1 - Welcome to the Course (34.76 MB)
2 - Overview of Sectorspecific Use Cases (33.91 MB)
3 - How can you get the most out of this course (27.44 MB)
4 - What is RFM analysis (56.43 MB)
6 - RFM Example (133.45 MB)
10 - Explore the dataset (48.29 MB)
11 - Step 1 PyCaret Setup Function (34.79 MB)
12 - Step 2 CreateModel Function (46.55 MB)
14 - Step 3 Assign Model (21.51 MB)
15 - Step 4 Plot Model (52.88 MB)
8 - PyCaret Clustering Module Workflow (11.18 MB)
9 - Install PyCaret then Load the Dataset (29.18 MB)
17 - What is Sentiment Analysis (39.25 MB)
19 - Sentiment Analysis with Textblob Financial News Dataset (201.27 MB)
20 - Vader Sentiment Analysis (59.96 MB)
21 - Text 2 Emotion (36.99 MB)
22 - Overview of Anomaly Detection (15.77 MB)
23 - Types of Anomalies (39.18 MB)
24 - Project 1 Social Media Monitoring Example (211.89 MB)
25 - Intuition behind Topic Modelling (19.68 MB)
26 - How LDA works (30.37 MB)
27 - Topic coherence Evaluating the results of topic modelling (19.39 MB)
28 - Load the dataset (78.63 MB)
29 - Why the Setup Function is Vital (42.78 MB)
30 - Step One Setup Function (71.54 MB)
31 - Step Two Create Function (26.47 MB)
32 - Step Three Assign Function (40.39 MB)
33 - Step Four Plot Model Function (24.23 MB)
34 - Step Five Evaluate Function (45 MB)
35 - Save Model (5.71 MB)
36 - The type of the data influences the interpretability of results (50.66 MB)
37 - What is Association Rule Mining (51.36 MB)
39 - What is Support (21.87 MB)
40 - Part 1 Explore the dataset (71.56 MB)
42 - Part 2 Create the Model and Examine the rules (75.36 MB)
43 - Part 3 Visualize the results of the Association Rule Mining Exercise (64.29 MB)