Ranking Search Results using Machine Learning

Ranking Search Results using Machine Learning, available at $39.99, has an average rating of 2.8, with 17 lectures, based on 25 reviews, and has 127 subscribers.

You will learn about At the end of my course students will be able to use the machine machine learned models to search and rank large dataset of documents At the end of my course students will be able to configure search engine to use Machine Learning At the end of my course students will be able to configure search engine to collect features This course is ideal for individuals who are Intermediate search developer who are curious to learn about how to apply machine learning to solve real world search ranking problems. It is particularly useful for Intermediate search developer who are curious to learn about how to apply machine learning to solve real world search ranking problems.

Enroll now: Ranking Search Results using Machine Learning

Summary

Title: Ranking Search Results using Machine Learning

Price: $39.99

Average Rating: 2.8

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

At the end of my course students will be able to use the machine machine learned models to search and rank large dataset of documents

At the end of my course students will be able to configure search engine to use Machine Learning

At the end of my course students will be able to configure search engine to collect features

Who Should Attend

Intermediate search developer who are curious to learn about how to apply machine learning to solve real world search ranking problems.

Target Audiences

Intermediate search developer who are curious to learn about how to apply machine learning to solve real world search ranking problems.

Course Description

Learn ranking search results with the machine learning and popular programming language Python and Elastic Search.

Build a strong foundation in Machine Learning with this tutorial for intermediate programmers.

Understanding of Search Ranking

Leverage Machine Learning to rank search results

Use PyCharm and Python for programming

Use LAMBDAMART, LAMBDANET, RANKNET Machine Learning Algorithms for ranking Search results

Use RankLib to train ranking models

Use Learning To Rank Plug to configure and collect features

A Powerful Skill at Your Fingertips  Learning the fundamentals of ranking search results  puts a powerful and very useful tool at your fingertips. Python and Elastic Search are free, easy to learn, has excellent documentation.

Jobs in machine learning area are plentiful, and being able to learn ranking search results with machine learning will give you a strong edge.

Machine Learning is becoming very popular. Alexa, Siri, IBM Deep Blue and Watson are some famous example of Machine Learning application. Ranking search results is vital in information retrieval.  Learning ranking search results with machine learning will help you become a machine learning developer which is in high demand.

Big companies like Google, Bloomberg, Microsoft,  and  Yahoo already using ranking search results with machine learning in information retrieval and social platforms. They claimed that using Machine Learning and ranking search results has boosted productivity of entire company significantly.

Content and Overview  

This course teaches you on how to  rank search results using open source Python and Elastic Search framework.  You will work along with me step by step to build following answers

Introduction to Search Ranking

Introduction to Search Ranking using Machine Learning

Build an application step by step using Learning to Rank plug in, Elastic Search, Python and demo application from Open Source connections

Feature Engineering

Collect Features

Train Models

Evaluate Models

Learn use cases of ranking search results with machine learning

What am I going to get from this course?

Learn ranking search results and Machine Learning programming from professional trainer from your own desk.

Over 10 lectures teaching you ranking search results programming

Suitable for intermediate programmers and ideal for users who learn faster when shown.

Visual training method, offering users increased retention and accelerated learning.

Breaks even the most complex applications down into simplistic steps.

Note: Please note that I am using short documents in this example to illustrate concepts. You can use same code for longer documents as well.

Course Curriculum

Chapter 1: Introduction to Search Ranking

Lecture 1: What is search ranking?

Lecture 2: Ranking demystified

Lecture 3: Why use machine learning for search?

Lecture 4: Use Cases

Lecture 5: About Author

Chapter 2: Setting up stage for using Machine Learning for Search Engine

Lecture 1: Install Elastic Search

Lecture 2: Install LTR Plug in

Lecture 3: Install Python and Pycharm

Lecture 4: Download Source Code, ML library and dataset

Chapter 3: Feature Engineering

Lecture 1: Index documents

Lecture 2: Training Background

Lecture 3: Configure feature store

Lecture 4: Collect Features

Chapter 4: ML Training and Evaluation

Lecture 1: Train Models

Lecture 2: Evaluate Models

Lecture 3: Use model in search

Lecture 4: Closing Remarks

Instructors

Evergreen Technologies
Software Mentor

Rating Distribution

1 stars: 6 votes

2 stars: 4 votes

3 stars: 7 votes

4 stars: 3 votes

5 stars: 5 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!