Section 1 : Introduction and Review
Lecture 1 Introduction and Outline 00:04:02 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 Review of Important Concepts 00:03:21 Duration
Lecture 4 Where to get the Code and Data 00:02:02 Duration

Section 2 : K-Nearest Neighbor
Lecture 1 K-Nearest Neighbor Intuition 00:04:00 Duration
Lecture 2 K-Nearest Neighbor Concepts 00:04:54 Duration
Lecture 3 KNN in Code with MNIST
Lecture 4 When KNN Can Fail 00:03:43 Duration
Lecture 5 KNN for the XOR Problem 00:02:05 Duration
Lecture 6 KNN for the Donut Problem 00:02:36 Duration
Lecture 7 Effect of K 00:05:38 Duration

Section 3 : Naive Bayes and Bayes Classifiers
Lecture 1 Bayes Classifier Intuition (Continuous) 00:18:05 Duration
Lecture 2 Bayes Classifier Intuition (Discrete) 00:10:48 Duration
Lecture 3 Naive Bayes
Lecture 4 Naive Bayes Handwritten Example 00:03:20 Duration
Lecture 5 Naive Bayes in Code with MNIST 00:05:43 Duration
Lecture 6 Non-Naive Bayes 00:03:56 Duration
Lecture 7 Bayes Classifier in Code with MNIST 00:02:03 Duration
Lecture 8 Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
Lecture 9 Generative vs Discriminative Models 00:02:38 Duration

Section 4 : Decision Trees
Lecture 1 Decision Tree Intuition 00:04:36 Duration
Lecture 2 Decision Tree Basics 00:04:50 Duration
Lecture 3 Information Entropy 00:03:50 Duration
Lecture 4 Maximizing Information Gain 00:07:49 Duration
Lecture 5 Choosing the Best Split 00:03:54 Duration
Lecture 6 Decision Tree in Code 00:12:46 Duration

Section 5 : Perceptrons
Lecture 1 Perceptron Concepts 00:06:58 Duration
Lecture 2 Perceptron in Code 00:05:14 Duration
Lecture 3 Perceptron for MNIST and XOR
Lecture 4 Perceptron Loss Function 00:03:52 Duration

Section 6 : Practical Machine Learning
Lecture 1 Hyperparameters and Cross-Validation 00:04:07 Duration
Lecture 2 Feature Extraction and Feature Selection
Lecture 3 Comparison to Deep Learning 00:04:31 Duration
Lecture 4 Multiclass Classification 00:03:11 Duration
Lecture 5 Sci-Kit Learn 00:08:54 Duration
Lecture 6 Regression with Sci-Kit Learn is Easy 00:05:39 Duration

Section 7 : Building a Machine Learning Web Service
Lecture 1 Building a Machine Learning Web Service Concepts 00:04:02 Duration
Lecture 2 Building a Machine Learning Web Service Code 00:06:12 Duration

Section 8 : Conclusion
Lecture 1 What’s Next Support Vector Machines and Ensemble Methods (e 00:02:42 Duration

Section 9 : Appendix
Lecture 1 What is the Appendix 00:02:41 Duration
Lecture 2 About Certification
Lecture 3 Windows-Focused Environment Setup 2018 00:20:13 Duration
Lecture 4 How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
Lecture 5 How to Code by Yourself (part 1) 00:15:48 Duration
Lecture 6 How to Code by Yourself (part 2) 00:09:22 Duration
Lecture 7 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 8 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:21:56 Duration
Lecture 9 Proof that using Jupyter Notebook is the same as not using it 00:12:24 Duration
Lecture 10 Python 2 vs Python 3 00:04:31 Duration