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Coursera is a well known and popular MOOC teaching platform that partners with top universities and organizations to offer online courses.
A typical course at Coursera includes pre recorded video lectures, multi-choice quizzes, auto-graded and peer reviewed assignments, community discussion forum and a shareable electronic course completion certificate.
Are Coursera Artificial Intelligence Courses free?
Not fully.
You can study the course materials for free.
But you have to pay if you want course certification and peer graded assignments.
Some Coursera courses facilitate onetime payment fee that lasts for 180 days.
If you enroll to courses that are part of a specialization, you have to opt for monthly subscription fee to have access to the courses.
Coursera hosts around a good number of courses, specializations, certificate programs and master’s degree in the field of data science.
All these courses are curated and taught by professors from world’s best universities.
Which courses should I take for artificial intelligence in coursera?
If you want me to point one AI course you should take at Coursera, its Andrew NG artificial intelligence course.
Andrew NG is a pioneer in the field of machine learning and artificial intelligence. He is also the co-founder of Coursera.
His Coursera artificial intelligence Stanford course gives insights on core AI principles but he uses MATLAB programming here not Python programming framework.
It is the most popular and well regarded course in AI at Coursera Currently.
There is this advance ML specialization from National research University that has courses that dives deep into modern AI techniques.
University of Washington has a computational neuroscience course that can give you a fair idea on neural networks.
IBM offers an Applied AI with Deep Learning course where it talks about fundamentals of linear Algebra and neural networks and popular deep learning frameworks.
Yonsei University has a deep learning for business course that throws light on ML technology used in business and industries.
If you are from a non-technical background, AI for everyone course throws some introductory light about AI and its surrounding projects.
Practical machine learning course from John Hopkins University gives you a good understanding on the ML algorithms and statistical techniques.
Which is the best online course for artificial intelligence in Coursera?
Below are our best picks of Coursera Artificial Intelligence courses if you are currently looking to learn AI and its related topics.
Machine Learning – Stanford
This is the most popular Coursera artificial intelligence tutorial taught by AI pioneer and Stanford professor Andrew NG.
The course runs for approx 11 weeks with roughly 5 hours of study every week and you do all the programming assignments on Octave/Matlab.
Take this course first and then move on to his deeplearning.ai specialization and it would give you a smooth transition into core AI.
Course Ratings: 4.9+ from 94,275+ students
Key Learning’s from the Course:
- Broad introduction to machine learning, data mining and statistical pattern recognition
- Supervised learning: parametric /non parametric algorithms, SVM, kernels and neural networks
- Unsupervised Learning : Clustering, dimensionality reduction, recommender systems and deep learning
- Machine learning Best practices
- Innovation process in ML and AI
- How to apply algorithms to build smart robots, text understanding, database mining, etc
Who is this course best suited? Anybody who wants to get started with learning machine learning.
Skills Gained from the course: Logistic Regression, Artificial Neural Network, ML algorithms and Machine Learning
Course Reviews:
Structuring Machine Learning Projects – Deeplearning.ai
This is the third course in the deep learning specialization offered by Coursera.
Andrew here talks about the basics of how to assign time /resource in a deep learning task if you are in the R&D of a project.
There are no as such programming assignments in this course but can attempt the quizzes if you had paid for the certificate.
It has 7 hours of lecture content with a suggested studying window of 2 weeks.
It is a complete course in itself to enroll for better understanding ML projects.
Course Ratings: 4.8+ from 24,053+ students
Key Learning’s from the Course:
- How to build a successful machine learning project
- Work on two flight simulators to practice decision making in projects
- Understand how to diagnose errors in a ML system
- How to prioritize directions to reduce error
- Understand complex ML settings
- How to apply end-end learning, transfer learning and multi-task learning.
Who is this course best suited? If you want a guide that can help you with your current ML projects
Skills Gained from the course: Machine Learning, Deep Learning, Inductive Transfer and multi –task learning
Course Reviews:
Neural Networks and Deep Learning – Deeplearning.ai
This is the first course of Andrew NG Deep Learning Specialization offered by Coursera.
The course focuses on the building blocks of deep learning i.e. neural networks.
Course Ratings: 4.9+ from 46,425+ students
Key Learning’s from the Course:
- Understand the major technology trends of deep learning
- How to build, train and apply fully connected deep neural networks
- How to implement vectorized neural networks
- Key parameters in a neural network’s architecture
Who is this course best suited? For a complete beginner who wants to learn deep learning foundations in pure Python
Skills Gained from the course: Artificial Neural Network, Backpropagation, Python Programming and Deep Learning
Course Reviews:
Improving Deep Neural Networks – Deeplearning.ai
This is the second course in the 5 course series of deep learning specialization.
It focuses on different neural networks training methods like regularization, optimization, and parameter tuning.
It also gives a brief introduction to the popular deep learning library called TensorFlow.
Course Ratings: 4.9+ from 29,382+ students
Key Learning’s from the Course:
- Industry best practices for building deep learning applications
- How to effectively use common neural networks
- Implement and apply a variety of optimization algorithms
- How to set up, train, develop, test sets and analyse bias/variance
- How to implement a neural network in TensorFlow
Who is this course best suited? If you want to learn deep neural networks with TensorFlow
Skills Gained from the course: Hyperparameter, TensorFlow, Hyperparameter Optimization and Deep Learning
Course Reviews:
Sequence Models – Deeplearning.ai
This is the last course of the Andrew’s deep learning specialization and talks on how to build applications under natural language processing, music generation, speech recognition, chatbots, etc.
Course Ratings: 4.8+ from 11,698+ students
Key Learning’s from the Course:
- Learn about recurrent neural networks
- Natural Language processing with deep learning
- Augmentation of sequence model algorithm using an attention mechanism
Who is this course best suited? Learn neural networks in relation to text data, especially NLP.
Skills Gained from the course: RNN, ANN, Deep Learning and LSTM
Course Reviews:
Machine Learning Foundations – University of Washington
University of Washington’s Machine learning specialization is good alternative if you want other course options apart from Andrew NG’s AI and ML courses.
It is a 6 week introductory course and acts as a foundation to ML concepts like regression, classification, deep learning, etc with practical case-studies.
Course Ratings: 4.6+ from 8,156+ students
Key Learning’s from the Course:
- Identify potential applications of machine learning in practice.
- Describe the core differences in analyses enabled by regression, classification, and clustering.
- Select the appropriate machine learning task for a potential application.
- Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
- Represent your data as features to serve as input to machine learning models.
- Assess the model quality in terms of relevant error metrics for each task.
- Utilize a dataset to fit a model to analyze new data.
- Build an end-to-end application that uses machine learning at its core.
- Implement these techniques in Python.
Who is this course best suited? For absolute ML beginners.
Skills Gained from the course: Python Programming, ML Concepts, ML and DL
Course Reviews:
Convolutional Neural Networks – Deeplearning.ai
This is the fourth course of Andrew Ng deep learning specialization and teaches how to build convolutional neural networks and apply it to image data.
Course Ratings: 4.8+ from 18,424+ students
Key Learning’s from the Course:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
Who is this course best suited? If you want to get comfortable with CNN’s.
Skills Gained from the course: Facial Recognition System, TensorFlow, CNN and ANN
Course Reviews:
Do you recommend any other Coursera Artificial Intelligence courses worth enrolling into?
Happy Learning!
