AI Learning Resources #
A curated list of high-quality online courses to learn Artificial Intelligence, Machine Learning, and Deep Learning from reputable universities and organisations.
Recommended Books & References #
Deep Neural Networks (DNN) #
Deep Learning. MIT Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). (Vol. 1, No. 2).Introduction to Deep Learning. MIT Press.
Eugene, C. (2019).Deep Learning with Python. Simon & Schuster.
Chollet, F. (2021).Deep Learning for Time Series Forecasting. Machine Learning Mastery.
Brownlee, J. (2018).Neural Architecture Search: A Survey.
Journal of Machine Learning Research, 20(55), 1–21.
Elsken, T., Metzen, J. H., & Hutter, F. (2019).
Mathematics (for Machine Learning) #
Linear Algebra. Pearson Education, 2nd Edition.
Hoffman, K., & Kunze, R. (2005).Advanced Engineering Mathematics. Wiley India, 10th Edition.
Kreyszig, E. (2015).
(Earlier editions are also acceptable)
Machine Learning #
Machine Learning. McGraw-Hill, Indian Edition.
Mitchell, Tom M. (1997).Pattern Recognition and Machine Learning. Springer.
Bishop, C. M. (2006).Introduction to Data Mining. Pearson, 2nd Edition.
Tan, P.-N., Steinbach, M., & Kumar, V.A Tutorial on Support Vector Machines for Pattern Recognition.
Kluwer Academic Publishers, Boston, pp. 1–43.
Burges, C. J. C.
Probability & Statistics #
Probability and Statistics for Engineers. PHI Learning, 8th Edition.
Miller & Freund.Statistics for Business and Economics. Cengage Learning.
Anderson, D. R., Sweeney, D. J., & Williams, T. A.
Online Resources #
Machine Learning Specialization #
Stanford University | DeepLearning.AI
A comprehensive introduction to Machine Learning, covering supervised learning, unsupervised learning, and practical applications.
🔗 https://www.coursera.org/specializations/machine-learning-introduction
Mathematics for Machine Learning Specialization #
Imperial College London
Builds the mathematical foundations required for Machine Learning, including linear algebra, calculus, and probability.
🔗 https://www.coursera.org/specializations/mathematics-machine-learning
AI For Everyone #
DeepLearning.AI
A non-technical course explaining what AI is, how it is used, and its impact on society and business.
🔗 https://www.coursera.org/learn/ai-for-everyone
Deep Learning Specialization #
DeepLearning.AI
An in-depth program covering neural networks, deep learning architectures, CNNs, RNNs, and optimisation techniques.
🔗 https://www.coursera.org/specializations/deep-learning
Neural Networks and Deep Learning #
DeepLearning.AI
The first course in the Deep Learning Specialization, focusing on the fundamentals of neural networks.
🔗 https://www.coursera.org/learn/neural-networks-deep-learning
Structuring Machine Learning Projects #
DeepLearning.AI
Learn how to design, evaluate, and improve Machine Learning systems in real-world projects.
🔗 https://www.coursera.org/learn/machine-learning-projects