|
Video Name |
Time |
|
1. What You Will Learn in This Section |
02:02 |
|
2. Why Machine Learning is the Future? |
10:30 |
|
3. What is Machine Learning? |
09:31 |
|
4. Understanding various aspects of data - Type, Variables, Category |
07:06 |
|
5. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range |
07:41 |
|
6. Types of Machine Learning Models - Classification, Regression, Clustering etc |
10:02 |
|
Video Name |
Time |
|
1. Data Input-Output - Upload Data |
08:18 |
|
2. Data Input-Output - Convert and Unpack |
08:53 |
|
3. Data Input-Output - Import Data |
05:46 |
|
4. Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns |
11:34 |
|
5. Data Transform - Apply SQL Transformation, Clean Missing Data, Edit Metadata |
18:29 |
|
6. Sample and Split Data - How to Partition or Sample, Train and Test Data |
16:56 |
|
Video Name |
Time |
|
1. Logistic Regression - What is Logistic Regression? |
06:46 |
|
2. Logistic Regression - Build Two-Class Loan Approval Prediction Model |
22:09 |
|
3. Logistic Regression - Understand Parameters and Their Impact |
11:19 |
|
4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score |
13:17 |
|
5. Logistic Regression - Model Selection and Impact Analysis |
05:50 |
|
6. Logistic Regression - Build Multi-Class Wine Quality Prediction Model |
08:13 |
|
7. Decision Tree - What is Decision Tree? |
07:35 |
|
8. Decision Tree - Ensemble Learning - Bagging and Boosting |
07:05 |
|
9. Decision Tree - Parameters - Two Class Boosted Decision Tree |
05:34 |
|
10. Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction |
10:43 |
|
11. Decision Forest - Parameters Explained |
03:37 |
|
12. Two Class Decision Forest - Adult Census Income Prediction |
14:43 |
|
13. Decision Tree - Multi Class Decision Forest IRIS Data |
08:14 |
|
14. SVM - What is Support Vector Machine? |
04:02 |
|
15. SVM - Adult Census Income Prediction |
05:32 |
|
Video Name |
Time |
|
1. What is Linear Regression? |
06:19 |
|
2. Regression Analysis - Common Metrics |
06:27 |
|
3. Linear Regression model using OLS |
10:54 |
|
4. Linear Regression - R Squared |
04:26 |
|
5. Gradient Descent |
10:48 |
|
6. Linear Regression: Online Gradient Descent |
02:12 |
|
7. LR - Experiment Online Gradient |
04:21 |
|
8. Decision Tree - What is Regression Tree? |
06:41 |
|
9. Decision Tree - What is Boosted Decision Tree Regression? |
02:00 |
|
10. Decision Tree - Experiment Boosted Decision Tree |
07:01 |
|
Video Name |
Time |
|
1. Section Introduction |
02:49 |
|
2. How to Summarize Data? |
06:29 |
|
3. Summarize Data - Experiment |
03:12 |
|
4. Outliers Treatment - Clip Values |
06:52 |
|
5. Outliers Treatment - Clip Values Experiment |
07:51 |
|
6. Clean Missing Data with MICE |
07:19 |
|
7. Clean Missing Data with MICE - Experiment |
06:44 |
|
8. SMOTE - Create New Synthetic Observations |
08:33 |
|
9. SMOTE - Experiment |
05:50 |
|
10. Data Normalization - Scale and Reduce |
03:11 |
|
11. Data Normalization - Experiment |
02:32 |
|
12. PCA - What is PCA and Curse of Dimensionality? |
06:24 |
|
13. PCA - Experiment |
03:24 |
|
14. Join Data - Join Multiple Datasets based on common keys |
06:03 |
|
15. Join Data - Experiment |
02:43 |
|
Video Name |
Time |
|
1. Feature Selection - Section Introduction |
05:48 |
|
2. Pearson Correlation Coefficient |
04:36 |
|
3. Chi Square Test of Independence |
05:34 |
|
4. Kendall Correlation Coefficient |
04:11 |
|
5. Spearman's Rank Correlation |
03:42 |
|
6. Comparison Experiment for Correlation Coefficients |
07:40 |
|
7. Filter Based Selection - AzureML Experiment |
03:33 |
|
8. Fisher Based LDA - Intuition |
04:43 |
|
9. Fisher Based LDA - Experiment |
05:46 |