What will i learn?
- Introduction to Python for Data Science
- Pandas, Seaborn and Matplotlibs
- Data Cleaning Preprocessing
- EDA Case Study
- Linear Regression, Logistic Regression, Decision Tress, Ensemble
- Kmeans & Hierarchical
Curriculum for this course
32 Lessons
23:59:54 Hours
Overview
1 Lessons
00:04:25 Hours
- Course Overview 00:04:25
Introduction to Python for Data Science & Numpy
3 Lessons
01:41:59 Hours
- Introduction to Python for Data Science 00:47:24
- Numpy Part1 00:31:05
- Numpy Part2 00:23:30
Pandas
3 Lessons
02:00:57 Hours
- Pandas Part 1 00:37:17
- Pandas Part 2 00:44:05
- Pandas Part 3 00:39:35
Seaborn and Matplotlib
4 Lessons
02:04:08 Hours
- Seaborn 00:38:25
- Matplotlib Part1 00:41:13
- Matplotlib Part2 00:31:49
- Matplotlib Part3 00:12:41
Business Statistics
3 Lessons
01:19:37 Hours
- Business Statistics Part 1 00:28:21
- Business Statistics Part 2 00:32:29
- Business Statistics Part 3 00:18:47
Data Cleaning Preprocessing
3 Lessons
01:38:57 Hours
- Data Cleaning Preprocessing - Part1 00:32:01
- Data Cleaning Preprocessing - Part 2 00:32:28
- Data Cleaning Preprocessing - Part 3 00:34:28
EDA Case Study
5 Lessons
03:07:00 Hours
- Part1 00:29:02
- Part2 00:33:11
- Part3 00:43:41
- Part 4 00:38:08
- Part 5 00:42:58
Linear Regression, Logistic Regression, Decision Tress, Ensemble
6 Lessons
06:26:40 Hours
- Linear Regression 00:40:58
- Logistic Regression 01:59:37
- Decision Trees Part 1 00:24:34
- Decision Trees Part 2 01:31:53
- Ensemble Part1 00:40:11
- Ensemble Part2 01:09:27
Kmeans & Hierarchical
2 Lessons
03:00:16 Hours
- Kmeans 01:47:12
- Hierarchical 01:13:04
Case Study
2 Lessons
02:35:55 Hours
- Part1 01:24:17
- Part 2 01:11:38
Requirements
- Some programming experience
- Admin permissions to download files
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Description
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About the instructor
- 4 Reviews
- 1 Students
- 129 Courses
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