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2 Students
41 Lessons
Beginner

What Will You Learn?

  • Get a solid understanding of decision tree
  • Tune a machine learning model's hyperparameters and evaluate its performance.
  • Understand the business scenarios where decision tree is applicable
  • Use decision trees to make predictions
  • Learn the advantage and disadvantages of the different algorithms

Curriculum

5h 3m
Section 1: Introduction
03:09
Section 3: Setting up Python and Python Crash Course
1:38:02
Introduction to Jupyter
13:27
Arithmetic operators in Python Python Basics
04:28
Strings in Python Python Basics
19:07
Lists Tuples and Directories Python Basics
18:41
Working with Numpy Library of Python
11:55
Working with Pandas Library of Python
09:15
Working with Seaborn Library of Python
08:58
Section 4: Simple Decision trees
1:18:57
Basics of decision trees
10:10
Understanding a Regression Tree
10:18
The stopping criteria for controlling tree growth
03:15
The Data set for the Course
02:59
Importing Data in Python
05:40
Missing value treatment in Python
03:39
Dummy Variable creation in Python
04:58
Dependent- Independent Data split in Python
04:03
Test-Train split in Python
06:04
Creating Decision tree in Python
03:47
Evaluating model performance in Python
04:10
Plotting decision tree in Python
04:59
Pruning a tree
04:17
Pruning a tree in Python
10:38
Section 5: Simple Classification Tree
30:57
Classification tree
06:06
The Data set for Classification problem
01:38
Classification tree in Python Preprocessing
08:25
Classification tree in Python Training
13:14
Advantages and Disadvantages of Decision Trees
01:34
Section 6: Ensemble technique 1 - Bagging
17:44
Ensemble technique 1 - Bagging
06:39
Ensemble technique 1 - Bagging in Python
11:05
Section 7: Ensemble technique 2 - Random Forests
22:17
Ensemble technique 2 - Random Forests
03:56
Ensemble technique 2 - Random Forests in Python
06:07
Using Grid Search in Python
12:14
Section 8: Ensemble technique 3 - Boosting
27:28
Boosting
07:11
Ensemble technique 3a - Boosting in Python
05:09
Ensemble technique 3b - AdaBoost in Python
04:01
Ensemble technique 3c - XGBoost in Python
11:07

Description

Requirements

  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

About the Instructor

Reviews 3
Students 152
Courses 5

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics, and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

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