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Quick answer
100 Days of Code: The Complete Python Pro Bootcamp is Dr. Angela Yu's Udemy course that teaches Python from no programming experience through a hundred projects, in 56 hours 48 minutes of lectures. It is a sound foundation for AI courses that require Python, but not an AI course: by its curriculum, data analysis and visualisation take ten of its 102 sections (Days 72 to 81, ending in a house-price prediction capstone), beside games, desktop apps, automation and Flask web development. We have not scored it.
Where we would start, among the ones that pay us
This review's subject: Angela Yu's 100 Days of Code, about 57 hours of Python from zero through a hundred projects. It is a Python course first; the review explains where it fits before AI study.
Many AI courses ask the same thing first: can you already write Python? One on this site says outright that it “will not cover Python basics”. Udemy counts 1,883,815 learners on 100 Days of Code, so a great many people have used it to answer that question. This review asks whether it is the right answer when the destination is AI, and how much of its hundred days point that way. It is built from the course page as read in a browser on 25 September 2026 and its full curriculum, every section expanded, read on 26 September 2026, and says so wherever those run out.
What is it?
One Udemy course, taught by Dr. Angela Yu, whom the page names as its Developer and Lead Instructor; Udemy styles the title “100 Days of Code™: The Complete Python Pro Bootcamp”. It is recorded lectures — 56 hours 48 minutes across 604 of them — in 102 sections named by day, and its promise is that you will “master the Python programming language by building 100 unique projects over 100 days.” It is taught in English, and Udemy showed it as last updated in September 2026.
Udemy labels it All Levels. We publish it as Beginner because the first requirement reads “No programming experience needed - I'll teach you everything you need to know”, and each of the first fourteen section titles carries the word Beginner. The titles then climb: Intermediate for Days 15 to 31, Intermediate+ for most of Days 32 to 57, and Advanced for most of Days 59 to 81, while the web-foundation and web-design days and the portfolio days from Day 82 carry no level. Nor is it tied to an exam: the page claims to prepare you for no certification.
What you'll actually learn
The page gives two views of the course: six promised outcomes, and a curriculum of 102 sections whose titles were all read on 26 September 2026 with every section expanded. The lecture titles inside the sections were not captured, so this review describes each stretch by its section titles only.
The six promised outcomes, in the page's words
- “You will master the Python programming language by building 100 unique projects over 100 days.”
- “You will learn automation, game, app and web development, data science and machine learning all using Python.”
- “You will be able to program in Python professionally”
- “You will learn Selenium, Beautiful Soup, Request, Flask, Pandas, NumPy, Scikit Learn, Plotly, and Matplotlib.”
- “Create a portfolio of 100 Python projects to apply for developer jobs”
- “Be able to build fully fledged websites and web apps with Python”
The hundred days, grouped by their own section titles
- Days 1 to 14, Beginner: variables, strings, control flow, lists, loops, functions, dictionaries, scope and debugging, taught through small games from Hangman to the Blackjack capstone.
- Days 15 to 31, Intermediate: a local development setup, object-oriented programming, Turtle graphics and the Snake, Pong and Turtle Crossing games, files and paths, list comprehension, Tkinter desktop apps, and errors, exceptions and JSON. Day 25 is “Working with CSV Data and the Pandas Library”.
- Days 32 to 40, Intermediate+: email and dates, APIs, keys and environment variables, SMS and Google Sheets, ending in a two-part flight-deal capstone.
- Days 41 to 44 and 58, Web Foundation: HTML, CSS and Bootstrap.
- Days 45 to 53: web scraping with Beautiful Soup and browser automation with Selenium, through a run of bots.
- The rest of Days 54 to 71: web development with Flask — templates, forms, SQLite and SQLAlchemy databases, a RESTful API, authentication and a blog capstone — with a web-design day, Git and GitHub, and deploying the application.
- Days 72 to 81, Advanced: data analysis and visualisation with Pandas, Matplotlib, Plotly, NumPy and Seaborn, including linear regression (Day 78) and t-tests (Day 80), ending in “Day 81 - Advanced - Capstone Project - Predict House Prices”.
- Days 82 to 100: nineteen “Professional Portfolio Project” sections, each labelled with a kind of project, from [Python Scripting] to [Data Science]. Days 82 and 83 list one lecture each; Days 84 to 100 display “0” where other sections list their lectures, and no length.
- Then a three-lecture “Final Stretch” and, last, “Bonus Lecture: Succeed in the Age of AI” (1 lecture, 2min).
The first ten titles cover the core of the language — variables, strings, control flow, lists, loops, functions and dictionaries — with classes following on Day 16 and files on Day 24. Selenium, Beautiful Soup, Flask, Pandas, NumPy, Plotly and Matplotlib, seven of the nine libraries the outcomes name, all appear in section titles; Scikit Learn appears in none, so this review cannot say where it is taught.
How much of it is data science and machine learning?
The plain answer is ten of the hundred days, by the curriculum's own section titles. The data work sits in one block near the end:
- Days 72 to 81 are data analysis and visualisation: ten of the 102 sections and 80 of the 604 lectures. Pandas on salaries by college major and on a LEGO dataset, Matplotlib, Google Trends time series, Plotly charts of the Android app store, NumPy arrays, linear regression with Seaborn, a Nobel Prize analysis and t-tests, ending in the house-price capstone. Udemy lists those ten sections at 130 minutes of length between them; short listed lengths are common from Day 38 on, and the page does not say how long the work in them takes.
- Day 25 comes first: “Working with CSV Data and the Pandas Library”, among the intermediate days.
- Machine learning, by title, is two days: Day 78's linear regression and Day 81's house-price capstone. Scikit Learn is one of the nine libraries the outcomes name, but no section title names it.
- Three portfolio days are labelled data science: Day 92 [Image Processing & Data Science] and Days 99 and 100 [Data Science]. Like every portfolio day from Day 84 on, they display “0” lectures and no length.
Everything else is general programming: games, desktop apps, APIs, scraping and automation, and Days 54 to 71 of Flask web development, databases, authentication, Git and deployment. Neither the outcomes nor any of the 102 section titles name anything past classical machine learning: no PyTorch, TensorFlow or Keras, no neural networks, no language models, and the only section with AI in its title is the two-minute bonus lecture. So expect ten days of Pandas, NumPy, charts and basic statistics with a linear regression; do not expect deep learning or language models, where several AI courses on this site begin.
Not sure this is the right one for you?
Tell the picker about your background and what you want the certificate to do, and it narrows the list to the one or two courses we would start with — from the same vetted list we rank from.
Try the AI Certification Picker →The details: cost, time, prerequisites
Cost. Sold per course on Udemy. This review prints no price: Udemy prices by country, so a single figure would be wrong for most readers. Check the price the course page shows you.
Time. Udemy lists 56 hours 48 minutes across 604 lectures, under six minutes a lecture on average, and its includes box lists 52 hours of on-demand video, 23 coding exercises, 236 articles and 167 downloadable resources. That is the length of the content, not of the work: the hundred projects are yours to write, the page gives no estimate of how long they take, and Days 84 to 100 display “0” lectures and no length.
Prerequisites. The page's requirements read, in full: “No programming experience needed - I'll teach you everything you need to know”; “A 64-bit Mac or PC computer with 4GB of memory and access to the internet”; “No paid software required - I'll teach you how to use PyCharm, Jupyter Notebooks and Google Colab”; and “I'll walk you through, step-by-step how to get all the software installed and set up”. No maths, no earlier course — but a Mac or PC, not a tablet.
Certificate. Udemy's certificate of completion, its standard for a paid course. Accreditation was not checked, the page names no exam the course prepares for, and nothing we read on it says the projects are graded. The projects, not the certificate, are the evidence of what you can do.
Learner evidence. 436,671 ratings averaging 4.7 out of 5 from 1,883,815 learners, as Udemy showed them on 25 September 2026. That says the teaching works for most people who get far enough to rate it; it says nothing about how many reached day one hundred.
How we checked this. We list the cost as a one-off Udemy purchase, with no single figure published. Source: The course page, read logged out on 25 September 2026. Udemy prices each course by country and runs frequent site-wide sales, so a figure read in one place on one day is wrong for most readers; the course page shows the price for your country. We re-check every price against the provider before each monthly review, and publish no figure we cannot source — where a provider prices regionally, we say so rather than quote a number that is wrong for most readers.
Pros and cons
Pros
- Starts from zero: the first requirement is “No programming experience needed”, and the first ten days cover the core of the language
- Teaches by building: its section titles run from Hangman and Pong through desktop apps, bots and a Flask blog to a house-price capstone
- Gives ten days (72 to 81) to Pandas, Matplotlib, Plotly, NumPy, Seaborn and basic statistics, the data tools AI courses build on
- Teaches PyCharm, Jupyter Notebooks and Google Colab, with no paid software required
- 436,671 ratings averaging 4.7 out of 5, and updated in September 2026
Cons
- Not an AI course: data science is ten of the hundred days, and machine learning, by section title, is one linear-regression day and a house-price capstone
- Neither its outcomes nor its 102 section titles name a deep-learning framework or language-model tooling, where several AI courses on this site begin
- 56 hours 48 minutes across 604 lectures, before any project time, is a long road for a reader who only needs enough Python to start an AI course
- The certificate is a Udemy completion record, tied to no exam, with accreditation unchecked
Who should take it (and who shouldn't)
Take it if you have never programmed and the AI course you want lists Python as a requirement; if you learn best by finishing small things, like the games of the first fourteen days, rather than data exercises; or if you want Python for web development and automation as well as data.
Skip it if you already write Python: the fourteen Beginner days are fundamentals you have. Skip it, too, if you want only enough Python to begin an AI course by the shortest route: a track that teaches Python through data, such as DataCamp's Data Analyst in Python, reaches pandas directly, where this course's data block begins on Day 72. And if you want to work with AI without writing code, a hundred days of Python is the wrong investment; our list of the best AI certifications with no coding starts there.
Which AI courses on this site assume Python
If Python is the reason you are here, these are the AI courses on this site that expect it.
- AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents on Udemy: “Familiarity with Python. This course will not cover Python basics and is completed in Python.”
- PyTorch for Deep Learning Bootcamp on Udemy: “Basic Python knowledge is required”. It teaches PyTorch from the fundamentals, not Python.
- DataCamp's Deep Learning in Python, Machine Learning Fundamentals in Python and Developing Large Language Models tracks: our reviews give working Python as the prerequisite for all three, and Associate AI Engineer for Developers expects you to read and write it comfortably from its first course.
- IBM AI Engineering on Coursera expects you to be comfortable writing Python, and the Machine Learning Specialization uses Python throughout, with graded labs in notebooks.
What those courses assume is not a hundred days of Python. It is reading and writing ordinary code — variables, conditions, loops, functions, lists and dictionaries — without fighting the language, and by their titles the first ten days of this course cover that list. What follows, by the section titles, is breadth — desktop apps, APIs, automation, Flask web development and, from Day 72, data analysis — that no course above requires, so you can start an AI course once the fundamentals feel ordinary.
Our guide to Python courses for AI and data science sets more of the foundation options side by side. And if you would rather skip Python altogether, DataCamp's AI Fundamentals track is one of the AI courses on this site that needs none.
How it compares to the alternatives
Four Beginner-level routes into Python, from 36 to 90 hours, all of which start from no programming experience: two Udemy courses and two DataCamp tracks, compared on how much of each is data and machine-learning work.
| Course | Provider | Level | Time | Starts from | Data and ML content | Enrol |
|---|---|---|---|---|---|---|
| 100 Days of Code: The Complete Python Pro Bootcamp | Udemy | Beginner | ~56.8 hrs | No programming experience | Ten of 102 sections (Days 72 to 81): Pandas, Matplotlib, Plotly, NumPy, Seaborn regression and t-tests, ending in a house-price prediction capstone | Udemy → |
| Python Mega Course: Build 20 Real-World Apps and AI Agents | Udemy | Beginner | ~49.07 hrs | No prior programming experience | “Data science tools” and data processing among its outcomes, with LangChain agents | Udemy → |
| Data Analyst in Python | DataCamp | Beginner | ~36 hrs | Introduction to Python, its first course | Nine courses: Python basics, then pandas, statistics, charts, sampling and hypothesis testing; no machine learning | DataCamp → |
| Associate Data Scientist in Python | DataCamp | Beginner | ~90 hrs | Introduction to Python, its first course | 23 courses: Python, pandas, cleaning, charts and statistics, ending on three scikit-learn machine-learning courses | DataCamp → |
The closest like-for-like is the other Udemy row. Python Mega Course: Build 20 Real-World Apps and AI Agents, taught by Ardit Sulce, also starts from nothing and runs 49 hours 4 minutes across 467 lectures in 57 sections. Its first twelve section titles read as one app built up concept by concept, from user input and lists to error handling and functions, and its outcomes name a destination 100 Days of Code does not: “Integrate Python with modern AI tools like LangChain to build intelligent Agents and Automations.” On learner evidence, the Python Mega Course: Build 20 Real-World Apps and AI Agents showed 73,879 ratings averaging 4.5 out of 5 from 374,942 learners on 25 September 2026.
The DataCamp rows answer a narrower question: how to learn Python through data. Data Analyst in Python is nine courses and 36 hours, from Introduction to Python through pandas, statistics, charts, sampling and hypothesis testing, with no machine learning. Associate Data Scientist in Python holds all nine of those among its 23 courses, at 90 hours, and ends on three scikit-learn machine-learning courses. Both run on a DataCamp subscription, which DataCamp prices by country, and neither promises web development, automation or games. If your destination is data and machine learning, either track gets there more directly; if you want to be a general Python programmer first, the hundred days serve you better.
Is it worth it?
For a complete beginner who wants to program in Python, the page makes a strong case: it starts from nothing, covers the fundamentals in order, teaches by building, was updated in September 2026, and carries a very large number of very high ratings. For a reader headed for AI it is a sound but indirect foundation: most of its hundred days are general programming, data science is ten of them (Days 72 to 81), and its machine learning, by section title, stops at a linear regression and a house-price capstone.
If Python is the gap and you want breadth, take it, and start your AI course once the fundamentals feel ordinary. If you want the shortest road to data work, a data-first track gets there faster. We have not scored it yet: until a course has been through our six-factor methodology, this page says “Not scored” rather than show a figure nothing supports.
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Frequently asked questions
Is 100 Days of Code enough Python for AI courses?
The AI courses on this site that require Python expect you to read and write ordinary code: variables, conditions, loops, functions, lists and dictionaries. By their titles, the first ten days of 100 Days of Code cover that list, so you need not reach day one hundred before starting one.
It does not teach the AI itself: its ten data days (72 to 81) cover Pandas, NumPy, charts and a linear regression, but neither its outcomes nor its section titles name a deep-learning framework or language-model tooling. Treat it as the foundation, then take a course that starts where it stops.
How much of 100 Days of Code is data science and machine learning?
Ten of its hundred days, by the curriculum's section titles. Days 72 to 81 are data analysis and visualisation — Pandas, Matplotlib, Plotly, NumPy, linear regression with Seaborn, and t-tests — ending in “Day 81 - Advanced - Capstone Project - Predict House Prices”. That is ten of 102 sections and 80 of 604 lectures, and Day 25 introduces CSV data and Pandas earlier on.
Machine learning, by title, is Day 78's linear regression and that capstone; Scikit Learn is named in the outcomes but in no section title. Three portfolio days are labelled data science; like every portfolio day from Day 84 on, they display “0” lectures and no length. The rest is games, desktop apps, automation, APIs and Flask web development.
Do I need any programming experience to start?
No. The first requirement on the course page reads “No programming experience needed - I'll teach you everything you need to know”, and the first day is about variables. The other requirements are practical: a 64-bit Mac or PC with 4GB of memory and internet access, and no paid software, because the course teaches PyCharm, Jupyter Notebooks and Google Colab and walks you through installing them.
That is why we publish it as Beginner although Udemy labels it All Levels.
Is the 100 Days of Code certificate worth anything?
It is Udemy's certificate of completion, the standard one for a paid course. Accreditation was not checked, the page names no certification exam the course prepares for, and nothing we read on it says the projects are graded, so it tells an employer that you finished a course, not what you can do.
The course's own outcomes point at better evidence: “Create a portfolio of 100 Python projects to apply for developer jobs”. Code you can show is worth more than the certificate; if you need a credential recruiters screen for, look at an assessed certification.
Should I take 100 Days of Code or the Python Mega Course?
Both are Udemy courses that start from no programming experience. 100 Days of Code runs 56 hours 48 minutes across 604 lectures and promises a hundred projects, with web development, automation, games, data science and machine learning among its outcomes. Python Mega Course: Build 20 Real-World Apps and AI Agents runs 49 hours 4 minutes across 467 lectures, promises twenty apps and names LangChain agents among its outcomes.
If you want your Python course to lead toward AI applications, choose the Mega Course; if you want the larger project count and the data libraries named up front, choose 100 Days of Code.