Section 1 : Introduction and Review

 		Lecture 1 Introduction and Outline 4:2
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 Review of Important Concepts 3:21
Lecture 3 Where to get the Code and Data 2:2

Section 2 : K-Nearest Neighbor

 		Lecture 5 K-Nearest Neighbor Intuition 4:0
Lecture 6 K-Nearest Neighbor Concepts 4:54
Lecture 7 KNN in Code with MNIST
Lecture 8 When KNN Can Fail 3:43
Lecture 9 KNN for the XOR Problem 2:5
Lecture 10 KNN for the Donut Problem 2:36
Lecture 11 Effect of K 5:38

Section 3 : Naive Bayes and Bayes Classifiers

 		Lecture 12 Bayes Classifier Intuition (Continuous) 18:5
Lecture 13 Bayes Classifier Intuition (Discrete) 10:48
Lecture 14 Naive Bayes
Lecture 15 Naive Bayes Handwritten Example 3:20
Lecture 16 Naive Bayes in Code with MNIST 5:43
Lecture 17 Non-Naive Bayes 3:56
Lecture 18 Bayes Classifier in Code with MNIST 2:3
Lecture 19 Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) 0:0
Lecture 20 Generative vs Discriminative Models 2:38

Section 4 : Decision Trees

 		Lecture 21 Decision Tree Intuition 4:36
Lecture 22 Decision Tree Basics 4:50
Lecture 23 Information Entropy 3:50
Lecture 24 Maximizing Information Gain 7:49
Lecture 25 Choosing the Best Split 3:54
Lecture 26 Decision Tree in Code 12:46

Section 5 : Perceptrons

 		Lecture 27 Perceptron Concepts 6:58
Lecture 28 Perceptron in Code 5:14
Lecture 29 Perceptron for MNIST and XOR
Lecture 30 Perceptron Loss Function 3:52

Section 6 : Practical Machine Learning

 		Lecture 31 Hyperparameters and Cross-Validation 4:7
Lecture 32 Feature Extraction and Feature Selection
Lecture 33 Comparison to Deep Learning 4:31
Lecture 34 Multiclass Classification 3:11
Lecture 35 Sci-Kit Learn 8:54
Lecture 36 Regression with Sci-Kit Learn is Easy 5:39

Section 7 : Building a Machine Learning Web Service

 		Lecture 37 Building a Machine Learning Web Service Concepts 4:2
Lecture 38 Building a Machine Learning Web Service Code 6:12

Section 8 : Conclusion

 		Lecture 39 What’s Next Support Vector Machines and Ensemble Methods (e 2:42

Section 9 : Appendix

 		Lecture 40 What is the Appendix 2:41
Lecture 41 About Certification Pdf
Lecture 42 Windows-Focused Environment Setup 2018 20:13
Lecture 43 How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
Lecture 44 How to Code by Yourself (part 1) 15:48
Lecture 45 How to Code by Yourself (part 2) 9:22
Lecture 46 How to Succeed in this Course (Long Version) 10:17
Lecture 47 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 21:56
Lecture 48 Proof that using Jupyter Notebook is the same as not using it 12:24
Lecture 49 Python 2 vs Python 3 4:31