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Business Business Analytics & Intelligence Decision Trees

Decision Trees, Random Forests, AdaBoost & XGBoost in Python

Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python
Rating: 4.0 out of 54.0 (509 ratings)
82,440 students
Created by Start-Tech Academy
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Get a 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.
  • Use decision trees to make predictions
  • Learn the advantage and disadvantages of the different algorithms

Requirements

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

Description

You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right?

You've found the right Decision Trees and tree based advanced techniques course!

After completing this course you will be able to:

  • Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost  of Machine Learning.

  • Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost

  • Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.

  • Confidently practice, discuss and understand Machine Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through Decision tree.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a decision tree based model, which are some of the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:

  • Section 1 - Introduction to Machine Learning

    In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

  • Section 2 - Python basic

    This section gets you started with Python.

    This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Section 3 - Pre-processing and Simple Decision trees

    In this section you will learn what actions you need to take to prepare it for the analysis, these steps are very important for creating a meaningful.

    In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like  missing value imputation, variable transformation and Test-Train split. In the end we will create and plot a simple Regression decision tree.

  • Section 4 - Simple Classification Tree

    This section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python

  • Section 5, 6 and 7 - Ensemble technique
    In this section we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. In this course we will discuss Random Forest, Baggind, Gradient Boosting, AdaBoost and XGBoost.

By the end of this course, your confidence in creating a Decision tree model in Python will soar. You'll have a thorough understanding of how to use Decision tree  modelling to create predictive models and solve business problems.


Go ahead and click the enroll button, and I'll see you in lesson 1!


Cheers

Start-Tech Academy


------------

Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 4 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use Python for data Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.


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

Course content

10 sections • 62 lectures • 7h 13m total length

  • Preview03:08
  • Course Resources
    00:05

  • Preview03:04
  • This is a milestone!
    03:31
  • Opening Jupyter Notebook
    09:06
  • Introduction to Jupyter
    13:26
  • 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
    11:54
  • Working with Pandas Library of Python
    09:15
  • Working with Seaborn Library of Python
    08:57

  • Preview16:03
  • Building a Machine Learning Model
    08:42

  • Preview10:10
  • Understanding a Regression Tree
    10:17
  • 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
    03:38
  • Dummy Variable creation in Python
    04:58
  • Dependent- Independent Data split in Python
    04:02
  • 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
    04:58
  • Pruning a tree
    04:16
  • Pruning a tree in Python
    10:37

  • Classification tree
    06:06
  • The Data set for Classification problem
    01:38
  • Classification tree in Python : Preprocessing
    08:25
  • Classification tree in Python : Training
    13:13
  • Advantages and Disadvantages of Decision Trees
    01:34

  • Ensemble technique 1 - Bagging
    06:39
  • Ensemble technique 1 - Bagging in Python
    11:05

  • Ensemble technique 2 - Random Forests
    03:56
  • Ensemble technique 2 - Random Forests in Python
    06:06
  • Using Grid Search in Python
    12:14

  • Boosting
    07:10
  • Quiz
    1 question
  • Ensemble technique 3a - Boosting in Python
    05:08
  • Ensemble technique 3b - AdaBoost in Python
    04:00
  • Ensemble technique 3c - XGBoost in Python
    11:07
  • Quiz
    1 question
  • Quiz
    1 question

  • Gathering Business Knowledge
    03:26
  • Data Exploration
    03:19
  • The Dataset and the Data Dictionary
    07:31
  • Importing Data in Python
    06:04
  • Univariate analysis and EDD
    03:34
  • EDD in Python
    12:11
  • Outlier Treatment
    04:15
  • Outlier Treatment in Python
    14:18
  • Missing Value Imputation
    03:36
  • Missing Value Imputation in Python
    04:57
  • Seasonality in Data
    03:35
  • Bi-variate analysis and Variable transformation
    16:14
  • Variable transformation and deletion in Python
    09:21
  • Non-usable variables
    04:44
  • Dummy variable creation: Handling qualitative data
    04:50
  • Dummy variable creation in Python
    05:45
  • Correlation Analysis
    10:05
  • Correlation Analysis in Python
    07:07
  • Quiz
    1 question

  • The final milestone!
    01:33
  • Congratulations & About your certificate
    00:40

Instructor

Start-Tech Academy
1,700,000+ Enrollments | 4+ Rated | 160+ Countries
Start-Tech Academy
  • 4.4 Instructor Rating
  • 35,881 Reviews
  • 938,558 Students
  • 38 Courses

Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. 
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. 

Founded by Abhishek Bansal and Pukhraj Parikh.

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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