexible learning.
How does the AI Bootcamp at newline stay updated with the latest advancements and trends in the field?
The curriculum reflects cutting-edge developments in AI, informed by the instructors’ active work in the field. Topics like multi-modal LLMs, RAG, and emerging tools are continuously integrated to ensure relevance.
What is the salary of an AI Engineer in the USA?
AI engineers in the USA earn an average salary of $120,000–$200,000 annually, depending on their expertise and experience. Completing this bootcamp can increase your earning potential by $50,000 annually.
Do you offer preparation for Artificial Intelligence interview questions?
Yes, the bootcamp includes a cheatsheet for AI interviews at top companies (e.g., FANG) and guidance for acing technical and business-focused roles in AI.
What are the possible careers in Artificial Intelligence?
AI offers diverse career opportunities, including:
- AI/ML Engineer
- Data Scientist
- AI Consultant
- Research Scientist
- AI Startup Founder
- Product Manager for AI-driven solutions