The AI Engineer Course 2026: Complete AI Engineer Bootcamp

Explore the differences between structured data, organized in rows and columns, and unstructured data, such as text, images, and video, and learn how AI turns unstructured data into valuable insights.

Explore how data drives machine learning, from binary image representation in mNIST to collecting diverse data sources and ensuring high-quality inputs for reliable artificial intelligence models.

Compare labeled and unlabeled data for AI modeling; manual labeling builds data and accuracy but costs time, while unlabeled data lets the model learn on its own from unstructured data.

The lecture explains how digitalization and high-quality data fuel AI advances, and highlights metadata as data that describes other data, including asset type, author creation, date, usage, and file size.

Discover how machine learning uses training data to learn and improve through trial and error, and apply it to predicting home prices with a real estate app.

Explore supervised learning with labeled data for classification and prediction, unsupervised learning for discovering patterns without labels, and reinforcement learning that optimizes goals through trial and error.

Deep learning, a subset of machine learning inspired by the brain, uses neural networks to process input through layers and learn with weights and biases for digit recognition in mNIST.

Explore the long history and modern rise of robotics, from ancient automata to AI-driven humanoid systems, sensors, and multi-model architectures powering autonomous machines.

Explore computer vision, the AI field that uses machine learning and neural networks to interpret images and videos, including CNNs, transformers, GANs, and U-Net, with applications like self-driving cars.

Explore traditional machine learning and its real-world business impact, from fraud detection and mortgage prediction to pricing, demand forecasting, order optimization, and personalized product recommendations.

Discover generative AI, the technology behind ChatGPT and Dall-E, and explore LLMs, diffusion models, GANs, neural radiance fields, and hybrid models that produce novel content from training data.

Trace the rise of ChatGPT from 3.5 to 4.0 and how its dialogue-focused design showcases Gen AI and large language models, enabling text summarization and help with technical tasks.

Trace NLP from rule-based systems to statistical approaches, illustrating how data analysis distinguishes noun and verb usage and leads to vector embeddings and deep learning.

Explore how NLP advanced from statistical analysis to neural networks and the transformer architecture, using vector embeddings to capture semantic similarity and enable large language models.

Explore how language models predict the next word from context, contrasting masked and autoregressive approaches. Learn how autoregressive models like GPT generate text and how multilingual training data scales llms.

Explore the efficiency of training large language models by comparing supervised learning with labeled data and costly labeling to unsupervised and self-supervised approaches, addressing scalability and context through transformers.

Trace the evolution of language modeling from n-grams to transformers, detailing unigram, bigram, trigram, RNNs, LSTMs, and the attention mechanism powering llms like ChatGPT.

Explore the phases in building llms, from model design and dataset engineering to pre-training, post-training, fine-tuning, and final testing, highlighting ethical data practices and domain adaptation.

Compare prompt engineering, retrieval augmented generation (RAG), and fine tuning to understand how each method affects AI performance, context, and weights without altering the model itself.

Foundation models enable general tasks by moving from text-only systems to multimodal capabilities. They can handle text, code, images, and video, with fine tuning and prompt engineering expanding their capabilities.

Evaluate buy versus make for foundation models, balancing internal strategic value with outsourcing through model as a service from OpenAI to tailor AI use cases.

Explore how ai models hallucinate and become inconsistent, learn to fact-check outputs, implement prompts that ask for verified answers, and understand factors like training data and hardware variability.

Budget before building ai models by balancing data set quality and model size to optimize performance within cost limits; consider smaller retrainable models for cost efficiency.

Address latency in customer-facing AI apps by understanding the autoregressive architecture's sequential word generation, which slows outputs, and explore parallel computing and smaller models to boost speed.

Running out of data slows future model growth as data scraping lawsuits and rising licensing costs push proprietary datasets into the spotlight, while AI content fuels repetition, hallucination, and biases.

Develop Python coding skills to access AI APIs from OpenAI, Llama, and Anthropic, master prompt engineering, and use NumPy, Pandas, and Matplotlib with Jupyter Notebook, Google Colab, Spyder, or PyCharm.

Learn how APIs connect clients and servers to enable data exchange through requests and responses, with real-world examples like 365 company and a job board, and integrating OpenAI's API.

Discover how vector databases store embeddings for fast similarity search of unstructured data and store past interactions as vectors to improve context.

Explore how open source artificial-intelligence models compete with closed systems, driven by community collaboration, reduced hardware needs, and domain-specific fine-tuning, while weighing costs, data risks, and future trends.

Discover how Hugging Face promotes open source ai collaboration, offers pre-trained models via the transformers library, and democratizes access to tools for sharing, fine-tuning, and evaluating models.

Leverage Lang Chain, an open-source orchestration environment in python and javascript, to integrate multiple foundation models and data sources with pre-built components, reducing code and enabling scalable AI apps.

Explore AI evaluation tools and the AI judge approach to test AI-powered products, enabling cost effectiveness, scalability, and speed while comparing open-ended and coding questions with human oversight.

Identify and align AI use cases with the overall business strategy, guide deployment and evaluation of AI models, and evangelize adoption to boost products, processes, and decision making.

Define the AI developer as the role that builds foundation models. Position the AI engineer as the one who builds applications on top.

Bridge foundation models with real-world products by integrating AI into websites, mobile apps, and smart devices. Use fine-tuning, prompt engineering, and RAG AI to optimize performance with Python and pandas.

Explore how AI ethics balance moral principles with human accountability to maximize benefits for the common good while mitigating risks like privacy, bias, misinformation, and job displacement through governance.

Explore the future of generative AI through market forecasts and growth drivers, emphasizing electricity, data, and computing power, and the regulation, data ethics, and energy considerations shaping training and models.

Learn how to translate real-world tasks into machine readable source code. Explore the difference between programming and computer science, and how to write clear, maintainable code.

Explore why Python is a free, open-source, general-purpose, high-level, cross-platform language with broad domain applicability—from finance and econometrics to economics, data science, machine learning, and big data.