Starting a new Lecture Notes Series on Machine Learning and Deep Learning - Fundamentals and Applications
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Youtube Lecture Playlist CreditsChannel Name: NPTEL IIT Guwahati
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Machine Learning and Deep Learning - Fundamentals and Applications By Lecture Notes together!
Lecture 2: Lec 1: Introduction to Machine Learning
Lecture 4: Lec 3: Bias-Variance Tradeoff
Lecture 5: Lec 4: Regression
Lecture 6: Lec 5: Bayesian Decision Theory
Lecture 7: Lec 6: Bayesian Decision Theory
Lecture 9: Lec 8: Bayesian Decision Theory
Lecture 10: Lec 9: Bayesian Decision Theory
Lecture 11: Lec 10: Bayesian Belief Networks
Lecture 14: Lec 13: Concept of non-parametric techniques
Lecture 15: Lec 14: Density Estimation by Parzen Window
Lecture 20: Lec 19: Support Vector Machine (Part I)
Lecture 21: Lec 20: Support Vector Machine (Part II)
Lecture 22: Lec 21: Logistic Regression
Lecture 23: Lec 22: Decision Tree
Lecture 24: Lec 23: Hidden Markov Model (HMM)
Lecture 25: Lec 24: Ensemble Classifiers (Part I)
Lecture 26: Lec 25: Ensemble Classifiers (Part II)
Lecture 28: Lec 27: Principal Component Analysis
Lecture 31: Lec 30: Gaussian Mixture Model and EM Algorithm
Lecture 32: Lec 31: K-means clustering.
Lecture 33: Lec 32: Fuzzy K-means clustering
Lecture 40: Lec 39: CNN Architectures - LeNet-5 and AlexNet
Lecture 45: Lec 44: Programming Concepts
Lecture 46: Lec 45: Programming Concepts
Lecture 47: Lec 46: Problem Solving Session
Lecture 48: Lec 47: Problem Solving Session
Lecture 49: Lec 48: Problem Solving Session