Description

Feeding raw data into your model is roughly the same as conceding that you know nothing about the problem. But often, that’s not the case. Feature engineering is the process by which we can take what we know about the domain we’re modeling and transform the data to make it easier for our model to solve, which ultimately has the potential to reduce challenging problems that would take a long time to solve into trivial ones.

 

Radiant Techlearning offers Feature Engineering training program in Classroom & Virtual Instructor Led / online mode.

 

Duration: 5 Days

 

Learning Objective:

  • Machine Learning

Pre-requisites

AudienceProfile:

  • Data Engineers and programmers interested in learning how to apply machine learning in practice.
  • Professionals interested in learning how to leverage machine learning in their enterprise.

Course Content

Introduction

  • Want to know how you can improve the accuracy of your ML models? What about finding which data columns make the most useful features? Feature Engineering is the place for you, where we discuss good and bad features, and how this can be preprocessed and transformed for efficient use in models.

 

Raw Data to Features

  • Feature engineering is often the longest and most difficult phase of building your ML project. In the feature engineering process, professionals start with their raw data and use their own domain knowledge to create features that will make machine learning algorithms work. In this module we will explore what makes a good feature and how to represent them in your ML model.

 

Pre-processing and Feature Creation

  • This module covers pre-processing and feature creations which are data processing techniques that can help you prepare a feature set for a ML system.

 

Feature Crosses

  • In traditional machine learning, feature crosses don’t play a vital role, but in modern day ML methods, feature crosses are an invaluable part of one’s toolkit.In this module, professionals will learn how to recognize the kinds of problems where feature crosses are a powerful way to help machines learn.

 

TF Transform

  • TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. Tf.Transform can be beneficial in preprocessing that requires a full pass the data, such as: bucketizing inputs based on the observed data distribution, normalizing an input value with the help of stdev and mean – converting a vocabulary into integers by analyzing input example. In this module we will explore the use cases for tf.Transform.

 

Summary

  • Here we will summarize the key learning through each module of Feature Engineering: Preprocessing at Scale, Good Feature selection, Utilizing Feature Crosses and Practicing it with TensorFlow.

FAQs

Q: What is feature engineering?

 

A: Engineering in feature deals with the method of transforming raw data into features which can represent better the underlying difficulty to the predictive models, result in improved model accuracy on unseen data.

 

Q: Does deep learning require more feature engineering work?

 

A: Many deep learning neural networks which contain hard-coded data processing, feature extraction, and feature engineering. They may need less of these than other machine learning algorithms, but they still require some.

 

Q: What is the difference between feature selection and feature extraction?

 

A: Feature selection is for filtering irrelevant or redundant features from user dataset. The fundamental difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.

 

Q: What is feature selection in machine learning?

 

A: In Statistics and machine learning, generally feature selection is also known as attributes selection, variable selection or variable subset selection. It is the method of selecting a subset of relevant features (variables, predictors) for use in construction of model.

 

Q: How feature importance is calculated?

 

A: Feature importance is generally calculated as the decrease in node impurity weighted by the probability of reaching at that node. The node probability can be calculated by the number of samples which reach the node, divided by the total number of samples. More important the feature will if the value is higher.

 

Q: What is feature tools?

 

A: Feature tools is a kind of framework to perform automated feature engineering. It generally excels at transforming temporal and relational datasets into feature matrices for machine learning.

 

Q: What is the benefit of doing training from Radiant Techlearning?

 

A: Radiant Techlearning is receptive to new ideas and always believes in a creative approach that makes learning easy and effective. We stand strong with highly qualified & certified technology Consultants, trainers and developers who believe in amalgamation of practical and creative training to groom the technical skills.

Our training programs are practical oriented with 70% – 80% hands on the training technology tool.  Our training program focuses on one-on-one interaction with each participant, latest content in curriculum, real time projects and case studies during the training program.

Our experts will also share best practices & will give you guidance to score high & perform better in your certification exams.

To ensure your success, we provide support session even after the training program.

You would also be awarded with a course completion certificate recognized by the industry after completion of the course & the assignment.

 

Q: Does this training program include any project?

 

A: Yes, Radiant will provide you the most updated, high valued and relevant real time projects and case studies in each training program.

We included projects in each training program from fundamental level to advance level so that you don’t have to face any difficulty in future. You will work on highly exciting projects and that will upgrade your skill, knowledge and industry experience.

 

Q: How the training will be delivered or conducted?

 

A: Radiant Techlearning offers customized training delivery solutions for individuals, teams and businesses depending upon what they require. Here is how we help each one through our diverse formats.

 

Dedicated Classroom Training program

Onsite: To meet the needs & expectations of our corporate clients all over the world, our expert will travel all the way to your location to deliver the training program at a premise of your choice & convenience.

Offsite: Our client and Individual professionals across the world travel all the way to India to attend our classroom training sessions. We assist them in services like accommodation, Airport pick & drop, daily cab & Visa assistance.

Public Batches: Corporates & Individual professionals across the world can nominate their employees or themselves in our classroom or online public batches. Our public batches would have limited number of participants to ensure individual attention. As the participants are from different background and companies, you learn from everyone’s experience.

On-the-Job Learning: Our team of consultant would help you to execute end-to-end project and simultaneously learn the technology.

Q: When the training would be conducted?

 

A: Once we receive your enrollment request, we will share the enrollment details with you to select and complete the enrollment process.

You can always email us on the below email address (whichever applicable) to know the upcoming schedule for a specific technology training program.

Individual:  training@radianttechlearning.com

Corporate: Corporate@radiantechlearning.com

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