Decision trees and connecting techniques in Python. How to run Bagging, Random Forest, GBM, Adabust and XGBost in Python
You are looking for the perfect Decision tree course Which teaches you everything you need to build a Decision Tree / Random Forest / XGBoost model in Python, right?
You have found the right Decision Trees and Tree Based Advanced Techniques Course!
After completing this course, Dr. You will be able to:
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Identify business problems that can be solved using XGBoost of Decision Tree / Random Forest / Machine Learning.
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There is a clear understanding of advanced decision tree based algorithms such as Random Forest, Bagging, Adaboost and XGBoost
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Create tree-based (Decision Tree, Random Forest, Bagging, Adabust and XGBost) models in Python and analyze its results.
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Practice, discuss and understand machine learning concepts with confidence
How will this course help you?
A Verification certificate of completion This machine is introduced to all students doing advanced learning courses.
If you are a business manager or executive, or a student who wants to learn and apply machine learning in the real world problems of the business, this course will teach you some advanced techniques of machine learning and provide a solid foundation for it. There are Adaboost and XGBoost.
Why should you choose this course?
This course covers all the steps that need to be taken to solve 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 the analysis is more important is that you have the right data before running the analysis and do some pre-processing on it. And after running the analysis, you should be able to determine how good your model is and explain the results to actually help your business.
What qualifies us to teach you?
This course is taught by Abhishek and Pukhraj. As a manager at a global analytics consulting firm, we have helped businesses solve their business problems using machine learning techniques, and we have used our experience to incorporate practical aspects of data analysis into this course.
With over 150,000 enrollments and thousands of 5-star reviews like this – we’re also the creators of some of the most popular online courses:
This is very good, I like that all the explanations given can be understood by the common man – Joshua
Thanks to the author for this wonderful course. You are the best and this course is worth it. – Daisy
Our word
It is our job to teach our students and we are committed to that. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post questions in the course or send us a direct message.
Download practice files, take quizzes and complete assignments
With each lecture, class notes are attached for you to follow. You can also take a quiz to understand your concept. Each section has a practice assignment to put your learning into practice.
What is included in this course?
This course teaches you all the steps of creating decision tree based models to solve professional problems, which are the most popular machine learning models.
The content of this course on linear regression is given below:
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Section 1 – Introduction to Machine Learning
In this section we will learn – what is machine learning. What are the meanings or different terms associated with machine learning? You will see some examples so that you can understand what machine learning is. It also includes the stages of making a machine learning model, not just a linear model but any machine learning model.
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Section 2 – Python Basics
This section starts with Python.
This section will help you set up Python and Jupiter environments on your system and teach you how to perform some basic operations in Python. Let us understand the importance of various libraries like Numpy, Pandas and Seaborn.
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Section 3 – Pre-processing and simple decision plants
In this section you will learn what actions you need to take to prepare for the analysis, these steps are very important for making sense.
In this section, we will start with the basic principles of the decision tree and then we will cover data pre-processing topics such as Missing Value Imputation, Variable Transformation and Test-Train Split. Finally we will create and plot a simple regression decision tree.
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Section 4 – Simple taxonomy tree
In this section we will expand our knowledge about regression decision trees to taxonomy trees, we will also learn how to create taxonomy trees in Python.
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Sections 5, 6 and 7 – Ensemble Techniques
In this section we will begin our discussion of advanced attachment techniques for decision trees. Ensemble 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.
At the end of this course, your confidence to build a decision tree model in Python will increase. You will have a thorough knowledge of how to use Decision Tree Modeling to create predictive models and solve business problems.
Go ahead and click on the Enrollment button and I’ll see you in Chapter 1!
Cheers
Start-Tech Academy
A
Below is a list of popular FAQs for students looking to embark on their machine learning journey-
What is machine learning?
Machine learning is a field of computer science that gives computers the ability to learn without explicitly programming. 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 steps should I follow to be able to build a machine learning model?
You can divide your learning process into 4 parts:
Statistics and Probability – Basic knowledge of statistics and probability concepts is required to implement machine learning techniques. This part is included in the second section of the syllabus.
Understanding Machine Learning – Section 4 helps you to understand the terms and concepts related to machine learning and gives you the steps to follow to build a machine learning model.
Programming Experience – An important part of machine learning is programming. Python and R are clearly leaders in recent days. The third section will help you set up the Python environment and teach you some basic operations. The next part is a video on how to implement each of the concepts taught in the theory lecture in Python
Understanding Linear Regression Modeling – With a good knowledge of linear regression you get a solid understanding of how machine learning works. Although linear regression is the simplest technique of machine learning, it is still the most popular with good predictability. The fifth and sixth sections cover the end-to-end cover of the linear regression topic and provide a relevant practical lecture with each theory lecture where we run each query with you.
Why use Python for data machine learning?
Understanding Python is one of the most valuable skills required for a career in machine learning.
Although this is not always the case, Python is the programming language of choice for data science. Here is a brief history:
In 2016, it surpassed R on Kaggle, the premier platform for data science competitions.
In 2017, it surpassed R in KDNugges ‘annual survey 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 the growing development in the Python ecosystem. And while your journey of learning Python programming has only just begun, it’s a pleasure to learn that there are plenty (and growing) job opportunities.
What is the difference between data mining, machine learning and deep learning?
Simply put, machine learning and data mining use the same algorithms and techniques as data mining, except that the estimates are different. While data mining seeks previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge — and further that information is automatically applied 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 for learning, understanding, and identifying complex patterns. Automatic language translation and medical diagnostics are examples of in-depth learning.
Decision trees and connecting techniques in Python. How to run Bagging, Random Forest, GBM, Adabust and XGBost in Python
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