Credits : Stanford Cheatsheets: Machine Learning, AI, Probability Statistics, Deep Learning - Afshine Amidi and Shervine Amidi published on September 8, 2019
Explore these recommended books to enhance your understanding:
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Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
A comprehensive resource for ML theory and its applications. Perfect for GATE and Interviews -
Learning from Data course book for IIT D undergraduate level ML course
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Machine Learning Book by Tom M. Mitchell (CMU)
A classical book for Machine Learning.
Course to deepen your knowledge:
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"Introduction to Machine Learning IIT Delhi" by Prof Parag Singla((IIT-D) A comprehensive resource for ML theory, excellent coverage from GATE and Interviews perspective.
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"Stanford CS229: Machine Learning Full Course taught by Andrew Ng" by Prof Andrew NG (Stanford)
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); -
Learning from Data: Caltech course Lectures
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. || NTU MOOC : Learning from Data
Review these comprehensive notes to reinforce your grasp:
- Stanford CS229 Lecture Notes sufficient to cover entire syllabus of Machine Learning
Repository contains the pdf notes :download_here - CMU Machine Learning Notes by Huy NguyenPhD Student, Human-Computer Interaction Institute, Carnegie Mellon University \
Read insightful articles to gain additional insights:
Coding Examples : Kevin Murphy : A Probabilistic Perspective Book Coding Examples
Python 3 code to reproduce the figures in the books Probabilistic Machine Learning: An Introduction (aka "book 1") and Probabilistic Machine Learning: Advanced Topics (aka "book 2"). The code uses the standard Python libraries, such as numpy, scipy, matplotlib, sklearn, etc.
Test your knowledge and skills with these practice problems:
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Berkley CS 189 Machine Learning Previous Year Questions and Solutions
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Learning from Data Homework Caltech: MCQ Questions and Solutions
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2017 Final
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2018 Final
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2019 Final
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2022 Final
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2018 MidTerm
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2019 MidTerm
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UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning; MCQ 2022 MidTerm
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Regression problems from IIT Dharwad EE 405: Patter Recognition and Machine Learning
Machine Learning Mind Map
Youtube courses 📺 🖥️ :
- Advanced Introduction to Machine Learning | Winter 2023
Course Syllabus : Motivation and Linear Regression, Binary Classification, Multi-class Classification, Clustering, Anomaly Detection, Data Visualization, Deep Learning Motivation. Deep Learning Mechanics,Deep Learning in NLP, Advanced Topics || Course Website