By Akshay Vikhe, OnlineCourseing contributor. Last updated: 13 October 2026 (full refresh; syllabus, projects, prices and ratings re-checked on Udacity’s nd898 page that day). Editorial update by Josh Hutcheson, OnlineCourseing editor. See our review methodology.
Some links on this page are affiliate links: if you enroll through them we may earn a commission at no extra cost to you. It does not change our verdict, and several of the alternatives we name pay us nothing.
Udacity’s Artificial Intelligence Nanodegree (nd898) is a 40-hour advanced program in classical AI: search, constraint satisfaction, automated planning, optimization, adversarial search and probabilistic graphical models. It has eight courses, four reviewed projects, and is taught by Sebastian Thrun, Thad Starner and Peter Norvig. On 13 October 2026 it was rated 4.6 from 241 reviews on Udacity’s page and cost $249 a month. It does not cover neural networks, large language models or AI agents.
QUICK VERDICT
Our rating: 3.8 / 5
- Best for: programmers and computer science students who want rigorous, reviewed practice with the algorithms underneath AI: search, planning, game playing and probabilistic reasoning.
- Not for: beginners in Python or math, and anyone whose goal is deep learning, generative AI or building AI agents.
Bottom line: a well-built program on foundations that still matter, taught by the authors of the field’s standard textbook, and short enough to finish in about a month. Its weakness is fit: most people searching for an AI course in 2026 want modern machine learning, which this program does not teach.
Udacity AI Nanodegree at a Glance
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| Detail | Current program (13 Oct 2026) |
|---|---|
| Program | Artificial Intelligence Nanodegree (nd898) |
| Level | Advanced (Udacity’s label) |
| Estimated time | 40 hours |
| Structure | 8 courses (one optional), 35 lessons, 4 reviewed projects |
| Topics | Constraint satisfaction, classical search, automated planning, optimization, adversarial search, probabilistic graphical models |
| Prerequisites | Intermediate and object-oriented Python, data structures and algorithms, calculus, linear algebra, statistics |
| Instructors | Sebastian Thrun, Thad Starner, Peter Norvig |
| Rating on Udacity | 4.6 from 241 reviews |
| Last updated by Udacity | September 30, 2026 |
| Price | $249/month, $846 for 4 months, or $999 one-time |
| Credential | Udacity Nanodegree certificate (not academic credit) |
Source: Udacity’s nd898 program page, read 13 October 2026. The previous version of this review described five courses, a three-month duration at 12 to 15 hours a week, and prices of $799 for three months or $314 a month. The current program has eight courses, takes an estimated 40 hours, and is sold on Udacity’s standard subscription.
Classical AI vs Modern AI: Is This the Right Program?
This is the most important question to answer before you enroll. Classical AI solves problems by representing them explicitly (states, actions, rules and probabilities) and then searching or reasoning over that representation. Modern machine learning learns patterns from large amounts of data. The two overlap, and serious AI engineers know both, but they are different skill sets.
| Classical AI (this Nanodegree) | Modern machine learning and generative AI | |
|---|---|---|
| Core idea | Represent the problem, then search, plan or reason | Learn a model from data |
| Typical techniques | A* search, constraint propagation, minimax, Bayes nets, hidden Markov models | Neural networks, transformers, large language models |
| Where it is used | Game AI, route and task planning, scheduling, logistics, robotics, some NLP steps | Vision, speech, chatbots, recommendations, content generation |
| Data needs | Little or none; you encode the knowledge | Large labeled or unlabeled data sets |
| Udacity program | Artificial Intelligence Nanodegree | Intro to ML with PyTorch, Generative AI, Agentic AI |
Choose this program if you want to understand how an agent plans a sequence of actions, why A* search finds optimal routes, how a game engine looks ahead, or how a probabilistic model tags parts of speech. These ideas are also the foundation of many technical interviews and of later topics such as reinforcement learning and agent planning.
Choose something else if your goal is to build with today’s AI tools. Our reviews of Udacity’s Intro to Machine Learning with PyTorch, Generative AI Nanodegree and Agentic AI Nanodegree cover those paths. If you are new to Python, start with AI Programming with Python (see the program).
| Your goal | Best Udacity starting point |
|---|---|
| Understand search, planning and probabilistic reasoning | Artificial Intelligence Nanodegree (this review) |
| Learn Python and NumPy for AI from scratch | AI Programming with Python |
| Train neural networks | Intro to Machine Learning with PyTorch |
| Build applications with large language models | Generative AI Nanodegree |
| Build AI agents that use tools | Agentic AI Nanodegree (4.8 from 581 reviews) |
What the program does not cover
- Neural networks, deep learning and training models on data sets.
- Transformers, large language models, prompt design or retrieval-augmented generation.
- Building AI agents with tool use or agent frameworks.
- Reinforcement learning as a full topic, beyond the search ideas it builds on.
- Computer vision, speech recognition with neural models, or deploying models to production.
- Data analysis and machine learning libraries such as scikit-learn or PyTorch.
If any of these is your goal, pick one of the programs in the table above instead, or take this one first as a foundation and add a machine learning program afterwards.
Why classical AI still matters in 2026
Classical methods did not disappear when neural networks took over. Game-playing systems such as DeepMind’s AlphaGo, which Udacity cites in the adversarial search project description, combined learned neural networks with tree search. Route planners, warehouse robots and scheduling software still run on search, planning and constraint solving. AI agents built on large language models often wrap the model in a plan, act and check loop that echoes classical planning. Knowing the classical layer helps you judge when a problem needs a learned model at all, and when a well-chosen algorithm is faster, cheaper and easier to verify.
Who should take it: three learner profiles
- The computer science student or self-taught developer who knows Python and data structures and wants AI foundations before machine learning. This is the program’s core audience and where it is best value.
- The game or robotics developer who needs pathfinding, planning and opponent AI in practice. The search, planning and adversarial courses map directly to that work.
- The interview candidate preparing for AI or software roles where graph search, heuristics and probability questions come up. Four reviewed projects are a structured way to practice.
- Not a fit: career changers new to coding, data analysts who want machine learning for business data, and anyone who mainly wants to build chatbots or agents.
Prerequisites: Are You Ready?
Udacity labels this program Advanced and lists twelve prerequisites. In practice they fall into three groups.
| Area | What Udacity lists | Self-check |
|---|---|---|
| Programming | Intermediate Python, object-oriented Python and programming basics, scripting, command line basics, Jupyter notebooks | Can you write a class with methods, use dictionaries and sets fluently, and run a script from the terminal? |
| Computer science | Basic data structures and algorithms, basic algorithms | Can you implement breadth-first search and explain what a priority queue is? |
| Math | Basic descriptive statistics, basic and differential calculus, linear algebra | Can you compute a conditional probability and read summation notation? |
The probabilistic models course is the most math-heavy part. If conditional probability and Bayes’ rule are rusty, review them before week four. If the programming self-checks fail, take a Python course first; the projects assume you can read and extend an existing codebase.
Syllabus: All 8 Courses and 4 Projects
| Course | Hours | Lessons | Project |
|---|---|---|---|
| Introduction to Artificial Intelligence | 6.3 | Welcome; introduction to AI; solving Sudoku with AI; environment and workspaces; constraint satisfaction problems | Build a Sudoku Solver |
| Classical Search | 3.9 | Introduction; uninformed search; informed search; classroom search exercise; additional search topics | None |
| Automated Planning | 6.8 | Symbolic logic and reasoning; introduction to planning; classical planning; additional planning topics | Build a Forward-Planning Agent |
| Optimization Problems | 4.0 | Introduction; hill climbing; simulated annealing; genetic algorithms; optimization exercise; additional topics | None |
| Adversarial Search | 5.2 | Introduction; search in multiagent domains; optimizing minimax; extending minimax; additional topics | Build an Adversarial Game Playing Agent |
| Fundamentals of Probabilistic Graphical Models | 13.8 | Probabilistic models; probability; spam classifier with naive Bayes; Bayes nets; inference in Bayes nets; part of speech tagging with HMMs; dynamic time warping | Part of Speech Tagging |
| After the AI Nanodegree Program | 0.2 | Additional topics in AI | None |
| Extracurricular (optional) | 1.2 | Hidden Markov models; advanced HMMs | None |
Course 1: Introduction to Artificial Intelligence
The first course introduces the field and goes straight to a concrete problem: solving Sudoku. You learn constraint propagation (eliminating impossible values) and backtracking search (trying a value and undoing it when it fails), the two techniques behind most puzzle solvers and many scheduling tools. You also set up the course environment. Udacity’s skill tags are constraint propagation, constraint satisfaction problems, backtracking search, AI algorithms in Python and search algorithms.
Course 2: Classical Search
This course covers how an agent finds a path through a space of states. Uninformed search includes breadth-first, depth-first and uniform cost search; informed search adds heuristics and the A* algorithm. A classroom exercise has you implement the algorithms. If you have taken an algorithms course, this will feel familiar; if not, it is the part to slow down on, because everything after it builds on search.
Course 3: Automated Planning
Planning combines search with symbolic logic. You represent a problem domain with propositional and first-order logic, define actions with preconditions and effects, and search for a sequence of actions that reaches a goal. The course covers planning graphs and planning heuristics, the same ideas used in logistics and in space mission scheduling. It ends with the forward-planning agent project.
Course 4: Optimization Problems
Some problems have too many possible states to search exhaustively. This course covers local search methods that improve a solution step by step: hill climbing, simulated annealing and genetic algorithms. There is an exercise but no graded project. These methods are widely used in operations research and in tuning machine-learning systems.
Course 5: Adversarial Search
Games add an opponent. You learn minimax search, how to make it faster, and how to extend it to deeper and more complex games. The project is to build an agent that plays a two-player game against other agents. This is the classic foundation of game AI and a stepping stone toward the search methods used in modern game-playing systems.
Course 6: Fundamentals of Probabilistic Graphical Models
The longest course, at 13.8 hours, moves from logic to uncertainty. It reviews probability, builds a naive Bayes spam classifier, then covers Bayesian networks and inference in them. It continues with hidden Markov models for part of speech tagging and dynamic time warping for comparing sequences. Udacity’s skill tags include the Viterbi and Baum-Welch algorithms and time-series analysis. The optional extracurricular course adds more on hidden Markov models.
Key Concepts the Program Teaches, Explained
If the syllabus terms are unfamiliar, these short definitions show what each course is actually about. Each one is a core idea in the program and a common interview topic.
Constraint satisfaction and constraint propagation
A constraint satisfaction problem has variables, possible values for each, and rules that limit which combinations are allowed. Sudoku is the classic example: 81 cells, values 1 to 9, and rules for rows, columns and boxes. Constraint propagation repeatedly removes values that break a rule, which often shrinks the problem so much that little search is needed. Timetabling and staff rostering tools use the same idea.
Uninformed search vs informed search
Uninformed search, such as breadth-first or depth-first search, explores possible states without any sense of direction. Informed search uses a heuristic, an estimate of how far a state is from the goal, to explore promising states first. The difference decides whether a route planner answers in milliseconds or never finishes.
The A* algorithm
A* is the best-known informed search algorithm. It ranks each state by the cost to reach it plus the heuristic estimate of the remaining cost. If the heuristic never overestimates, A* is guaranteed to find the cheapest path. It is used in game pathfinding, robot navigation and many routing problems, and it appears in both the search and planning courses.
Automated planning
Planning describes a world with facts that are true or false, and actions with preconditions and effects. A planner searches for a sequence of actions that turns the starting facts into the goal facts. Planning graphs and heuristics make that search feasible for larger problems. The same approach is used to schedule spacecraft operations, warehouse robots and manufacturing steps.
Local search: hill climbing, simulated annealing and genetic algorithms
When there are too many states to search systematically, local search starts with some solution and improves it. Hill climbing always moves to a better neighbor and can get stuck on a local peak. Simulated annealing sometimes accepts worse moves early on to escape those peaks. Genetic algorithms keep a population of solutions and combine the best ones. These methods are standard in optimization and operations research.
Minimax and game-tree search
In a two-player game, minimax assumes your opponent plays as well as possible: you choose the move that maximizes your outcome while they choose moves that minimize it. Because full game trees are enormous, the course covers ways to optimize and extend minimax, such as cutting off branches that cannot change the decision and limiting search depth with an evaluation function.
Bayesian networks
A Bayesian network is a graph of variables connected by probabilistic dependencies, such as symptoms depending on a disease. It lets you compute the probability of one variable given evidence about others. The program builds up to Bayes nets from basic probability and a naive Bayes spam classifier, then covers how to run inference in them.
Hidden Markov models and the Viterbi algorithm
A hidden Markov model describes a sequence where the true states are hidden but produce observable outputs, for example parts of speech (hidden) producing words (observed). The Viterbi algorithm finds the most likely sequence of hidden states for a given sequence of observations. That is exactly what the part of speech tagging project builds, and the same model family is used in speech recognition and bioinformatics.
Other Udacity AI Programs Compared
Udacity sells several AI programs, and the names overlap. This is how the Artificial Intelligence Nanodegree fits among them, based on each program’s Udacity page on 13 October 2026 and our reviews.
| Program | Focus | Level and rating | Our review |
|---|---|---|---|
| Artificial Intelligence (nd898) | Classical AI: search, planning, games, probabilistic models | Advanced; 4.6 (241) | This page |
| AI Product Manager (nd088) | AI and LLM products for product managers, no coding | Intermediate; 4.4 (378); 18 hours | Review |
| Agentic AI (nd900) | Building AI agents with large language models | 4.8 (581) | Review |
| Generative AI | Building applications with generative models | See review | Review |
| Intro to Machine Learning with PyTorch | Supervised and unsupervised learning, neural networks in PyTorch | See review | Review |
| AI Programming with Python | Python foundations for AI and machine learning | See review | Review |
A sensible order for someone who wants both foundations and modern skills is AI Programming with Python, then Intro to Machine Learning, then either this Nanodegree (for algorithms and interview depth) or the Generative AI and Agentic AI programs (for building with large language models). Because Udacity’s subscription covers its catalog, taking two programs inside one four-month bundle is often cheaper than paying for each separately.
The Four Projects in Detail
Each project is graded by a human reviewer against a published rubric. You can resubmit until it passes, and you need all four to earn the certificate.
| Project | What you build | Skills it proves |
|---|---|---|
| Build a Sudoku Solver | An agent that solves any Sudoku puzzle | Constraint propagation, backtracking search |
| Build a Forward-Planning Agent | An agent that automatically develops and executes plans to achieve its goals | Symbolic logic, planning graphs, heuristics |
| Build an Adversarial Game Playing Agent | An agent that plays a two-player game against other agents | Minimax, game-tree search optimizations |
| Part of Speech Tagging | A hidden Markov model that tags words with a universal part of speech tagset | Hidden Markov models, the Viterbi algorithm |
The Sudoku project is the gentlest and mostly tests whether your Python is ready. The planning and game-playing projects are where most of the program’s difficulty sits: they reward careful reading of the starter code and good heuristics more than lines of code. The part of speech tagging project connects the probabilistic material to a real natural language processing task, which makes it the most useful single portfolio piece for anyone heading toward NLP.
Udacity does not publish time estimates per project, but the course hours give a guide. The Sudoku solver sits inside a 6.3-hour course and usually takes a few evenings. The planning agent sits inside a 6.8-hour course and is where most learners spend extra time, because the heuristics take experimentation. The game-playing agent and the tagging project each take about a week at 10 hours a week, longer if you compare several strategies, which is what makes the write-up worth reading. Budget slack for at least one resubmission across the four projects.
For a portfolio, write a short README for each project explaining the problem, your approach and your results. Reviewers check the rubric; employers read the README.
Check the current AI Nanodegree syllabus →
Instructors
- Sebastian Thrun, founder and executive chairman of Udacity (Udacity’s description). He led Google’s early self-driving car work and co-founded Udacity.
- Thad Starner, professor of computer science at Georgia Tech (Udacity’s description), known for wearable computing research and his work as a technical lead on Google Glass.
- Peter Norvig, listed by Udacity as research director at Google, and co-author with Stuart Russell of Artificial Intelligence: A Modern Approach, now in its 4th US edition and the standard AI textbook in university courses.
Having the textbook’s co-author teach the material is a genuine strength: the program follows the structure many university AI courses use. The lectures date from the program’s launch, so expect older video production, while Udacity’s page shows the program itself was updated in September 2026.
Cost and How Long It Takes
Udacity sells access by subscription. On 13 October 2026 the program page listed:
| Plan | Price | Best if |
|---|---|---|
| Monthly | $249 a month | You can finish 40 hours and four projects in four to six weeks |
| 4-month bundle | $846 (about $212 a month) | You will also take another Udacity program, such as Intro to Machine Learning |
| One-time purchase | $999 for this program | Rarely the best value for a 40-hour program |
| Your pace | Time to finish | Likely cost |
|---|---|---|
| 15 hours a week | About 3 to 4 weeks | $249 |
| 10 hours a week | About 5 to 6 weeks | $249 to $498 |
| 5 hours a week | About 2 to 3 months | $498 to $846 |
Udacity’s Terms of Use give seven days from your first purchase to cancel for a full refund. After that, fees are not refunded, and canceling a monthly plan keeps access until the end of the paid month. A learner on r/Udacity in 2020 reported finishing the whole program in 21 days during a free month, which shows how much a fast, focused pace can save.
What Learners Say
Udacity’s page shows 4.6 from 241 reviews. The most recent ones, from June 2026, are five-star and short; one describes the curriculum as comprehensive and practical for understanding complex AI concepts. Udacity as a company is rated 4.5 from 3,406 reviews on Trustpilot. In the 60 newest reviews (29 August to 12 October 2026), 43 were five-star and praised the projects and code reviews; the few low-star reviews complained about slow project reviews and support tickets, broken workspace terminals, and errors in newer courses.
Reddit is more measured. In a 2020 r/Udacity thread, the learner who finished in 21 days asked whether Nanodegrees are useful to employers. A commenter who said he ran this program when it launched in 2017 replied that Nanodegrees are neither necessary nor sufficient for a job: the content is valuable, but jobs need breadth and depth beyond one specialized program. Other commenters advised listing the projects on a resume rather than the certificate. The original poster said parts taught by Peter Norvig were the highlight.
Sameh Kishta
AI instructor at ITI, via LinkedIn (2021): he thanked Egypt’s National Telecommunication Institute (NTI) for supporting his cohort through the Artificial Intelligence Nanodegree, and Udacity for the labs and modules, calling it a great journey.
Pros and Cons
| Pros | Cons |
|---|---|
| Taught by Thrun, Starner and Norvig, co-author of the standard AI textbook | No neural networks, LLMs or agents |
| Four substantial projects with human review | Advanced prerequisites in Python and math |
| Short: about 40 hours, realistic in a month | $249 a month is expensive next to free university material |
| Covers interview staples: search, A*, minimax, Bayes nets | Lecture videos date from the program’s launch |
| 4.6 from 241 reviews on Udacity’s page | Certificate is not academic credit and rarely decides hiring |
Career Value: Where Classical AI Is Used
This program does not prepare you for a specific job title, and Udacity’s own earlier FAQ said as much: it builds on existing engineering skills. Where the skills show up:
- Game development: pathfinding and opponent AI rely on A* and minimax.
- Robotics and autonomous systems: motion and task planning use search and planning algorithms.
- Operations research and logistics: scheduling, routing and resource allocation use constraint satisfaction and local search.
- Natural language processing: hidden Markov models and probabilistic tagging remain useful baselines.
- Technical interviews: graph search and dynamic programming questions are common for AI and software roles.
For pay context, the US Bureau of Labor Statistics reports a median of $135,980 for software developers, $120,230 for data scientists and $88,940 for operations research analysts (May 2025). It does not track AI engineers as a separate occupation, so treat unsourced AI salary figures with caution. Most AI and machine learning postings also expect deep learning experience, which means this program is usually one part of a longer path.
Free and Cheaper Alternatives
| Option | Cost | What you get | What you miss |
|---|---|---|---|
| UC Berkeley CS 188 | Free course site | Lectures, readings, homework and projects for UC Berkeley’s introductory AI course (Fall 2026 site) | Grading, review and a certificate |
| AIMA textbook and code | Book cost; code free | The reference text by Russell and Norvig, with code repositories | Structure and feedback |
| AI For Everyone (Coursera) | Coursera subscription | A 7-hour, non-technical overview by Andrew Ng, rated 4.8 | Any algorithms or coding |
| Udacity AI Nanodegree | $249 a month | Four reviewed projects and a certificate in about a month | Modern machine learning |
If you are disciplined and only want the knowledge, Berkeley’s course site plus the textbook cover the same foundations for free. The Nanodegree’s value is the review loop and the deadline: someone checks your planning agent and tells you why it fails, which is hard to get on your own. See our wider roundups of AI courses and machine learning courses for more options, or AI for Trading if your interest is finance.
A Six-Week Study Plan
| Week | Courses | Goal by the end of the week |
|---|---|---|
| 1 | Introduction to AI; Classical Search | Submit the Sudoku solver; implement BFS, DFS and A* in the exercise |
| 2 | Automated Planning | Get the planning agent working on the smallest problem |
| 3 | Automated Planning; Optimization Problems | Submit the forward-planning agent |
| 4 | Adversarial Search | Submit the game-playing agent |
| 5 | Probabilistic Graphical Models (first half) | Finish the naive Bayes and Bayes nets lessons |
| 6 | Probabilistic Graphical Models (second half) | Submit part of speech tagging, then cancel before renewal |
Submit each project as soon as it meets the rubric so the review wait overlaps with the next course.
How to get the most from the program
- Read the project rubric before the lessons. Knowing what the reviewer checks tells you which parts of each course to study closely.
- Keep the textbook open. The program follows the same order as Artificial Intelligence: A Modern Approach, so the matching chapters fill in proofs and examples the short videos skip.
- Write your own small tests. Search and planning bugs are silent: a wrong heuristic still returns an answer, just a slow or non-optimal one. Test on tiny problems where you know the correct answer.
- Use the reviewer feedback. A returned project with written comments is the most valuable thing the subscription buys. Resubmit quickly while the feedback is fresh.
- Turn each project into a portfolio entry. Push the code to GitHub with a README that explains the problem, the algorithm you chose and the results, including timing comparisons between search strategies.
Nanodegree Certificate
You receive a certificate like this one when all four projects pass. It is not accredited by a university and does not carry academic credit, but you can add it to LinkedIn and access it from your Udacity account. Employers will learn more from your project repositories than from the certificate itself.
Is the Udacity AI Nanodegree Worth It?
We rate the program 3.8 out of 5. For the right learner it is good value: a programmer with the math prerequisites gets four reviewed projects on core AI algorithms, taught by the people who wrote the standard textbook, in about a month for $249 to $498. For most people searching for an AI course in 2026, it is the wrong program, because it stops where modern machine learning begins. Decide based on your goal: foundations and interviews, yes; building with neural networks or LLMs, choose a different Udacity program.
Start the Artificial Intelligence Nanodegree →
Frequently Asked Questions
Is the Udacity Artificial Intelligence Nanodegree worth it?
It is worth it if you are a programmer who wants a rigorous, project-based grounding in classical AI: search, planning, optimization, adversarial games and probabilistic models. At 40 hours, most people finish in four to six weeks, so it costs about $249 to $498. It is not worth it if you want to work with neural networks, LLMs or AI agents, which it does not cover.
What is a Udacity Nanodegree?
A Nanodegree is Udacity’s name for a multi-course program built around projects that a human reviewer grades against a rubric. You earn a Udacity certificate when every project passes. It is not a college degree or academic credit.
How long does the AI Nanodegree take?
Udacity estimates 40 hours across eight courses and 35 lessons, plus four projects. At 10 hours a week that is about four weeks of content; allow five or six weeks including project reviews and resubmissions. The three-month estimate in older reviews is out of date.
How much does it cost?
On 13 October 2026 Udacity listed $249 a month, $846 for four months (about $212 a month), or $999 to buy the program outright. The $799 three-month price and $314 monthly price quoted in older reviews are out of date.
Does the AI Nanodegree teach deep learning, ChatGPT-style models or agents?
No. It covers classical, symbolic and probabilistic AI. For neural networks, start with Udacity’s Intro to Machine Learning with PyTorch; for large language models and AI agents, see the Generative AI and Agentic AI Nanodegrees.
What are the prerequisites?
Udacity lists intermediate and object-oriented Python, basic data structures and algorithms, command-line basics, Jupyter notebooks, descriptive statistics, calculus and linear algebra. It labels the program Advanced.
What projects are included?
Four: Build a Sudoku Solver, Build a Forward-Planning Agent, Build an Adversarial Game Playing Agent, and Part of Speech Tagging with hidden Markov models.
Who teaches the AI Nanodegree?
Sebastian Thrun (founder and executive chairman of Udacity), Thad Starner (professor of computer science at Georgia Tech) and Peter Norvig (listed by Udacity as research director at Google), who co-wrote the textbook Artificial Intelligence: A Modern Approach.
What jobs does it prepare you for?
It does not prepare you for one specific job. The skills apply to game AI, robot path planning, scheduling and logistics, optimization and some natural language processing pipelines, and they come up in technical interviews. Most AI and machine learning job postings also expect deep learning experience.
Is there a free alternative?
Yes. UC Berkeley publishes its CS 188 Introduction to Artificial Intelligence course site, which covers much of the same material, and the AIMA textbook site offers code and resources. What you do not get for free is human review of your projects and a certificate.
Akshay Vikhe is an aspiring data scientist with a strong interest in technology who reviews courses that add real value to students’ careers. Read his story.
Related Udacity guides: Every Udacity Nanodegree in 2026 (Hub) | Best Udacity Courses | AI Programming with Python | Intro to Machine Learning with PyTorch | Generative AI Nanodegree | Agentic AI Nanodegree | AI Product Manager Nanodegree
For the full Udacity breakdown, read our Udacity Nanodegree review (2026). Further reading: Anaconda (Python distribution) | Hubble Space Telescope scheduling | Markov and hidden Markov models.