filmov
tv
Introduction to Feature Engineering in Machine Learning
Показать описание
In this video, we will learn about feature engineering in Machine Learning.
Feature engineering is a critical task that data scientists have to perform prior to training AI/ML models.
Check out top-rated Udemy courses here:
10 days of No Code AI Bootcamp
Modern Artificial Intelligence with Zero Coding
Python & Machine Learning for Financial Analysis
Modern Artificial Intelligence Masterclass: Build 6 Projects
AWS SageMaker Practical for Beginners | Build 6 Projects
Data Science for Business | 6 Real-world Case Studies
AWS Machine Learning Certification Exam | Complete Guide
TensorFlow 2.0 Practical
TensorFlow 2.0 Practical Advanced
Machine Learning Regression Masterclass in Python
Machine Learning Practical Workout | 8 Real-World Projects
Machine Learning Classification Bootcamp in Python
MATLAB/SIMULINK Bible|Go From Zero to Hero!
Python 3 Programming: Beginner to Pro Masterclass
Autonomous Cars: Deep Learning and Computer Vision in Python
Control Systems Made Simple | Beginner's Guide
Artificial Intelligence in Arabicالذكاء الصناعي مبتدئ لمحترف
The Complete MATLAB Computer Programming Bootcamp
As a data scientist, you may need to:
- Highlight important information in the data
- Remove/isolate unnecessary information (e.x.: outliers).
- Add your own expertise and domain knowledge to alter the data.
Feature engineering is an art of introducing new features that weren’t existing before. Data scientists spend 80% of their time performing feature engineering. The remaining 20% is the easy part which includes training the model and performing hyperparameters optimization.
Performing proper feature engineering is crucial to improve AI/ML model performance.
As a data scientist, you need to answer the following questions:
- What are the capabilities of the ML model I have?
- Which features should I select?
- Can I add my domain knowledge to use less features?
- Can I come up with new features from the data I have at hand?
- What should I put in the missing data locations?
It is important to choose features that are most relevant to the problem.
Adding new features that are unnecessary will increase the computational requirements needed to train the model (curse of dimensionality).
There are numerous techniques that could be used to reduce the number of features (compress/encode the data) such as Principal Component Analysis (PCA).
Thanks.
#featureengineering #machinelearning
Feature engineering is a critical task that data scientists have to perform prior to training AI/ML models.
Check out top-rated Udemy courses here:
10 days of No Code AI Bootcamp
Modern Artificial Intelligence with Zero Coding
Python & Machine Learning for Financial Analysis
Modern Artificial Intelligence Masterclass: Build 6 Projects
AWS SageMaker Practical for Beginners | Build 6 Projects
Data Science for Business | 6 Real-world Case Studies
AWS Machine Learning Certification Exam | Complete Guide
TensorFlow 2.0 Practical
TensorFlow 2.0 Practical Advanced
Machine Learning Regression Masterclass in Python
Machine Learning Practical Workout | 8 Real-World Projects
Machine Learning Classification Bootcamp in Python
MATLAB/SIMULINK Bible|Go From Zero to Hero!
Python 3 Programming: Beginner to Pro Masterclass
Autonomous Cars: Deep Learning and Computer Vision in Python
Control Systems Made Simple | Beginner's Guide
Artificial Intelligence in Arabicالذكاء الصناعي مبتدئ لمحترف
The Complete MATLAB Computer Programming Bootcamp
As a data scientist, you may need to:
- Highlight important information in the data
- Remove/isolate unnecessary information (e.x.: outliers).
- Add your own expertise and domain knowledge to alter the data.
Feature engineering is an art of introducing new features that weren’t existing before. Data scientists spend 80% of their time performing feature engineering. The remaining 20% is the easy part which includes training the model and performing hyperparameters optimization.
Performing proper feature engineering is crucial to improve AI/ML model performance.
As a data scientist, you need to answer the following questions:
- What are the capabilities of the ML model I have?
- Which features should I select?
- Can I add my domain knowledge to use less features?
- Can I come up with new features from the data I have at hand?
- What should I put in the missing data locations?
It is important to choose features that are most relevant to the problem.
Adding new features that are unnecessary will increase the computational requirements needed to train the model (curse of dimensionality).
There are numerous techniques that could be used to reduce the number of features (compress/encode the data) such as Principal Component Analysis (PCA).
Thanks.
#featureengineering #machinelearning
0:08:37
Introduction to Feature Engineering in Machine Learning
0:07:38
Intro to Feature Engineering with TensorFlow - Machine Learning Recipes #9
0:05:12
What is feature engineering | Feature Engineering Tutorial Python # 1
0:08:37
Introduction to Feature Engineering in Data Science
0:05:41
What is Feature Engineering?
0:03:33
R Tutorial: Introduction to feature engineering in R
0:22:23
Feature Engineering Secret From A Kaggle Grandmaster
0:47:58
Feature Engineering Techniques For Machine Learning in Python
0:10:52
Introduction to Generative AI | Generative AI and Prompt Engineering
1:11:10
Feature Engineering Full Course - in 1 Hour | Beginner Level
0:14:19
Step By Step Process In EDA And Feature Engineering In Data Science Projects
0:15:32
How to use Feature Engineering for Machine Learning, Equations
0:04:36
Feature Engineering | Applied Machine Learning, Part 1
0:22:39
Feature Selection In Machine Learning | Feature Selection Techniques With Examples | Simplilearn
0:28:26
Introduction to Feature Engineering | Introduction to dplyr Part 4
0:07:45
What is Feature Engineering Explained in Hindi with Examples | Machine Learning
1:17:31
Machine Learning Course - 4. Intro to Feature Engineering with Text
1:26:57
Feature Engineering | Process of Feature Engineering | Great Learning
0:24:52
What is Feature Engineering | Day 23 | 100 Days of Machine Learning
0:53:59
Advanced Feature Engineering Tips and Tricks - Data Science Festival
0:06:24
Featuretools | Machine Learning & Data Science Open-source Spotlight #2
0:02:35
Machine Learning Explained in 100 Seconds
0:09:36
Feature Selection in Machine Learning: Easy Explanation for Data Science Interviews
0:18:41
Feature engineering vs Feature Learning (tips tricks 46 )
Комментарии