At 10 hrs/week
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Formal prerequisites include basic knowledge of algebra, and basic programming in any language.See detailed requirements.
Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.Use a Pre-trained Image Classifier to Identify Dog Breeds
Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.
Learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.
Learn the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example.
Gain a solid foundation in the hottest fields in AI: neural networks, deep learning, and PyTorch.Create Your Own Image Classifier
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Ortal Arel has a PhD in Computer Engineering, and has been a professor and researcher in the field of applied cryptography. She has worked on design and analysis of intelligent algorithms for high-speed custom digital architectures.
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.
Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.
Grant Sanderson is the creator of the YouTube channel 3Blue1Brown, which is devoted to teaching math visually, using a custom-built animation tool. He was previously a content creator for Khan Academy.
Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Mike is a Content Developer with a BS in Mathematics and Statistics. He received his PhD in Cognitive Science from the University of Irvine. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead.
As a data scientist at Looplist, Juno built neural networks to analyze and categorize product images, a recommendation system to personalize shopping experiences for each user, and tools to generate insight into user behavior.
Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.
This was a very good program, I learned a lot from it. Now I have a good understanding of AI network and how they are trained and used. and I'm ready to dive a bit deeper, and go to tthe next level All material presented was sufficient to complete the project. But with many personal interruptions, I felt that I had to do a treasure hunt to find the info to put together the final projects; we had bits and pieces of all the info, but with variation on implementation, and probably with some confusion on Pytorch usage (reading from files, or directory structure, or going from numpy to pytorch, etc). This was compounded by the inability to train torchvision VGG16 model with CUDA on my own NVidia 780 GTX TI with 3GB of ram (I was running out of memory, but I was able to do “prediction”). And the fact that I was getting disconnected every so often when running on Udacity server with CUDA (repeat, restart, etc), and not getting convergence, while trying to debug the code, and trying to internalize the transform and other factors, made it a bit difficult to complete some tasks in one sitting. I wish there were some hints, and more indication on what to look for; e.g. it would have been nice to about trying different lower value “LearnRate”, I would have saved a week. And there are many gotchas too: like saving the model parameters ( which ones), and if you save in CUDA, the model would not run on my desktop ( which has no CUDA). Also, it was nice to have classmate comments and solution column, but the web-format was painful to use (hard to read, and follow)… But I ’m very thankful for the few hints I got from other classmates.
It is a great course for getting started with AI. I would say I found it easy to cope up even when I have a day job. I have experience with Python and maths involved in the course. My reason behind taking this course was to develop an intuition in solving AI/DL problems and I am not disappointed. At the same time, I am not sure about someone who is completely new to the field. There is a lot to cover, python is a great programming language and numpy, pandas, seaborn are quite dense topics on their own. Then comes the maths. Great lessons by Grant Sanderson, really helpful visualizations for all the vector and matrix operations. In the end I feel the course managed to give me a good taste of what I was looking for: how to solve problems using AI (I come from a web development background). I feel confident about myself now. Cheers!
Great program. It's clear, meaningful and cuts out a lot of noise when compared to other training programs. Also, I'm studying at one of the most elite tech universities in the world, and I can say that these modules are a far better source of knowledge (better structures, better content and far more practical in terms of delivery methods). The only thing I'd recommend is to have the option to be able to download a digital PDF of all the content for future references. I'm very grateful for Udacity and it's brilliant Nanodegrees!
I like this course because it gives knowledge both on basics of Python and on basics of machine learning. Some exersizes were challenging, so I needed more time to complete them. Study materials are informative and well structured. I also like the supportive services Udacity provides, like personal mentor, students forum, planner, study challenge #30DaysOfUdacity. Thanks to all this my studies were organized, regular and thus effective.
My program is going well. This is my second Nanodegree program, thus I am familiar with the concept and the structure of it. That is why it is easier to plan my study and to learn what I am looking for. I think of keeping learning about AI related courses at the advanced level, thus try not to hurry up but to advance step by step every single day. Thanks for the wonderful course and the people who are there to help the students.
Time management was hard. I somehow completed the final project but i doubt i will remember the python libraries in coming weeks. Udacity should enforce, student not only learn but able to recall. I suggest it introduce a minimum check-in time of 5 hours/week where the system monitors student performing some programming tasks. This repetitive work will go long way towards recalling libraries even after nanodegree completion.
Numbers don't lie. See what difference it makes in career searches.*
Career-seeking and job-ready graduates found a new, better job within six months of graduation.
Average salary increase for graduates who found a new, better job within six months of graduation.
AI-powered increases in safety, productivity, and efficiency are already improving our world, and the best is yet to come! As it becomes increasingly evident how impactful AI can be, demand for employees with AI skills increases—demand is in fact already skyrocketing.
The AI Programming with Python Nanodegree program makes it easy to learn the in-demand skills employers are looking for. You’ll learn foundational AI programming tools (Python, NumPy, PyTorch) and the essential math skills (linear algebra and calculus) that will enable you to start building your own AI applications in just three months.
Whether you’re seeking a full-time role in an AI-related field, want to start applying AI solutions in your current role, or simply want to start learning the defining technology of our time, this is the perfect place to get started.
While this is an introductory course that is not designed to prepare you for a specific job, after completing this program, you should be proficient in the skills used in the AI Industry, including but not limited to Python, machine learning, etc. If you wish to prepare for a full-time AI-related career, we recommend enrolling in our Machine Learning Engineer Nanodegree program next.
Learning to program with Python, one of the most widely used languages in Artificial Intelligence, is the core of this program. You’ll also focus on neural networks—AI’s main building blocks. By learning foundational AI and math skills, you lay the groundwork for advancing your career—whether you’re just starting out, or readying for a full-time role.
Formal prerequisites include basic knowledge of algebra and basic programming in any language. You will also need to be able to communicate fluently and professionally in written and spoken English.
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
The AI Programming with Python Nanodegree program is comprised of content and curriculum to support two (2) projects. We estimate that students can complete the program in three (3) months working 10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Please see the Udacity Executive Program FAQs for policies on enrollment in our programs.
We’ll teach you how to install all the software required. Virtually any 64-bit operating system with at least 8GB of RAM will be suitable. Udacity does not provide any hardware.