Complete A.I. & Machine Learning, Data Science Bootcamp (Udemy.com)
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
Created by: Andrei Neagoie
Last updated February 2026
Our take
Based on the ratings of 30,982 students, a sample of their written reviews and the syllabus, as the course stood in February 2026. No course pays to be reviewed.
A 44 hour, 384 lecture bootcamp that runs from Python basics through pandas, NumPy, Matplotlib and scikit-learn to neural networks in TensorFlow 2. Andrei Neagoie co-teaches with Daniel Bourke, and reviewers say both teach well and keep it engaging. The syllabus is built as two paths: programmers can skip the two Python sections parked at the end, while true beginners start there and loop back. It suits people with no math or coding background who want one long, structured route to a first deep learning model rather than a rigorous theory course.
Students praise the energy. Reviewers call it engaging and fun to watch, and one likes the framing where the learner is treated as a new hire handed real scenarios, which keeps the material focused. The rating spread backs that up: about 63 percent five stars from roughly 31,000 ratings, with under 2 percent at one or two stars. The low-star reviews are consistent. They say the course leaves out unsupervised learning, treats some concepts as self explanatory, and pads lessons with informal chat. One reviewer called it information overload with not enough practice, and the numbers agree: 384 lectures but only two quizzes and one coding exercise.
The course was refreshed in February 2026, so the outdated complaints in a few reviews are hard to square with the update log, though it was first published in January 2020. Next to Machine Learning A-Z, which also covers R, this one is Python and TensorFlow only. A good pick for a first pass at the whole field with two portfolio projects along the way. Not the right choice for anyone who needs clustering, statistics depth, or graded practice.
Pros
- Two milestone projects, a classification task and a time series model, with all notebooks provided on GitHub
- Two separate Python sections so non-programmers can start from zero and coders can skip straight to pandas
- Refreshed in February 2026, with 384 lectures and 61 articles spread across 20 sections
- Reviewers call the teaching engaging and fun to watch, with a new-hire framing that keeps lessons focused
Cons
- Unsupervised learning is in the blurb, but one low-star reviewer says it is missing and the course feels incomplete
- Only two quizzes and one coding exercise across 44 hours, so practice is mostly watch and retype
- Informal chatter and lightly explained concepts frustrate reviewers who wanted more depth
Great setup for a course, Did a few initial sections, looking forward to completing. Engaging, especially with the idea that you are hired, and here are scenarios, keeps the material focused.
Labelled all levels, but it is really a beginner course; anyone already doing ML work will find the concepts too thin.
Featured in our guide: The 5 Best Machine Learning Courses in 2026
What you will learn
- Become a Data Scientist and get hired
- Master Machine Learning and use it on the job
- Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
- Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
- Present Data Science projects to management and stakeholders
- Learn which Machine Learning model to choose for each type of problem
- Real life case studies and projects to understand how things are done in the real world
- Learn best practices when it comes to Data Science Workflow
- Implement Machine Learning algorithms
- Learn how to program in Python using the latest Python 3
- How to improve your Machine Learning Models
- Learn to pre process data, clean data, and analyze large data.
- Build a portfolio of work to have on your resume
- Developer Environment setup for Data Science and Machine Learning
- Supervised and Unsupervised Learning
- Machine Learning on Time Series data
- Explore large datasets using data visualization tools like Matplotlib and Seaborn
- Explore large datasets and wrangle data using Pandas
- Learn NumPy and how it is used in Machine Learning
- A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
- Learn to use the popular library Scikit-learn in your projects
- Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
- Learn to perform Classification and Regression modelling
- Learn how to apply Transfer Learning
Course content
20 sections · 384 lectures · 44 hours of video 2 quizzes, 1 coding exercise, 61 articles
- 1Introduction 2 free previews5 lectures · 16 min
- 2Machine Learning 101 4 free previews11 lectures · 45 min
- 3Machine Learning and Data Science Framework 1 free preview15 lectures · 1.1 hours
- 4The 2 Paths3 lectures · 4 min
- 5Data Science Environment Setup 1 free preview13 lectures · 1.8 hours
- 6Pandas: Data Analysis15 lectures · 1.7 hours
- 7NumPy19 lectures · 2.2 hours
- 8Matplotlib: Plotting and Data Visualization 1 free preview20 lectures · 2.3 hours
- 9Scikit-learn: Creating Machine Learning Models 1 free preview52 lectures · 7.9 hours
- 10Supervised Learning: Classification + Regression1 lecture
- 11Milestone Project 1: Supervised Learning (Classification) 1 free preview25 lectures · 3.6 hours
- 12Milestone Project 2: Supervised Learning (Time Series Data)21 lectures · 3.2 hours
- 13Data Engineering 1 free preview13 lectures · 58 min
- 14Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2 1 free preview44 lectures · 7.1 hours
- 15Storytelling + Communication: How To Present Your Work8 lectures · 27 min
- 16Career Advice + Extra Bits14 lectures · 1.5 hours
- 17Learn Python49 lectures · 4.2 hours
- 18Learn Python Part 251 lectures · 4.8 hours
- 19Extra: Learn Advanced Statistics and Mathematics1 lecture
- 20Where To Go From Here?
Who it is for
The instructor says it suits
- Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python
- You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
- Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
- You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry
- You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
- You want to learn to use Deep learning and Neural Networks with your projects
- You want to add value to your own business or company you work for, by using powerful Machine Learning tools.
What you need before you start
- No prior experience is needed (not even Math and Statistics). We start from the very basics.
- A computer (Linux/Windows/Mac) with internet connection.
- Two paths for those that know programming and those that don't.
- All tools used in this course are free for you to use.
How it compares
Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:
- Rating4.7 out of 5higher than 93% of them
- Reviews30,982#4 of 156 by number of reviews
- Length44 hourslonger than 95% of them
- Last updatedFebruary 2026more recently than 80% of them
Course Description
Become a complete A.I., Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery!
Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).
This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.
The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!
The topics covered in this course are:
- Data Exploration and Visualizations
- Neural Networks and Deep Learning
- Model Evaluation and Analysis
- Python 3
- Tensorflow 2.0
- Numpy
- Scikit-Learn
- Data Science and Machine Learning Projects and Workflows
- Data Visualization in Python with MatPlotLib and Seaborn
- Transfer Learning
- Image recognition and classification
- Train/Test and cross validation
- Supervised Learning: Classification, Regression and Time Series
- Decision Trees and Random Forests
- Ensemble Learning
- Hyperparameter Tuning
- Using Pandas Data Frames to solve complex tasks
- Use Pandas to handle CSV Files
- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
- Using Kaggle and entering Machine Learning competitions
- How to present your findings and impress your boss
- How to clean and prepare your data for analysis
- K Nearest Neighbours
- Support Vector Machines
- Regression analysis (Linear Regression/Polynomial Regression)
- How Hadoop, Apache Spark, Kafka, and Apache Flink are used
- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
- Using GPUs with Google Colab
By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.
Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.
Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.
Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.
You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!
Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!
Taught By:
Daniel Bourke:
A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.
My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.
Instructor Details
- 4.7 Rating
30,982 Reviews
Andrei Neagoie
Andrei is the instructor of some of the highest rated programming and technical courses online. He no longer teaches on Udemy. Instead, he is now the founder of ZTM Academy which is one of the fastest growing education platforms in the world
ZTM Academy is known for having some of the best instructors and success rates for students.
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Reviews
By MANIKANTA VASKURI on 5/19/2026
yeah it is good for beginers and i am currently at the pandas so that why but i dont the correct order on how to learn ml i hope this course is covering it in the correct order because i though in this course they will tell about nuerak netweors and the concet behing it and correct deeo meaning instead of formilae furst but he is teachin gthe coding part . okay i hope in the next nueral netwoks cncet would b e great
By Harikamaraj on 4/5/2026
This course exceeded my expectations. The concepts were explained clearly with practical examples, making even complex topics easy to understand. The hands-on exercises and real-world applications helped me build confidence in AI and Machine Learning. Overall, it was highly informative and well-structured—definitely a great learning experience.
By Rashmika Mahindrakar on 2/27/2026
I really like how they included a workflow style for teaching (sure I am gonna forever have Keiko Corp as my favorite company haha). Thank you so much for making this course. Gonna go build really cool stuff and make Daniel, Andrei and even Bruno proud hehe :) PS - Daniel is way too funny haha, so I bet you on this - you are never gonna get bored during the videos
By Anas Saleem on 2/18/2026
Course is Perfect. I learned a lot. but I honestly think that Daniel DIDN'T teach us the best approach to get the perfect accuracy of the *Dog Vision*. Because on Kaggle he get score of 20 and top result have ~0.002 score.... Else everything is perfect.
By Jahangir Asgarov on 2/2/2026
This machine learning course is very useful. The instructors’ explanations are engaging and make the lessons enjoyable instead of boring. Everything is explained clearly, which makes learning much easier. Thank you for your effort and hard work.
By Rishi Kumar on 1/28/2026
The First Video is Totally Best in terms of Motivation + Logical idea behind the Scenario when we enter in the industry and lack of hesitation and Poor Knowledge never stands OR helps in anyway, that's the real truth explained by the 2 instructors in a very Cool way , Thanks a lot @Andrei Neagoie Sir and @Daniel Bourke Sir for helping us all the worldwide students including me through the Knowledge ( Course + each Platform ) , I HOPE THIS COURSE HELPS ME A LOT IN EVERY USEFUL CASE & ALSO I GIVE MY BEST IN IT. Yours Sincerely Rishi Kumar
By Shem Njiiri on 1/24/2026
I was intrigued by the authenticity of the course, as Daniel made the typos, they made the course more real and relatable and also aided me in improving my debugging skills as I saw them as norm. This eased the pressure of becoming a perfect programmer making it easier to learn. THANK YOU guys for the wonderful eye opening course .
By Adrian Stanley on 1/14/2026
Great introduction to ML and really enjoyed being able to attempt real world problems and starting some kaggle competitions. Obviously in this python world there are a couple of version related issues to overcome (or not see when instructor is seeing them, but nothing that gemini couldn`t help me solve. I went for local GPU acceleration which I had to set up by myself but even on a five year old gaming laptop, it had better performance that colab without the timeouts... but would have been brave to try to accomodate everyones setup! Shame second course is only available on ZTM and not UDEMY as already stretching my training budget with udemy course with its broad coverage of educational topics
By Seoyeon Park on 12/25/2025
I started this course because I got a recommendation from an acquaintance. I've already taken a lecture on machine learning at my college, and this course covers practical aspects of the topic, which I needed alongside the theoretical knowledge. Hopefully it'll help my future career! Thank you for all the efforts that you made for this course.
By Pablo Strunz on 12/19/2025
It's amazing how Andrei and Daniel were able to pack so much knowledge into a single course and still make it easy to understand. I would definitely recommend this course to anyone interested in data science and machine learning.
Quality Score
No CourseDuck member has rated this course yet. Taken it? Give each part a thumbs up or down.
Overall Score : 94 / 100










![[2026] Machine Learning: Natural Language Processing (V2)](/programming/machine-learning/images/4294434_daf2_5.jpg?v=1789268237)

