Five Courses. One Clear Path.
From Python basics to production AI agents. Pick one, or combine several. Every session is 1-on-1 with a dedicated tutor who adapts to your pace and goals.
Python Fundamentals
The foundation everything else builds on
Perfect for:
- Complete beginners with no coding experience
- Professionals who want to automate their workflow
- Career changers making their first move into tech
Python is the most in-demand programming language for AI, data, and automation. This course takes you from zero to confidently writing Python programs, with a focus on practical skills you can use immediately at work or in your own projects.
What you will build
By the end of this course
You will be able to write Python programs from scratch, work with external data and APIs, and have the foundation to move into AI, data science, or web development.
What you will learn
- Variables, data types, and control flow
- Functions and modular code
- Object-oriented programming
- Working with files, APIs, and libraries
- Building real command-line tools and scripts
- Debugging and problem-solving strategies
Data Science & Machine Learning
From raw data to working ML models
Perfect for:
- Analysts moving into data science
- Engineers who want to understand ML fundamentals
- Professionals preparing for ML or data science roles
Data is the foundation of modern AI. This course walks you through the full workflow: a thorough SQL introduction for pulling data, then the real focus: NumPy for numerical computing, Pandas for data manipulation, Matplotlib for visualization, and scikit-learn for building predictive models. You finish with a portfolio of real ML projects.
What you will build
By the end of this course
You will understand the full data-to-model pipeline: pull data with SQL, clean and explore it with Pandas and NumPy, visualize findings, and build real ML models with scikit-learn. You leave with a portfolio of data science projects.
What you will learn
- SQL essentials: queries, joins, aggregations (a solid intro, not the main focus)
- NumPy: arrays, vectorized math, linear algebra
- Pandas: data wrangling, cleaning, and transformation
- Matplotlib: data visualization and storytelling
- Statistics and probability fundamentals for ML
- Supervised learning: regression and classification
- Unsupervised learning: clustering and dimensionality reduction
- Feature engineering and model evaluation
- End-to-end ML projects with scikit-learn
AI Tools & Claude
Put AI to work in your daily workflow
Perfect for:
- Professionals who want to 10x their productivity with AI
- Entrepreneurs building AI into their products
- Anyone curious about what AI can actually do for them
AI tools like Claude, ChatGPT, and Copilot are transforming how professionals work. This course teaches you to use them effectively: prompt engineering, building AI-powered workflows, and integrating AI APIs into your own tools.
What you will build
By the end of this course
You will know how to use AI tools effectively, build custom AI-powered applications, and understand when and how to apply AI in your work.
What you will learn
- Prompt engineering fundamentals
- Advanced prompting: chain-of-thought, few-shot, system prompts
- Using Claude and ChatGPT for real work tasks
- Building AI-powered tools with the Anthropic API
- Automating workflows with AI
- Understanding model capabilities and limitations
LLM Engineering
Build production AI applications
Perfect for:
- Software engineers pivoting to AI
- Developers who want to build AI-powered products
- Technical founders building AI features
Go beyond using AI tools to building with them. This course covers Retrieval-Augmented Generation (RAG), AI agents, fine-tuning, and deploying AI applications. You will build real systems, not toy examples.
What you will build
By the end of this course
You will be able to design, build, and deploy production-grade AI applications using LLMs, RAG, and agent frameworks.
What you will learn
- LLM fundamentals: tokens, embeddings, context windows
- Retrieval-Augmented Generation (RAG) systems
- Building AI agents with tool use
- Vector databases and semantic search
- Fine-tuning and evaluation
- Deploying AI applications to production
Agentic AI Code
Build AI agents that ship real work
Perfect for:
- Engineers shipping AI-powered features
- Technical founders building AI products
- Senior developers ready to work at the AI frontier
The frontier of AI engineering. This course teaches you to build production AI agents using Claude Code, OpenAI's Codex, tool-use frameworks, and modern orchestration patterns. You finish with portfolio-ready agents that can automate real workflows.
What you will build
By the end of this course
You will be able to design, build, and deploy production-level AI agents that automate real workflows, with a portfolio of shipped projects to show for it.
What you will learn
- Claude Code and modern agent development
- Tool use and function calling patterns
- Multi-step agent workflows and planning
- Memory, state management, and persistence
- Multi-agent architectures and orchestration
- Evaluation, debugging, and observability
- Deployment patterns for production agents
Explore Every Lesson
Browse the complete path across all four tracks. Switch tracks, open any section, and see exactly what you will learn.
Professional Python Coding
11 sections · 199 lessonsLevel 1 · The Basics
Essential setup and tips for navigating the course: setting up the environment, tackling challenges, and getting the most out of the learning experience.
- 1Download Python
- 2Download an IDE (software to write code in)
- 3Create our first project
- 4Course overview
A strong foundation for future projects, covering essential Python and programming concepts and how coding actually works.
- 1Our first program
- 2Printing in Python
- 3Strings and special characters
- 4Creating variables
- 5String data types
- 6Handling data using slicing and string operators
The core elements of coding: blocks and statements, how code runs, parameters, and different expressions.
- 1Introduction to blocks and statements
- 2if, elif, and else statements
- 3Debugging in IntelliJ
- 4Testing operators
- 5Complex expressions (AND / OR)
- 6Simplified chained comparison
- 7Truth value testing
- 8Using "in" and "not in"
Loops made approachable, with each challenge designed to build understanding step by step.
- 1Introduction to for loops
- 2Debugging loops
- 3for loops over a range
- 4Nested loops
- 5Using break and continue in loops
- 6Introduction to while loops
- 7else statements in loops
Essential data handling and manipulation in Python, focused on lists and sequence types, applied through project-based work.
- 1Introduction to sequence types
- 2Mutable and immutable objects
- 3Iterating over a list
- 4The enumerate function
- 5Removing items from a list
- 6Creating and sorting lists
- 7Safely deleting items from a list
- 8Nested lists
Level 2 · Intermediate Topics Explored
The intermediate milestone: breaking code into modular components with functions.
- 1Defining functions in Python
- 2Parameters and arguments
- 3Debugging our parameters
- 4Functions calling functions
- 5Returning values and None
- 6Handling invalid arguments
- 7Default parameter values
- 8Writing a Fibonacci function (mini project)
- 9Function annotations and type hints
- 10Star args (*args)
Object-Oriented Programming simplified through 2 to 3 mini projects, building confidence to transition to any language.
- 1Object-oriented programming and classes
- 2Instances, constructors, self, and more
- 3Class attributes
- 4Methods and static methods
- 5Attribute accessibility
- 6Getters and setters
- 7Data attributes and properties
- 8Inheritance
- 9Subclasses and overloading
Dictionaries and sets: more complex than lists and tuples, with unmatched flexibility in data manipulation.
- 1Introduction to dictionaries
- 2Iterating over a dictionary
- 3Adding, changing, and removing items in a dictionary
- 4Computer building (mini project)
- 5Creating, updating, and values in dictionaries
- 6Shallow and deep copy with dictionaries
Level 3 · Practical Real World Skills
Working with external files: file handling (local or streamed) across various file types.
- 1Opening, reading, and parsing text files
- 2Parsing and using data from a text file
- 3Writing data to a text file
- 4JSON files 101
- 5Streaming JSON files using urllib
An introduction to SQL alongside Python, complementing programming knowledge for data analysis, data science, and AI development.
- 1Databases 101
- 2SQLite 101
- 3UPDATE, DELETE, and WHERE
- 4Database administration
- 5ORDER BY and JOIN our tables
- 6LIKE wildcards and creating views
- 7Restoring backups and functions
- 8Database SQL in Python
- 9Committing changes
Level 4 · Exercises and Projects
Ten exercises designed to test and apply everything learned.
- 1Number Guessing
- 2Summing Numbers
- 3Pig Latin
- 4Lists Exercise
- 5Dictionary Login System
- 6Function Inventory System
- 7Functions and Files Inventory System (continued)
- 8OOP Banking System
- 9Library Management System
- 10Restaurant Menu Manager
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