Section 1 : Welcome to the course!
Lecture 1 Applications of Machine Learning 00:02:32 Duration
Lecture 2 Why Machine Learning is the Future 00:06:38 Duration
Lecture 3 Installing R and R Studio (MAC & Windows) 00:05:25 Duration
Lecture 4 Update Recommended Anaconda Version
Lecture 5 Installing Python and Anaconda (MAC & Windows) 00:06:22 Duration
Lecture 6 BONUS Meet your instructors

Section 2 : Part 1 Data Preprocessing
Lecture 1 Welcome to Part 1 - Data Preprocessing
Lecture 2 Get the dataset 00:06:25 Duration
Lecture 3 Importing the Libraries 00:05:20 Duration
Lecture 4 Importing the Dataset 00:11:55 Duration
Lecture 5 For Python learners, summary of Object-oriented pr
Lecture 6 Missing Data 00:15:58 Duration
Lecture 7 Categorical Data
Lecture 8 Splitting the Dataset into the Training set and Te 00:17:37 Duration
Lecture 9 Feature Scaling 00:15:36 Duration
Lecture 10 And here is our Data Preprocessing Template! 00:08:48 Duration

Section 3 : Part 2 Regression
Lecture 1 Welcome to Part 2 - Regression

Section 4 : Simple Linear Regression
Lecture 1 How to get the dataset
Lecture 2 Dataset + Business Problem Description 00:02:56 Duration
Lecture 3 Simple Linear Regression Intuition - Step 1 00:05:46 Duration
Lecture 4 Simple Linear Regression Intuition - Step 2
Lecture 5 Simple Linear Regression in Python - Step 1 00:09:56 Duration
Lecture 6 Simple Linear Regression in Python - Step 2 00:08:20 Duration
Lecture 7 Simple Linear Regression in Python - Step 3 00:06:43 Duration
Lecture 8 Simple Linear Regression in Python - Step 4 00:14:50 Duration
Lecture 9 Simple Linear Regression in R - Step 1 00:04:40 Duration
Lecture 10 Simple Linear Regression in R - Step 2 00:05:59 Duration
Lecture 11 Simple Linear Regression in R - Step 3 00:03:39 Duration
Lecture 12 Simple Linear Regression in R - Step 4 00:05:42 Duration

Section 5 : Multiple Linear Regression
Lecture 1 How to get the dataset 00:03:19 Duration
Lecture 2 Dataset + Business Problem Description 00:03:44 Duration
Lecture 3 Multiple Linear Regression Intuition - Step 1 00:01:03 Duration
Lecture 4 Multiple Linear Regression Intuition - Step 2 00:01:00 Duration
Lecture 5 Multiple Linear Regression Intuition - Step 3 00:07:21 Duration
Lecture 6 Multiple Linear Regression Intuition - Step 4 00:02:11 Duration
Lecture 7 Multiple Linear Regression Intuition - Step 5 00:15:42 Duration
Lecture 8 Multiple Linear Regression in Python - Step 1 00:15:58 Duration
Lecture 9 Multiple Linear Regression in Python - Step 2 00:02:57 Duration
Lecture 10 Multiple Linear Regression in Python - Step 3 00:05:28 Duration
Lecture 11 Multiple Linear Regression in Python - Backward El 00:13:15 Duration
Lecture 12 Multiple Linear Regression in Python - Backward El 00:12:41 Duration
Lecture 13 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 14 Multiple Linear Regression in R - Step 1 00:07:51 Duration
Lecture 15 Multiple Linear Regression in R - Step 2 00:10:26 Duration
Lecture 16 About Certification
Lecture 17 Multiple Linear Regression in R - Backward Elimina 00:17:51 Duration
Lecture 18 Multiple Linear Regression in R - Backward Elimina 00:07:34 Duration

Section 6 : Polynomial Regression
Lecture 1 Polynomial Regression Intuition 00:05:09 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 Polynomial Regression in Python - Step 1 00:11:39 Duration
Lecture 4 Polynomial Regression in Python - Step 2 00:11:45 Duration
Lecture 5 Polynomial Regression in Python - Step 3 00:19:58 Duration
Lecture 6 Polynomial Regression in Python - Step 4 00:05:46 Duration
Lecture 7 Python Regression Template 00:10:59 Duration
Lecture 8 Polynomial Regression in R - Step 1 00:09:13 Duration
Lecture 9 Polynomial Regression in R - Step 2 00:09:59 Duration
Lecture 10 Polynomial Regression in R - Step 3 00:19:55 Duration
Lecture 11 Polynomial Regression in R - Step 4 00:09:36 Duration
Lecture 12 R Regression Template 00:11:59 Duration

Section 7 : Support Vector Regression (SVR)
Lecture 1 How to get the dataset 00:03:19 Duration
Lecture 2 SVR in Python 00:19:57 Duration
Lecture 3 SVR in R 00:11:44 Duration

Section 8 : Decision Tree Regression
Lecture 1 Decision Tree Regression Intuition 00:11:07 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 Decision Tree Regression in Python 00:14:46 Duration
Lecture 4 Decision Tree Regression in R 00:19:54 Duration

Section 9 : Random Forest Regression
Lecture 1 Random Forest Regression Intuition 00:06:44 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 Random Forest Regression in Python 00:16:45 Duration
Lecture 4 Random Forest Regression in R 00:17:43 Duration

Section 10 : Evaluating Regression Models Performance
Lecture 1 R-Squared Intuition 00:05:12 Duration
Lecture 2 Adjusted R-Squared Intuition 00:09:57 Duration
Lecture 3 Evaluating Regression Models Performance - Homewor 00:08:54 Duration
Lecture 4 Interpreting Linear Regression Coefficient 00:09:16 Duration
Lecture 5 Conclusion of Part 2 - Regression

Section 11 : Part 3 Classification
Lecture 1 Welcome to Part 3 - Classification

Section 12 : Logistic Regression
Lecture 1 Logistic Regression Intuition 00:17:07 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 Logistic Regression in Python - Step 1 00:05:48 Duration
Lecture 4 Logistic Regression in Python - Step 2 00:03:24 Duration
Lecture 5 Logistic Regression in Python - Step 3 00:02:35 Duration
Lecture 6 Logistic Regression in Python - Step 4 00:04:34 Duration
Lecture 7 Logistic Regression in Python - Step 5 00:19:40 Duration
Lecture 8 Python Classification Template 00:03:53 Duration
Lecture 9 Logistic Regression in R - Step 1 00:05:59 Duration
Lecture 10 Logistic Regression in R - Step 2 00:02:59 Duration
Lecture 11 Logistic Regression in R - Step 3 00:05:23 Duration
Lecture 12 Logistic Regression in R - Step 4 00:02:48 Duration
Lecture 13 Logistic Regression in R - Step 5 00:19:24 Duration
Lecture 14 R Classification Template 00:04:17 Duration

Section 13 : K-Nearest Neighbors (K-NN)
Lecture 1 K-Nearest Neighbor Intuition 00:04:53 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 K-NN in Python 00:14:10 Duration
Lecture 4 K-NN in R 00:15:47 Duration

Section 14 : Support Vector Machine (SVM)
Lecture 1 SVM Intuition 00:09:49 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 SVM in Python 00:12:24 Duration
Lecture 4 SVM in R 00:12:09 Duration

Section 15 : Kernel SVM
Lecture 1 Kernel SVM Intuition 00:03:18 Duration
Lecture 2 Mapping to a higher dimension 00:07:50 Duration
Lecture 3 The Kernel Trick 00:12:21 Duration
Lecture 4 Types of Kernel Functions 00:03:49 Duration
Lecture 5 How to get the dataset 00:03:20 Duration
Lecture 6 Kernel SVM in Python. 00:17:54 Duration
Lecture 7 Kernel SVM in R 00:16:36 Duration

Section 16 : Naive Bayes
Lecture 1 Bayes Theorem 00:20:27 Duration
Lecture 2 Naive Bayes Intuition 00:14:03 Duration
Lecture 3 Naive Bayes Intuition (Challenge Reveal) 00:06:04 Duration
Lecture 4 Naive Bayes Intuition (Extras) 00:09:44 Duration
Lecture 5 How to get the dataset 00:03:21 Duration
Lecture 6 Naive Bayes in Python 00:09:14 Duration
Lecture 7 Naive Bayes in R 00:14:54 Duration

Section 17 : Decision Tree Classification
Lecture 1 Decision Tree Classification Intuition 00:00:31 Duration
Lecture 2 How to get the dataset 00:03:20 Duration
Lecture 3 Decision Tree Classification in Python 00:12:35 Duration
Lecture 4 Decision Tree Classification in R 00:19:48 Duration

Section 18 : Random Forest Classification
Lecture 1 Random Forest Classification Intuition 00:04:29 Duration
Lecture 2 How to get the dataset 00:03:20 Duration
Lecture 3 Random Forest Classification in Python 00:19:56 Duration
Lecture 4 Random Forest Classification in R 00:19:56 Duration

Section 19 : Evaluating Classification Models Performance
Lecture 1 False Positives & False Negatives 00:08:01 Duration
Lecture 2 Confusion Matrix 00:04:57 Duration
Lecture 3 Accuracy Paradox 00:02:13 Duration
Lecture 4 CAP Curve 00:11:18 Duration
Lecture 5 CAP Curve Analysis 00:02:51 Duration
Lecture 6 Conclusion of Part 3 - Classification

Section 20 : Part 4 Clustering
Lecture 1 Welcome to Part 4 - Clustering

Section 21 : K-Means Clustering
Lecture 1 K-Means Clustering Intuition 00:14:19 Duration
Lecture 2 K-Means Random Initialization Trap 00:07:51 Duration
Lecture 3 K-Means Selecting The Number Of Clusters 00:11:55 Duration
Lecture 4 How to get the dataset 00:03:19 Duration
Lecture 5 K-Means Clustering in Python 00:17:57 Duration
Lecture 6 K-Means Clustering in R 00:11:47 Duration

Section 22 : Hierarchical Clustering
Lecture 1 Hierarchical Clustering Intuition 00:08:49 Duration
Lecture 2 Hierarchical Clustering How Dendrograms Work 00:08:49 Duration
Lecture 3 Hierarchical Clustering Using Dendrograms 00:06:57 Duration
Lecture 4 How to get the dataset 00:03:19 Duration
Lecture 5 HC in Python - Step 1 00:04:58 Duration
Lecture 6 HC in Python - Step 2 00:06:33 Duration
Lecture 7 HC in Python - Step 3 00:05:29 Duration
Lecture 8 HC in Python - Step 4 00:04:29 Duration
Lecture 9 HC in Python - Step 5 00:04:06 Duration
Lecture 10 HC in R - Step 1 00:03:46 Duration
Lecture 11 HC in R - Step 2 00:05:24 Duration
Lecture 12 HC in R - Step 3 00:03:19 Duration
Lecture 13 HC in R - Step 4 00:02:46 Duration
Lecture 14 HC in R - Step 5 00:02:33 Duration
Lecture 15 Conclusion of Part 4 - Clustering

Section 23 : Part 5 Association Rule Learning
Lecture 1 Welcome to Part 5 - Association Rule Learning

Section 24 : Apriori
Lecture 1 Apriori Intuition 00:18:14 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 Apriori in R - Step 1 00:19:53 Duration
Lecture 4 Apriori in R - Step 2 00:14:25 Duration
Lecture 5 Apriori in R - Step 3 00:19:18 Duration
Lecture 6 Apriori in Python - Step 1 00:17:59 Duration
Lecture 7 Apriori in Python - Step 2 00:14:39 Duration
Lecture 8 Apriori in Python - Step 3 00:12:06 Duration

Section 25 : Eclat
Lecture 1 Eclat Intuition 00:06:05 Duration
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 How to get the dataset 00:03:19 Duration
Lecture 4 Eclat in R 00:10:09 Duration

Section 26 : Part 6 Reinforcement Learning
Lecture 1 Welcome to Part 6 - Reinforcement Learning

Section 27 : Upper Confidence Bound (UCB)
Lecture 1 The Multi-Armed Bandit Problem 00:15:02 Duration
Lecture 2 Upper Confidence Bound (UCB) Intuition 00:14:54 Duration
Lecture 3 How to get the dataset 00:03:19 Duration
Lecture 4 Upper Confidence Bound in Python - Step 1 00:14:42 Duration
Lecture 5 Upper Confidence Bound in Python - Step 2 00:18:09 Duration
Lecture 6 Upper Confidence Bound in Python - Step 3 00:18:47 Duration
Lecture 7 Upper Confidence Bound in Python - Step 4 00:03:54 Duration
Lecture 8 Upper Confidence Bound in R - Step 1 00:13:40 Duration
Lecture 9 Upper Confidence Bound in R - Step 2 00:15:59 Duration
Lecture 10 Upper Confidence Bound in R - Step 3 00:17:38 Duration
Lecture 11 Upper Confidence Bound in R - Step 4 00:03:18 Duration

Section 28 : Thompson Sampling
Lecture 1 Thompson Sampling Intuition 00:19:13 Duration
Lecture 2 Algorithm Comparison UCB vs Thompson Sampling 00:08:12 Duration
Lecture 3 How to get the dataset 00:03:19 Duration
Lecture 4 Thompson Sampling in Python - Step 1 00:19:47 Duration
Lecture 5 Thompson Sampling in Python - Step 2 00:03:43 Duration
Lecture 6 Thompson Sampling in R - Step 1 00:19:02 Duration
Lecture 7 Thompson Sampling in R - Step 2 00:03:27 Duration

Section 29 : Part 7 Natural Language Processing
Lecture 1 Welcome to Part 7 - Natural Language Processing
Lecture 2 How to get the dataset 00:03:19 Duration
Lecture 3 Natural Language Processing in Python - Step 1 00:12:43 Duration
Lecture 4 Natural Language Processing in Python - Step 2 00:10:55 Duration
Lecture 5 Natural Language Processing in Python - Step 3 00:01:41 Duration
Lecture 6 Natural Language Processing in Python - Step 4 00:06:12 Duration
Lecture 7 Natural Language Processing in Python - Step 5 00:07:16 Duration
Lecture 8 Natural Language Processing in Python - Step 6 00:03:05 Duration
Lecture 9 Natural Language Processing in Python - Step 7 00:07:24 Duration
Lecture 10 Natural Language Processing in Python - Step 8 00:16:58 Duration
Lecture 11 natural Language Processing in Python - Step 9 00:05:59 Duration
Lecture 12 Natural Language Processing in Python - Step 10 00:09:57 Duration
Lecture 13 Homework Challenge
Lecture 14 Natural Language Processing in R - Step 1 00:16:35 Duration
Lecture 15 Natural Language Processing in R - Step 2 00:08:39 Duration
Lecture 16 Natural Language Processing in R - Step 3 00:06:28 Duration
Lecture 17 Natural Language Processing in R - Step 4 00:02:58 Duration
Lecture 18 Natural Language Processing in R - Step 5 00:02:05 Duration
Lecture 19 Natural Language Processing in R - Step 6 00:05:49 Duration
Lecture 20 Natural Language Processing in R - Step 7 00:03:27 Duration
Lecture 21 Natural Language Processing in R - Step 8 00:05:21 Duration
Lecture 22 Natural Language Processing in R - Step 9 00:12:51 Duration
Lecture 23 Natural Language Processing in R - Step 10 00:17:31 Duration
Lecture 24 Homework Challenge

Section 30 : Part 8 Deep Learning
Lecture 1 Welcome to Part 8 - Deep Learning
Lecture 2 What is Deep Learning 00:12:34 Duration

Section 31 : Artificial Neural Networks
Lecture 1 Plan of attack 00:02:52 Duration
Lecture 2 The Neuron 00:16:25 Duration
Lecture 3 The Activation Function 00:08:29 Duration
Lecture 4 How do Neural Networks work 00:12:48 Duration
Lecture 5 How do Neural Networks learn 00:12:59 Duration
Lecture 6 Gradient Descent 00:09:49 Duration
Lecture 7 Stochastic Gradient Descent 00:05:50 Duration
Lecture 8 Backpropagation 00:02:47 Duration
Lecture 9 How to get the dataset 00:03:19 Duration
Lecture 10 Business Problem Description 00:04:59 Duration
Lecture 11 ANN in Python - Step 1 - Installing Theano, Tensor 00:12:59 Duration
Lecture 12 ANN in Python - Step 2 00:18:17 Duration
Lecture 13 ANN in Python - Step 3 00:03:14 Duration
Lecture 14 ANN in Python - Step 4 00:02:21 Duration
Lecture 15 ANN in Python - Step 5 00:12:20 Duration
Lecture 16 ANN in Python - Step 6 00:02:44 Duration
Lecture 17 ANN in Python - Step 7 00:03:32 Duration
Lecture 18 ANN in Python - Step 8 00:06:56 Duration
Lecture 19 ANN in Python - Step 9 00:06:22 Duration
Lecture 20 ANN in Python - Step 10 00:06:46 Duration
Lecture 21 ANN in R - Step 1 00:17:17 Duration
Lecture 22 ANN in R - Step 2 00:06:31 Duration
Lecture 23 ANN in R - Step 3 00:12:30 Duration
Lecture 24 ANN in R - Step 4 (Last step) 00:14:07 Duration

Section 32 : Convolutional Neural Networks
Lecture 1 Plan of attack 00:03:32 Duration
Lecture 2 What are convolutional neural networks 00:15:52 Duration
Lecture 3 Step 1 - Convolution Operation 00:16:39 Duration
Lecture 4 Step 1(b) - ReLU Layer 00:05:01 Duration
Lecture 5 Step 2 - Pooling 00:14:14 Duration
Lecture 6 Step 3 - Flattening 00:01:53 Duration
Lecture 7 Step 4 - Full Connection 00:19:26 Duration
Lecture 8 Summary 00:04:23 Duration
Lecture 9 Softmax & Cross-Entropy 00:16:24 Duration
Lecture 10 How to get the dataset 00:03:24 Duration
Lecture 11 CNN in Python - Step 1 00:12:46 Duration
Lecture 12 CNN in Python - Step 2 00:03:01 Duration
Lecture 13 CNN in Python - Step 3 00:01:06 Duration
Lecture 14 CNN in Python - Step 4 00:12:53 Duration
Lecture 15 Welcome to Part 4 - Clustering
Lecture 16 CNN in Python - Step 6 00:05:02 Duration
Lecture 17 CNN in Python - Step 7 00:05:57 Duration
Lecture 18 CNN in Python - Step 8 00:02:52 Duration
Lecture 19 CNN in Python - Step 9 00:19:45 Duration
Lecture 20 CNN in Python - Step 10 00:08:28 Duration
Lecture 21 Conclusion

Section 33 : Part 9 Dimensionality Reduction
Lecture 1 Welcome to Part 9 - Dimensionality Reduction

Section 34 : Principal Component Analysis (PCA)
Lecture 1 How to get the dataset 00:03:19 Duration
Lecture 2 PCA in Python - Step 1 00:11:48 Duration
Lecture 3 PCA in Python - Step 2 00:08:05 Duration
Lecture 4 PCA in Python - Step 3 00:09:49 Duration
Lecture 5 PCA in R - Step 1 00:12:10 Duration
Lecture 6 PCA in R - Step 2 00:11:23 Duration
Lecture 7 PCA in R - Step 3 00:13:43 Duration

Section 35 : Linear Discriminant Analysis
Lecture 1 How to get the dataset 00:03:24 Duration
Lecture 2 LDA in Python 00:18:13 Duration
Lecture 3 LDA in R 00:20:02 Duration

Section 36 : Kernel PCA
Lecture 1 How to get the dataset 00:03:23 Duration
Lecture 2 Kernel PCA in Python 00:14:28 Duration
Lecture 3 Kernel PCA in R 00:20:34 Duration

Section 37 : Part 10 Model Selection & Boosting
Lecture 1 Welcome to Part 10 - Model Selection & Boosting

Section 38 : Model Selection
Lecture 1 How to get the dataset 00:03:23 Duration
Lecture 2 k-Fold Cross Validation in Python 00:13:48 Duration
Lecture 3 k-Fold Cross Validation in R 00:19:34 Duration
Lecture 4 Grid Search in Python - Step 1 00:15:11 Duration
Lecture 5 Grid Search in Python - Step 2 00:11:06 Duration
Lecture 6 Grid Search in R 00:14:04 Duration

Section 39 : XGBoost
Lecture 1 How to get the dataset 00:03:21 Duration
Lecture 2 XGBoost in Python - Step 1 00:09:32 Duration
Lecture 3 XGBoost in Python - Step 2 00:12:43 Duration
Lecture 4 XGBoost in R 00:18:14 Duration