Stanford University through Stanford Online is starting 4 online courses from 13th September 2021. These certificates are a part of the Artificial Intelligence Professional Program by Stanford Online. After completing any 3 of the 4 courses, you will be able to earn the Artificial Intelligence Professional Program Certificate. This program is designed for working professionals who want to dive into AI topics at graduate-level depth. Check out the programs that Stanford is providing through Stanford Online below-
Machine Learning with Graphs
This is a new course that is being started by Stanford Online. This is a 10-week course starting from September 13th, 2021, and lasting till November 21st, 2021. The course will extensively cover Graph Neural Networks which have been gaining a lot of attention these days. Graph Data structures are capable of representing relationships between objects by nodes and edges. This makes them suitable for specific purposes for performing Machine Learning.
The learning material for the course will be available on the first day of the course itself, i.e, 13th September 2021. The course syllabus will be available 10-14 days prior to the start of the course.
After completing the course, you will earn the Certificate of Achievement in Machine Learning with Graphs from the Stanford Center for Professional Development.
About the Course
- Start Date– 13th September 2021
- End Date– 21st November 2021
- Time Duration– 10 weeks
- Time Commitment Expected– 10-14 hours/week
- Number of Assignments– 5
- Instructor– Jure Leskovec, Associate Professor in the Computer Science Department, Stanford University
- Course Fee– $1,595
Prerequisites
The following pre-requisites are required to be a part of the course.
- Python– The course will consist of programming assignments that will be using the Python programming language. For this, it is extremely important to have proficiency in Python. Along with Python, a good grasp of libraries like numpy, matplotlib, etc is also very important. Having knowledge of basic shell commands will also be very helpful.
- Calculus– Having a good knowledge of Gradients and Chain Rule is a must.
- Linear Algebra– Having knowledge of Matrix algebra such as Matrix Multiplication and Matrix Inverse is a must.
- Probability– Having knowledge of expectation, independence, probability distribution functions, and cumulative distribution functions for both, continuous and discrete random variables is a must.
- Machine Learning– Having a basic knowledge of Machine Learning and Deep Learning would be helpful. Although it is not absolutely required for the course.
Check out the course, here.
Natural Language Processing with Deep Learning
This is a re-run of an old course by Stanford Online. This is a 10-week course starting from October 4th, 2021, and lasting till December 12th, 2021. The course will extensively cover Natural Language Processing(NLP), one of the most important, useful, and promising application areas of Artificial Intelligence.
The learning material for the course will be available on the first day of the course itself, i.e, 4th October 2021. The course syllabus will be available 10-14 days prior to the start of the course.
After completing the course, you will earn the Certificate of Achievement in Natural Language Processing with Deep Learning from the Stanford Center for Professional Development.
About the Course
- Start Date– 4th October 2021
- End Date– 12th December 2021
- Time Duration– 10 weeks
- Time Commitment Expected– 10-14 hours/week
- Number of Assignments– 5
- Instructor– Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL)
- Course Fee– $1,595
Prerequisites
The following pre-requisites are required to be a part of the course.
- Python– The course will consist of programming assignments that will be using the Python programming language. For this, it is extremely important to have proficiency in Python. Along with Python, a good grasp of libraries like numpy, matplotlib, etc is also very important. Having knowledge of basic shell commands will also be very helpful.
- Calculus– Having a good knowledge of Gradients and Chain Rule is a must.
- Linear Algebra– Having knowledge of Matrix algebra such as Matrix Multiplication and Matrix Inverse is a must.
- Probability– Having knowledge of expectation, independence, probability distribution functions, and cumulative distribution functions for both, continuous and discrete random variables is a must.
- Machine Learning– Having a basic knowledge of Machine Learning and Deep Learning would be helpful. Although it is not absolutely required for the course.
Check out the course, here.
Artificial Intelligence: Principles and Techniques
This is a re-run of an old course by Stanford Online. This is a 12-week course starting from November 1st, 2021, and lasting till January 23rd, 2022. The course will cover a broad overview of modern artificial intelligence and equip the students with the tools to tackle new AI problems they might encounter in life.
The learning material for the course will be available on the first day of the course itself, i.e, 1st November 2021. The course syllabus will be available 10-14 days prior to the start of the course.
After completing the course, you will earn the Certificate of Achievement in Artificial Intelligence Principles and Techniques from the Stanford Center for Professional Development.
About the Course
- Start Date– 1st November 2021
- End Date– 23rd January 2022
- Time Duration– 10 weeks extended to 12 weeks because of winter break from 20th December 2021 to 31st December 2021.
- Time Commitment Expected– 10-14 hours/week
- Number of Assignments– 6
- Instructor– Percy Liang Associate Professor of Computer Science and Statistics (Courtesy), and Dorsa Sadigh Assistant Professor of Computer Science and Electrical Engineering
- Course Fee– $1,595
Prerequisites
The following pre-requisites are required to be a part of the course.
- Python– The course will consist of programming assignments that will be using the Python programming language. For this, it is extremely important to have proficiency in Python. Along with Python, a good grasp of libraries like numpy, matplotlib, etc is also very important. Having knowledge of basic shell commands will also be very helpful.
- Calculus– Having a good knowledge of Gradients and Chain Rule is a must.
- Linear Algebra– Having knowledge of Matrix algebra such as Matrix Multiplication and Matrix Inverse is a must.
- Probability– Having knowledge of expectation, independence, probability distribution functions, and cumulative distribution functions for both, continuous and discrete random variables is a must.
- Basic Computer Science Theory– Having a basic knowledge of Computer Science Theory such as Tree Search, Graph Search, Greedy Algorithms, and Asymptotic analysis of time complexity for algorithms.
Check out the course, here.
Reinforcement Learning
This is a new course that is being started by Stanford Online. This is a 12-week course starting from November 8th, 2021, and lasting till January 30th, 2022. The course will extensively cover Reinforcement Learning, a field of Machine Learning with wide and far-reaching applications. The course will also cover the concepts of deep reinforcement learning- deep neural networks applied to the field of Reinforcement Learning.
The learning material for the course will be available on the first day of the course itself, i.e, 8th November 2021. The course syllabus will be available 10-14 days prior to the start of the course.
After completing the course, you will earn the Certificate of Achievement in Reinforcement Learning from the Stanford Center for Professional Development.
About the Course
- Start Date– 8th November 2021
- End Date– 30th January 2022
- Time Duration– 10 weeks extended to 12 weeks because of winter break from 20th December 2021 to 31st December 2021.
- Time Commitment Expected– 10-14 hours/week
- Number of Assignments– 5
- Instructor– Emma Brunskill Associate Professor in the Computer Science Department, Stanford University
- Course Fee– $1,595
Prerequisites
The following pre-requisites are required to be a part of the course.
- Python– The course will consist of programming assignments that will be using the Python programming language. For this, it is extremely important to have proficiency in Python. Along with Python, a good grasp of libraries like numpy, matplotlib, etc is also very important. Having knowledge of basic shell commands will also be very helpful.
- Calculus– Having a good knowledge of Gradients and Chain Rule is a must.
- Linear Algebra– Having knowledge of Matrix algebra such as Matrix Multiplication and Matrix Inverse is a must.
- Probability– Having knowledge of expectation, independence, probability distribution functions, and cumulative distribution functions for both, continuous and discrete random variables is a must.
- Machine Learning– Having a basic knowledge of Machine Learning and Deep Learning would be helpful. Although it is not absolutely required for the course.
Check out the course, here.
Check out the Google Landmark Retrieval Challenge here and, the Google Landmark Recognition Challenge here.