Our program is designed to provide the tools to succeed in the AI industry by developing AI skills via project-based work. Our key philosophy is to learn AI by solving real-world problems. 

Our program is divided into Sprints and every Sprint has it's own project. Once you're assigned a project, you'll begin by analyzing the data, constructing robust data pipelines, and developing machine learning models - starting with classical techniques and progressing to deep neural network models for more intricate tasks. You’ll then advance to mastering Large Language Models (LLMs), which have transformed the fields of natural language processing and AI-generated content. Finally, you’ll acquire the skills needed to deploy production-ready machine learning models.

Through practical projects, you'll receive guidance from our industry-leading expert instructors who will introduce you to the essential concepts required for success in this field. Here’s a snapshot of the core topics you’ll tackle:

Data Engineering 

In this foundational pillar, we focus on the data infrastructure that powers AI and Machine Learning. You'll learn how to collect, process, and manage large volumes of data effectively. You will become proficient in designing Data Pipelines, writing your own SQL queries, developing Data Extraction, Transformation, and Loading workflows (ETL) and implement good practices for data cleaning & preprocessing. 

Machine Learning 

Dive into the foundational concepts behind AI, focusing on statistical machine learning models—from training and evaluating them to exploring supervised (linear regression or Random Forrest) and unsupervised learning algorithms for clustering and dimensionality reduction. This section also covers best practices for model evaluation, selection, feature engineering, and deployment.

Deep Learning 

Deep Learning is at the core of modern AI innovations. In this module, you will learn how to design and implement your own deep learning architectures, enabling you to explore neural networks and advanced algorithms capable of solving highly complex tasks, from Computer Vision to Natural Language Processing (NLP). The curriculum spans a wide range of neural architectures, starting with simple feed-forward multilayer perceptrons and progressing to advanced convolutional neural networks for image recognition. You will also dive into recurrent neural networks, the concept of Attention and the groundbreaking Transformer architecture.

Large Language Models

In this pillar, you’ll gain expertise working with LLMs that have revolutionized the fields of natural language processing and generative AI. We will study some of the most important Transformer architectures for NLP like BERT for word-embedding, GPT, Llama and others for text generation. The key topics also include prompt engineering, pretraining & Fine-tuning of LLMs in order to adapt them to new tasks or domains. 

These are the 4 core pillars of our program. Once you have completed these you can continue to develop your skills by complementing the main program with our industry-focused add-on specializations designed to continue to learn in a flexible way. 

We currently offer specializations covering topics like developing LLM-based applications or Reinforcement Learning with Human Feedback, but also offering foundational training on Algorithms required to master code interviews. Artificial Intelligence is rapidly evolving which is why our add-on specializations are adapting to industry trends allowing our fellows to study at the edge of innovation. 

If you are interested in a highly technical program we invite you to apply to Anyone AI and take your next step in Machine Learning and Artificial Intelligence.