Course: Machine Learning Advanced: Decision Trees in Python

Machine Learning Advanced: Decision Trees in Python

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

The course is created on the basis of three pillars of learning:

  • Know (Study)
  • Do (Practice)
  • Review (Self feedback)


We have created a set of concise and comprehensive videos to teach you all the Excel related skills you will need in your professional career.


With each lecture, we have provide a practice sheet to complement the learning in the lecture video. These sheets are carefully designed to further clarify the concepts and help you with implementing the concepts on practical problems faced on-the-job.


Check if you have learnt the concepts by comparing your solutions provided by us. Ask questions in the discussion board if you face any difficulty.

Who this course is for:

  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience
  • Anyone curious to master Decision Tree technique from Beginner to Advanced in short span of time
Basic knowledge
  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
What you will learn
  • Solid understanding of decision tree
  • Understand the business scenarios where decision tree is applicable
  • Tune a machine learning model's hyperparameters and evaluate its performance
  • Use Pandas DataFrames to manipulate data and make statistical computations
Number of Lectures: 33
Total Duration: 04:33:06
Machine Learning Basics
  • Introduction to Machine Learning  
  • Building a Machine Learning Model  
Setting up Python and Python Crash Course
  • Installing Python and Anaconda  
  • Opening Jupyter Notebook  
  • Introduction to Jupyter  
  • Arithmetic operators in Python: Python Basics  
  • Strings in Python: Python Basics  
  • Lists, Tuples and Directories: Python Basics  
  • Working with Numpy Library of Python  
  • Working with Pandas Library of Python  
  • Working with Seaborn Library of Python  
Simple Decision trees
  • Basics of decision trees  
  • Understanding a Regression Tree  
  • The stopping criteria for controlling tree growth  
  • The Data set for the Course  
  • Importing Data in Python  
  • Missing value treatment in Python  
  • Dummy Variable creation in Python  
  • Dependent- Independent Data split in Python  
  • Test-Train split in Python  
  • Creating Decision tree in Python  
  • Evaluating model performance in Python  
  • Plotting decision tree in Python  
  • Pruning a tree  
  • Pruning a tree in Python  
  • Building a classification tree in Python part 1  
  • Building a classification tree in Python part 2  
Ensemble technique 1 - Bagging
  • Ensemble technique 1 - Bagging in Python  
Ensemble technique 2 - Random Forests
  • Ensemble technique 2 - Random Forests in Python  
  • Using Grid Search in Python  
Ensemble technique 3 - Boosting
  • Ensemble technique 3 - Boosting in Python  
  • AdaBoosting in Python  
  • XGBoosting in Python  
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