Material

• Faculty Video lectures:

  • YouTube Video Link , Videos 

• Faculty Presentation:

  • Presentation 

Unit wise material used:

Unit2: https://drive.google.com/drive/folders/19PtOblTVYbICCR8afsbM4rKx0B0yrGiF?usp=drive_link

Unit3: https://drive.google.com/drive/folders/1B-GbVamQevrIXMB9a92KftoVRPOsO39f?usp=drive_link

Unit4: https://drive.google.com/drive/folders/10blDdP3h6fAiyLQ1C3Gj6CvbsJ7uUUcH?usp=drive_link

Unit5: https://drive.google.com/drive/folders/12qEgal--dvRxBx-heqzKlM4RyhYgeY2K?usp=drive_link

Unit6: https://drive.google.com/drive/folders/11UMoACXCS4nCwx_s-xKjmT7s9J6_7BVc?usp=drive_link

Good reference links

• youtube play list

  • https://manas.iitmandi.ac.in/iwadl/ 

  • https://deeplearning.mit.edu/ 

  • https://www.youtube.com/user/krishnaik06/playlists 

• online courses

  • https://www.coursera.org/learn/machine-learning 

• websites

  • https://machinelearningmastery.com/ 

• Machine Learning Specialization (Coursera) : https://www.coursera.org/specializations/machine-learning-introduction

List of books

Text Book:

• TextBook: Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Ed. (O’Reilly).

Reference Book:

• Bishop, Christopher M. Pattern Recognition and Machine Learning. 1st Edition. New York: Springer, 2006.

• Mitchell, Tom M. Machine Learning. 1st Edition. New York: McGraw-Hill, 1997.

• Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer Series in Statistics. New York: Springer, 2009.

• Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. 1st Edition. Cambridge, MA: MIT Press, 2012.

• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. 1st Edition. Cambridge, MA: MIT Press, 2016.