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
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 techniqueIn 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!
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
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
- 001 Welcome to the Course FREE PREVIEW
- 002 Introduction to Machine Learning
- 003 Building a Machine Learning Model
- Course Resources
- 004 Installing Python and Anaconda
- 005 Opening Jupyter Notebook
- 006 Introduction to Jupyter
- 007 Arithmetic operators in Python Python Basics
- 008 Strings in Python Python Basics
- 009 Lists Tuples and Directories Python Basics
- 010 Working with Numpy Library of Python
- 011 Working with Pandas Library of Python
- 012 Working with Seaborn Library of Python
- 013 Basics of decision trees
- 014 Understanding a Regression Tree
- 015 The stopping criteria for controlling tree growth
- 016 The Data set for the Course
- 017 Importing Data in Python
- 018 Missing value treatment in Python
- 019 Dummy Variable creation in Python
- 020 Dependent- Independent Data split in Python
- 021 Test-Train split in Python
- 022 Creating Decision tree in Python
- 023 Evaluating model performance in Python
- 024 Plotting decision tree in Python
- 025 Pruning a tree
- 026 Pruning a tree in Python
- 027 Classification tree
- 028 The Data set for Classification problem
- 029 Classification tree in Python Preprocessing
- 030 Classification tree in Python Training
- 031 Advantages and Disadvantages of Decision Trees
- 032 Ensemble technique 1 - Bagging
- 033 Ensemble technique 1 - Bagging in Python
- 034 Ensemble technique 2 - Random Forests
- 035 Ensemble technique 2 - Random Forests in Python
- 036 Using Grid Search in Python
- 037 Boosting
- 038 Ensemble technique 3a - Boosting in Python
- 039 Ensemble technique 3b - AdaBoost in Python
- 040 Ensemble technique 3c - XGBoost in Python
A technology-based analytics education company
Founded by Abhishek Bansal and Pukhraj Parikh, 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.
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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