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Decision Trees, Random Forests, Bagging & XGBoost: R Studio

Course Description:
Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming


Publisher:
Start-Tech Academy


Detailed Description of the Course:

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 R, 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 R and analyze its result.
  • Confidently practice, discuss and understand Machine Learning concepts

How this course will help you?

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 :

  • 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 – R basic

    This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.

  • 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, Bagging, Gradient Boosting, AdaBoost and XGBoost.

By the end of this course, your confidence in creating a Decision tree model in R 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 3 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  models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

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.


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Digital Character Design Creations

Course Description:
Digital Character Design


Publisher:
MICHAEL Clarke


Detailed Description of the Course:

Tap into an imaginative new career in character design

We’re delighted to be able to offer our most comprehensive character design course. 100% online, our curriculum provides clear and straight – methods for creating your dream characters.

Character design is a fun and flourishing skill and can forge a lifetime career. The ability to create a character and then bring it to life through personality and circumstances is in high demand in an age where strong content is vital for successful advertising and promotion. Aimed at beginners, our unique online course will teach you the principles and technical expertise necessary to tap into this high-income market.

The course

During the three modules of this course, you’ll learn to create original and captivating characters through the process of drawing and design in the digital software called Procreate. Although you can easily translate what is taught over to the traditional method or with any other digital creative tool you have.

What you’ll learn:

During this first module, you’ll learn the basics of character design including:

Module 1 – Drawing

• Lines

• Shapes

• Structure

• Anatomy

• Features

• Animals

Having mastered the basics, this second module will show you how to refine your character using:

Module 2 – Design

• Silhouette

• Reference points

• American style vs Anime style

• Colour

• Embellishment

This module features exercises to help you perfect your design.

Our final module will show you how to make your character leap off the page and will cover:

Module 3 – Portfolio

•Turn arounds

• Expression sheets

• Action sheets

• Character illustrations

• Summaries

Learning outcomes

On completion of our course you will have:

• A clear understanding of the principles of character design

• Acquired the technical skills needed to create captivating characters

• Acquired visual communication skills

• Developed the ability to place your character in different settings and situations

What you’ll need:

An up to date web browser

An up to date version of Java

Strong internet connection

Who is this course for?

Anyone who has a keen interest in character design in 2-D. This is aimed at both beginners and intermediates.

  • Students ready to learn and work hard
  • Students wanting to add a new skill to their digital art tool belt
  • Student looking to learn how to design characters

Requirements

  • Some basic drawing skills
  • At some point, you will need to have access to some art software.
  • A drawing tablet is very helpful at some point you need one.
  • Paper and art pencils

The duration of this course is: 5 hours and 26 minutes


Learn JavaScript – For Beginners

Course Description:
Learn how to Code Web Pages using JavaScript


Publisher:
YouAccel Training


Detailed Description of the Course:

Students learn to add dynamic, interactivity to web pages using JavaScript.

We start with exploring the fundamental concepts, including JavaScript placement and output functions. We then transition to working with arithmetic operators, math functions, arrays, sorting, conditional statements and loops. Students also learn to work with events and functions.

The course includes several hands-on exercises to help students master this essential scripting language.


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Learn jQuery – For Beginners

Course Description:
Learn to create animated, interactive web pages using jQuery Library


Publisher:
YouAccel Training


Detailed Description of the Course:

 

Take your web development skills to new heights by using jQuery to create animated, interactive web page components using this powerful JavaScript Library.

We begin with the fundamental concepts such as embedding jQuery into documents and basic syntax. We then progress into working with Events, Event handlers, animations, fading, sliding, toggling, hiding and much more. Students will also learn to effectively use Callback functions and chaining methods.

Through a series of hands-on exercises students will create draggable layers, accordion menus and fully animated web interfaces. Many other jQuery attributes, filters and classes are also explored.


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