6-Week AI Engineering Accelerator

Master AI foundations with 8 comprehensive courses over 6 weeks, designed to build practical skills step-by-step.

Schedule: Sept 10-Oct 22

Check the Program Here

Student Testimonials

Since its launch in 2019, this bootcamp has successfully trained over hundreds participants, helping individuals gain essential skills in data science and AI. With a proven track record, the program has become a trusted path for aspiring professionals to build foundational knowledge in these fast-growing fields. Our structured approach and hands-on learning experiences have consistently prepared participants to thrive in data-driven roles, making a meaningful impact in their careers and the industry.

AUTUMN 2026: AI Engineering Accelerator

(Previously known as AI Bootcamp)

Data, Coding, and AI preparation courses for ODSC AI Engineering Accelerator

ODSC AI Primer Courses

These primer courses can be taken stand alone or as part of our AI Engineering Accelerator series. These foundations series is built from the ground up to boost your understanding of data-centric AI.

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  • On-Demand On-Demand Optional Prerequisites

    Start your AI journey with essential skills in data analysis, SQL, and Python programming through our on-demand courses. Learn to collect, organize, and analyze data, master SQL for data manipulation, and quickly get up to speed with Python. Each course is designed to build a strong foundation in just 2 hours each.
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  • Sep 10 to Oct 22 Live Training: Weeks 1 to 6

    Master AI from fundamentals to advanced applications in our 6-week AI Engineering Accelerator. Start with an introduction to AI and data prep with Python, then dive into hands-on machine learning, large language models, and AI agents with RAG. Each course is designed to equip you with the practical skills needed to excel in the AI field.
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  • Weekly Office Hours

    Weekly office hours provide personalized support and guidance to help you succeed in your learning journey. During these sessions, you can ask questions, clarify concepts, and receive feedback on your progress. Our instructors are available to ensure you fully grasp the course material and are prepared for the hands-on exercises.
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Certificate Award
On-DemandSep 10 to Oct 22

3 Ways to Access

Premium Annual

FREE with

Premium Annual

Access to this bootcamp is FREE with an Annual Premium Subscription, giving you unlimited access to all sessions, hands-on exercises, and ADDITIONAL courses throughout the year.

FREE with ODSC AI Accelerator

FREE with

ODSC AI West 2026 AI Accelerator Pass 

Free with ODSC AI West – AI Engineering Accelerator Pass, which includes the full accelerator program plus four days at the conference from October 27-29th (In-Person or Virtual) . With this pass, you’ll experience hands-on training, expert-led tutorials, inspiring keynotes, and our demo hall where you can explore the latest tools and technologies. It’s an all-inclusive opportunity to build foundational skills in AI and data science while connecting with industry leaders and peers at one of the premier AI conferences.

Buy Standalone

SAVE 30%

$399 (Save $200) 

The Get this entire course set for $399, a $200 discount off the regular price. This includes access to all sessions, hands-on exercises, providing a comprehensive foundation in AI and data science skills at a great value.

How it Works

  • Each course is 2.5 hours long and includes extra materials

  • The primer series is taught live and then available on demand.

  • If you miss the live course, each session is available on-demand as soon as you register. 

  • Each course includes exercises to improve learning outcomes.

  • Coding expercises allow you to learn hands-on skills.

  • Learn at your own pace. Courses can be taken alongside additional Ai+ courses.

Live Accelerator Courses:

The program has 8 live courses. Each session is complemented by optional office hours, providing additional support for students. In Sept – Oct 2026, refresher courses will be available for anyone seeking to reinforce their learning.

On-Demand Prerequisites:

New to data and programming or need a refresher? On-demand courses in  Python programming, SQL, data literacy, and generative AI literacy.

Office Hours & Certification

Our optional weekly office hours ensure you get the most out of each work. You’ll get help with course material and exercises. 

Build with Cutting-edge AI Tools and Frameworks

Week 1: Course 1

AI & ML Foundations for Agentic Systems

This AI course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training and types of machine learning, including supervised and unsupervised learning, as well as some of the most common models such as regression and k-means clustering.  

Upon completion, individuals will have a foundational understanding of machine learning and its capabilities and be well-positioned to take advantage of introductory-level hands-on training in machine learning and data science. 

Duration: 2.5 hours

Sep 10, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Module 1:
Introduction

  • An Overview of AI
  • The AI Stack
  • Machine Learning Definitions
  • ML vs Traditional Programming
  • Algorithms and Models
  • Machine Learning Workflow

Module 2:
Types of ML  

  • Independent vs Dependent Variables
  • Feature Selection
  • Data Labeling
  • Training & Testing Models
  • Structured and Unstructured Data
  • Type of Machine Learning

Module 3:
Supervised Learning

  • Supervised Machine Learning
  • Popular ML Algorithms
  • Classification Models
  • Regression Models
  • Which Model to Use?
  • Feature Extraction

Module 4:
Unsupervised Learninng

  • Unsupervised Machine Learning
  • Supervised vs Unsupervised ML
  • K-Means Cluster Models
  • Deep Learning Overview
  • Deep Learning vs Machine Learnin

Week 1: Course 2

AI-Assisted Coding for AI Development

This course is your hands-on entry point into coding for the age of AI — with no need to memorize rules before you build. In Vibe Coding with AI, you’ll explore how to write real, functional Python code using patterns that show up across modern AI workflows. We’ll code with immediate feedback using tools like Lovable, build interactive apps with Streamlit, and manipulate real-world data. You’ll get into the coding flow by doing — and learn programming structure only when it helps you move forward. By the end, you’ll be able to sketch, prototype, and build the foundations for AI apps and dashboards with confidence.

Duration: 2.5 hours

Sep 15, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Module 1:
Vibe Coding Foundations

  • What is vibe coding and why it fits the AI era
  • Writing Python in modern playgrounds like Lovable
  • Variables, expressions, and feedback-driven learning
  • Coding patterns: input → process → output
  • Quick wins with real-world examples (e.g., text, numbers, colors)

Module 2:
Data Structures You’ll Actually Use

  • Lists, dictionaries, strings — and when to reach for each
  • Pattern-based coding: parsing, counting, filtering
  • Working with simple datasets (CSV, JSON)
  • Tools: Intro to Pandas and basic data wrangling

Module 3:
Functions, Reuse, and Tooling Up

  • Writing useful functions (that you’ll actually reuse)
  • File I/O with real examples (e.g., chat logs, notes)
  • Bringing in the magic: importing libraries like openai, matplotlib, pandas
  • Building blocks of an AI workflow in code

Module 4:
From Code to Creation — Build Something Real

  • Intro to Streamlit: building your first interactive app
  • Mini-project: build a simple dashboard or AI assistant UI
  • Optional tools: Lovable Notebooks, Replit, ChatGPT Code Interpreter
  • Wrap-up: what you can build next with vibe coding + AI tools

Week 2: Course 1

Python for AI Development 

Data prep is the cornerstone of any data-driven project, and Python stands as one of the most powerful tools in this domain. In preparation for the ODSC conference, our specially designed course on “Machine Learning Data Prep with Python” offers attendees a hands-on experience to master the essential techniques. From cleaning and transforming raw data to making it ready for analysis, this course will equip you with the skills needed to handle real-world data challenges. As part of a comprehensive series leading up to the conference, this course not only lays the foundation for more advanced AI topics but also aligns with the industry’s most popular coding language.

Upon completion of this short course attendees will be fully equipped with the knowledge and skills to manage the data lifecycle and turn raw data into actionable insights, setting the stage for advanced data analysis and AI applications.

Duration: 2.5 hours

Sept 17, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Module 1:
Introduction

  • Introduction to Data Wrangling
  • Importance and role of data wrangling in the data analysis process.
  • Overview of data cleaning, transformation, and reshaping.

Module 2:
Data Cleaning  

  • Data sources
  • Techniques for obtaining data
  • Handling missing data.
  • Dealing with outliers and duplicates.
  • Addressing data quality issues

Module 3:
Data Transformation

  • Reshaping data
  • Pivoting, melting, and stacking
  • Handling categorical variables
  • Converting between data types
  • Normalization and scaling

Module 4:
Data Manipulation

  • The Pandas Library
  • Filtering, sorting, and aggregating data
  • Data Integration and Joining
  • Combining data
  • Merging and joining datasets

Week 2: Course 2

AI Engineering Foundations

This hand-on introduction to machine learning course will help you understand how machines can learn from data to make predictions and decisions. Throughout this course, you will learn key machine-learning concepts and their applications. You’ll gain hands-on experience with real-world datasets, like predicting real estate prices, and understand how to evaluate the performance of your models.

Knowing machine learning is crucial in today’s data-driven world, as it equips you with the tools to uncover insights from data, automate decision-making processes, and build intelligent systems that adapt and improve over time. By the end of this course, you’ll have a solid foundation in machine learning, enabling you to harness its power to analyze data, make informed decisions, and drive innovation.

Duration: 2.5 hours

Sept 24, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Introduction to Machine Learning & AI 

  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
  • Real-world applications of ML – Real Estate & Property Price Prediction
  • Features and labels. Model, training, inference.
  • Overfitting and underfitting.
  • Evaluation metrics

Data Profiling  & Data Cleaning

  • Understanding the Dataset with Data Profiling
  • Define the objective of the model
  • Data Exploration: examine features and  Identify relevant Features
  • Handling Missing Values
  • Detect and manage outliers

Feature Engineering & Dataset Splitting

  • Apply One-Hot Encoding
  • Converting variables
  • Normalize (scale) features
  • Feature Selection
  • Feature Creation
  • Data Transformation
  • Training and Test Sets
  • Cross-Validation:

Model Selection, Training, and Evaluation 

  • Model Selection
  • Algorithm Choice:
  • Baseline Models
  • Model Training
  • Performance Monitoring
  •  Model Evaluation
  • Evaluation Metrics
  • Mean squared error etc
  • Model Validation

Week 3:

LLMs: Architecture, Fine-Tuning & Evaluation

In the rapidly evolving field of AI, the “LLMs, Prompt Engineering, and Generative AI” course stands as a cutting-edge offering, designed to equip learners with the latest advancements in Large Language Models (LLMs), prompt engineering, and generative AI techniques. This course delves into the architecture and functioning of LLMs, the art of crafting effective prompts to guide AI responses, and the principles behind generating creative and coherent content. As these components are becoming integral to the AI stack, understanding them is essential for anyone looking to innovate, optimize, and excel in AI-driven applications.

Whether you’re a researcher, developer, or AI enthusiast, this course will provide you with the insights and hands-on experience needed to harness the power of these transformative technologies and stay at the forefront of the AI revolution.

Duration: 2.5 hours

Oct 1, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Module 1:
LLM Basics

  • Large Language Models (LLMs)
  • Transformer architecture
  • Applications of LLMs
  • Using LLMs out of the box
  • The process flow of chaining
  • Text summarization
  • Question answering
  • Text similarity

Module 2:
Prompt Engineering

  • Fundamentals
  • Prompt engineering examples
  • Manipulation prompt
  • Prompt engineering guardrails
  • Impact responses from prompting
  • Temperature – predictable versus creative outputs.

Module 3:
ChatGPT API 

  • Tokens & Prompting
  • Iterative Prompt Development
  • Evaluate OF prompt effectiveness
  • Guiding model behavior
  • Build your own Chatbot
  • Common shortfall of prompting
  • Hallucinations, Fairness, Biases, & Jailbreaking

Module 4:
Fine Tuning LLMs

  • Fine-tuning introduction
  • When to fine-tune
  • Model stages
  • Classification
    Topic Modeling, Sentiment analysis, and Entity recognition examples
  • Pre-training
  • Hardware and data considerations

Week 4:

Context and Memory for AI Agents 

This hands-on course provides a comprehensive introduction to Retrieval-Augmented Generation (RAG), exploring how it combines traditional retrieval techniques with generative AI models to deliver contextually accurate and real-time responses. Starting with the limitations of traditional search systems, the course delves into the components and workflow of RAG, including data processing, indexing, and querying methods. Through hands-on examples and best practices, learners will gain familiarity with key tools and embedding techniques, such as LangChain, vector stores and ranking, for building effective RAG applications.

The final module covers advanced frameworks and evaluation strategies, empowering students to implement and optimize RAG systems for various business and technical applications.

Duration: 2.5 hours

Oct 8, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Lesson 1:
Introduction

  • What is RAG
  • Traditional Methods
  • Key Advantages of RAG:
  • Reducing hallucinations
  • Real-time information access
  • Enhanced contextual relevance

Lesson 2:
RAG Workflow

  • Stages in RAG
  • Loading and Indexing
  • Storing and Querying
  • Evaluating RAG
  • Embeddings and similarity search
  • Common embedding models

Lesson 3:
Techniques & Tools

  • Chunking for Retrieval
  • Key Embedding Techniques:
  • Vector databases
  • Querying and Ranking
  • Similarity search and ranking
  • Embedding scoring
  • re-ranking

Lesson 4:
Frameworks and Evaluation

  • RAG Frameworks and Tools
  • LangChain and LlamaIndex:
  • Proprietary vs. open-source LLMs
  • RAG Orchestration
  • RAG System Performance
  • Evaluation methods

Week 5:

Building AI Agents and Agentic Workflows 

Introduction to AI Agents & RAG is a comprehensive course designed to equip students with the foundational knowledge and practical skills needed to build and implement AI agents. This course explores the core concepts of AI agents, including their types, key components, and tools, as well as advanced topics like agent chains and Agent Frameworks. 

Through a blend of theoretical insights and hands-on exercises, participants will learn how to leverage powerful frameworks like LangChain and LlamaIndex to create intelligent, context-aware systems that can handle complex tasks. Whether you are new to AI or looking to deepen your expertise, this course offers a structured pathway to mastering the latest advancements in AI agent technology.

Duration: 2.5 hours

Oct 15, 2026 | 2 pm ET

On-Demand Post Livestream 

Outline

Lesson 1:
AI Agents

  • Intro to AI Agents
  • Characteristics of AI Agents.
  • Types of AI Agents
  • Key Components of AI Agents
  • AI Agent building blocks

Lesson 2:
Building AI Agents

  • Building AI Agents
  • Components of AI Agents:
  • Agent Tools
  • AI Agent frameworks
  • Introduction to LangChain and LlamaIndex

Lesson 3:
Agent Chains 

  • AI Agent Chains
  • Agent Complex Sequences
  • Handling complex tasks
  • Frameworks for Building Agent Chains
  • Hands-on Notebook &Exercises

Lesson 4:
Agent Frameworks

  • LlamaIndex, BabyAGI, and Auto-GPT
  • AI Agent Challenges
  • Best Practices for Deploying AI Agents
  • Hands-on Notebook & Exercises

Week 6:

Capstone: Build and Deploy an Agentic AI Application

Capstone: Build and Deploy Agentic AI Application

In this capstone project, students will progressively build a useful, end-to-end AI agent by combining Retrieval-Augmented Generation (RAG), language model fine-tuning, and interactive AI agent design. Each module of the bootcamp will focus on constructing a core component of the agent, from fine-tuning a language model (LLM) on domain-specific data to setting up a real-time RAG pipeline. By the end, students will have a customized AI agent capable of delivering accurate, context-aware responses to user queries.

With hands-on guidance, template notebooks, and regular insights, students will build each part step-by-step, gaining confidence as they progress. This “build-as-you-go” approach not only strengthens technical skills but also provides a real-world AI application students can showcase, demonstrating their expertise in building the latest AI solutions.

Duration: 2.5 hours

Oct 22, 2026 | 2 pm ET

On-Demand Post Livestream 

Prerequisites

Prerequisites

As these are primer courses, no prior experience is necessary. Individual setup prerequisites will be provided prior to each course.

ON-DEMAND : Optional Prerequisites Courses

ON-DEMAND 1: Data & Generative AI Literacy  

Data is the essential building block of Data Science, Machine Learning, and AI. This course is the first in the series and is designed to teach you the foundational skills and knowledge required to understand, work with, and analyze data. It covers topics such as data collection, organization, profiling, and transformation as well as basic analysis. This course is aimed at helping people begin their AI journey and gain valuable insights that we will build up in subsequent SQL, programming, and AI courses.

Duration: 2.5 hours

On-Demand – Immediate Access on Ai+ 

ODSC EUROPE 2024:

Thursday, July 27th, 2024 | 1 PM BST (GMT+1)  8 AM ET (GMT-4)

ODSC WEST 2024:

Wednesday, July 31st, 2024 | 2 PM ET / 11 PM PT

Outline

Module 1:
Introduction to Data

  • What is Data
  • Why Data is Important
  • The Data Life Cycle
  • Understanding Data Types
  • Data Centric AI

Module 2:
Data Collection

  • Data Collection
  • Sourcing Data
  • External Data
  • Licencing Data
  • Data Collection Tools

Module 3:
Data Transformation

  • Data Transformation
  • Data Enrichment
  • Correlations and Outliers
  • Data Quality
  • Data Transformation Tools

Module 4:
Data Analysis

  • Data Profiling
  • Describing a Dataset
  • Data Shaping and Shaping Examples
  • Data Analsysis Tools

ON-DEMAND 2: Data Wrangling with SQL

This SQL coding course teaches students the basics of Structured Query Language, which is a standard programming language used for managing and manipulating data and an essential tool in AI.  The course covers topics such as database design and normalization, data wrangling, aggregate functions, subqueries, and join operations, and students will learn how to design and write SQL code to solve real-world problems. Upon completion, students will have a strong foundation in SQL and be able to use it effectively to extract insights from data.

The ability to effectively access, retrieve, and manipulate data using SQL is essential for data cleaning, pre-processing, and exploration, which are crucial steps in any data science or machine learning project. Additionally, SQL is widely used in industry, making it a valuable skill for professionals in the field. This course builds upon the earlier data course in the series.

Duration: 2.5 hours

On-Demand – Immediate Access on Ai+ 

Outline

Module 1:
Data Wrangling

  • Introduction to Data Wrangling
  • Why SQL for Data Wrangling?
  • Data Lifecycle Review
  • SQL Data Types
  • Sourcing & Collecting Data

Module 2:
Tables & Databases  

  • Data Storage
  • Popular Databases
  • Tables and Databases
  • Relational Data Design
  • Data Normalization
  • Foreign and Primary Keys

Module 3:
SQL Syntax

  • Introduction to SQL Syntax
  • SQL Query Syntax
  • Understanding SQL CRUD (Create, Read, Update, Delete)
  • Filtering Data with SQL
  • Data Profiling with SQL

Module 4:
Data Manipulation

  • Subqueries in SQL
  • Loading and Inserting Data
  • Transaction Control
  • Aggregate Functions and Groups
  • Join Operations
  • Updating Data with SQL

ON-DEMAND 3: Linear Algebra, Calculus, and Probability: The Math ML Experts Master

To be an outstanding data scientist or ML engineer, it doesn’t suffice to only know how to use ML algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of the math subjects underlying ML is required: linear algebra, calculus, and probability. When these math foundations are firm, it also makes it much easier to make the jump from general ML principles to specialized ML domains such as deep learning, NLP, machine vision, and reinforcement learning. This is because, the more specialized the application, the more likely its details for implementation are available only in academic papers or graduate-level textbooks, either of which assume an understanding of the math foundations.

Via hands-on code demos in Python, this workshop provides an overview of why each of the math subjects is essential in ML.

Duration: 2.5 hours

On-Demand – Immediate Access on Ai+ 

ON-DEMAND 4: Introduction to Math for Data Science

In this training, Thomas Nield (author of O’Reilly book “Essential Math for Data Science”) will provide a crash-course of carefully curated topics to jumpstart proficiency in key areas of mathematics. This includes probability, statistics, hypothesis testing, and linear algebra. Along the way you’ll integrate what you’ve learned and see practical applications for real-world problems. These examples include how statistical concepts apply to machine learning, and how linear algebra is used to fit a linear regression. We will also use Python to explore ideas in calculus and model-fitting, using a combination of libraries and from-scratch approaches.

Duration: 2.5 hours

On-Demand – Immediate Access on Ai+ 

ON-DEMAND 5: Statistics and Hypothesis Testing

Statistics and hypothesis testing are the foundation of all our data-driven innovations including machine learning and generative AI. But with all this availability of data and modeling, it is easy to lose sight of the scientific method and its role. In this session, we will learn the fundamentals of descriptive and inferential statistics, and how they relate to machine learning and data mining.

This will include understanding the relationship between a sample and a population, the p-value, and how we measure truth. We will also talk about the dangers of p-hacking and how it arises in data-driven environments.

Duration: 2.5 hours

On-Demand – Immediate Access on Ai+ 

Open Data Science

Ai+ | ODSC
One Broadway, 14th Floor
Cambridge, MA 02142
admin_aiplus@odsc.com

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