Full-Stack Data Science Elite: AI Engineering & Agentic Systems Mastery
Become an expert on the 2026 lifecycle of data through this incredible boot camp! Start by learning how to write Python code and do statistical analysis with data; then, you'll move on to creating production-quality generative AI applications and building RAG pipelines, and you'll finish with creating autonomous self-learning AI agents. You'll walk away with the "T-shaped" skillset necessary to drive innovative, data-powered businesses into a world of AI.
4.9
By Namifly
Last updated: April 2026
$800
One-time payment
30-day money-back guarantee
This course includes:
Data Foundations & Python for AI
Statistics & SQL for Decision Making
Supervised & Unsupervised Machine Learning
Deep Learning & Neural Networks
Generative AI & Natural Language Processing (NLP)
Agentic AI & Orchestration (New for 2026)
MLOps & Production Deployment
Capstone: The Multi-Agent Enterprise System
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Course Description
The Data Scientist: An Extinct Species
If you call yourself a Data Scientist today and all you know how to do is build a Random Forest, then by 2026 you won’t have a job. The demand in the marketplace for an "insight in a vacuum" is gone; what are employers looking for? They are looking for functioning systems to solve their business problems via Artificial Intelligence. Our Data Science Bootcamp with AI is designed around this new reality, combining an intense level of statistical rigor with cutting-edge Generative AI Engineering.
The 2026 Bootcamp will be the new industry standard.
The industry has experienced a major shift from using Predictive Models to using Generative and Agentic Systems. While traditional machine learning techniques (including regression and classification) are still the foundation, the 2026 curriculum adds an essential additional layer: the ability to incorporate Large Language Models (LLMs) along with your own private business data. Through this course, you will learn how to build Full-Stack AI and move from prototype to scalable applications that act as autonomous agents encapsulating entire companies.
The Four Pillars of Modern Data Science
Our professional roadmap is built on the 2026 "Core-Four" framework:
The Statistical Engine (Foundation): You will master Python, SQL, and the mathematics of data. Learn to perform complex exploratory data analysis (EDA) and hypothesis testing that survives the scrutiny of stakeholders.
Predictive Machine Learning: Build the "Bread and Butter" of industry AI. From XGBoost to advanced Neural Networks, you will learn to forecast demand, detect fraud, and build recommendation engines using Scikit-Learn and PyTorch.
Generative AI & LLM Ops:(The 2026 Core). Learn to build RAG pipelines using Vector Databases (like Pinecone or Milvus). Master prompt engineering, fine-tuning techniques, and the Model Context Protocol (MCP) to give AI agents access to live business data.
Deployment & MLOps: A model is only valuable if it’s live. You will learn to containerize your apps with Docker, deploy them via FastAPI, and monitor for "model drift" and AI hallucinations using modern observability tools.
Building for Human-AI Collaboration
Namifly promotes the ideology of the "Human-in-the-Loop" methodology. You will build models (not just “black boxes”), and understand AI Ethics and Explainability. You will understand how to locate bias in your training data and ensure your AI systems meet 2026's global regulations.
You will create three "Capstone"-type projects as a result of this bootcamp: a standard predictive model; a production-ready RAG chatbot; and a multi-agent autonomous workflow. If you are switching careers or an existing developer wanting to specialise, this is the most comprehensive route to becoming a successful, high-value Data Science leader in the world of AI today.
Course Curriculum
Expand the sections below to see the detailed curriculum for this course.
Python Syntax, Data Structures, and "Clean Code" for Data Science.
NumPy & Pandas: High-performance data manipulation.
Inferential Statistics, Probability, and A/B Testing.
Advanced SQL: Complex Joins, Window Functions, and Database Design.
Linear/Logistic Regression and Decision Trees.
Ensemble Methods: Random Forests, Gradient Boosting (XGBoost/LightGBM).
Clustering (K-Means) and Dimensionality Reduction (PCA)
Introduction to PyTorch & Keras.
Computer Vision (CNNs) and Sequence Modeling (RNNs).
Transformers, BERT, and GPT architectures.
Building RAG Pipelines: Embeddings and Vector Databases.
Prompt Engineering & Large Language Model (LLM) Fine-tuning.
Designing Autonomous Agents with LangChain & CrewAI.
Tool-use: Teaching AI to call APIs and search the web.
Building APIs with FastAPI.
CI/CD for Data Science: GitHub Actions & Docker.
Monitoring AI reliability and cost-management
End-to-end design, build, and deployment of a real-world AI product.
Instructor information not available.
4.9
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Frequently Asked Questions
Find answers to common questions about this course.
Yes. While we move fast, we start with Python and Math essentials. A background in logic or basic math is helpful, but no prior coding experience is required.
Most AI courses only teach you how to use AI. This bootcamp teaches you how to build and deploy it using real data, statistical principles, and engineering practices.
Graduates qualify for roles like Data Scientist, AI Engineer, MLOps Specialist, and BI Analyst.
