[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning

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Transform the variables in your data and build better performing machine learning models

Created by : Soledad Galli
Last updated : 10/2019
Language : English
Subs + Caption (CC) : Included
Course Source : https://www.udemy.com/course/feature-engineering-for-machine-learning/

What you'll learn

• Learn multiple techniques for missing data imputation
• Transform categorical variables into numbers while capturing meaningful information
• Learn how to deal with infrequent, rare and unseen categories
• Transform skewed variables into Gaussian
• Convert numerical variables into discrete
• Remove outliers from your variables
• Extract meaningful features from dates and time variables
• Learn techniques used in organisations worldwide and in data competitions
• Increase your repertoire of techniques to preprocess data and build more powerful machine learning models

Course content
all 120 lectures 09:25:37

Requirements

• A Python installation
• Jupyter notebook installation
• Python coding skills
• Some experience with Numpy and Pandas
• Familiarity with Machine Learning algorithms
• Familiarity with Scikit-Learn

Description

NEW! Updated in November 2019 for the latest software versions, including use of new tools and open-source packages, and additional feature engineering techniques.

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Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models.

Who is this course for?

So, you’ve made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you’re probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up. And to make things more complicated, you can’t find many consolidated resources about feature engineering. Maybe even just blogs? So you may start to wonder: how are things really done in tech companies?

This course will help you! This is the most comprehensive online course in variable engineering. You will learn a huge variety of engineering techniques used worldwide in different organizations and in data science competitions, to clean and transform your data and variables.

What will you learn?

I have put together a fantastic collection of feature engineering techniques, based on scientific articles, white papers, data science competitions, and of course my own experience as a data scientist.

Specifically, you will learn:

How to impute your missing data

How to encode your categorical variables

How to transform your numerical variables so they meet ML model assumptions

How to convert your numerical variables into discrete intervals

How to remove outliers

How to handle date and time variables

How to work with different time zones

How to handle mixed variables which contain strings and numbers

Throughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an elegant, efficient, and professional manner, using Python, NumPy, Scikit-learn, pandas and a special open-source package that I created especially for this course: Feature- engine.

At the end of the course, you will be able to implement all your feature engineering steps in a single and elegant pipeline, which will allow you to put your predictive models into production with maximum efficiency.

Want to know more? Read on...

In this course, you will initially become acquainted with the most widely used techniques for variable engineering, followed by more advanced and tailored techniques, which capture information while encoding or transforming your variables. You will also find detailed explanations of the various techniques, their advantages, limitations and underlying assumptions and the best programming practices to implement them in Python.

This comprehensive feature engineering course includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

REMEMBER, the course comes with a 30-day money back guarantee, so you can sign up today with no risk. So what are you waiting for? Enrol today, embrace the power of feature engineering and build better machine learning models.

Who this course is for :

• Data Scientists who want to get started in pre-processing datasets to build machine learning models
• Data Scientists who want to learn more techniques for feature engineering for machine learning
• Data Scientist who want to limprove their coding skills and best programming practices for feature engineering
• Software engineers, mathematicians and academics switching careers into data science
• Data Scientists who want to try different feature engineering techniques on data competitions
• Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering.

File list

[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
0. Websites you may like/How you can help Team-FTU.txt 237 B
1. Introduction/1. Introduction.mp4 32.9 MB
1. Introduction/1. Introduction.srt 6.8 KB
1. Introduction/1. Introduction.vtt 6.1 KB
1. Introduction/2. Course curriculum overview.mp4 33.4 MB
1. Introduction/2. Course curriculum overview.srt 7.2 KB
1. Introduction/2. Course curriculum overview.vtt 6.5 KB
1. Introduction/3. Course requirements.mp4 10.6 MB
1. Introduction/3. Course requirements.srt 4.1 KB
1. Introduction/3. Course requirements.vtt 3.7 KB
1. Introduction/4. How to approach this course.html 1.8 KB
1. Introduction/5. Setting up your computer.html 3.5 KB
1. Introduction/6. Download Jupyter notebooks.html 1.3 KB
1. Introduction/6.1 HandsOnPythonCode.zip.zip 9.2 MB
1. Introduction/7. Download datasets.html 2 KB
1. Introduction/8. Download course presentations.html 764 B
1. Introduction/8.1 FeatureEngineeringSlides.zip.zip 29.6 MB
1. Introduction/9. FAQ Data Science, Python programming, datasets, presentations and more....html 1.6 KB
10. Feature Scaling/1. Feature scaling Introduction.mp4 20.6 MB
10. Feature Scaling/1. Feature scaling Introduction.srt 4.6 KB
10. Feature Scaling/1. Feature scaling Introduction.vtt 4.1 KB
10. Feature Scaling/10. Scaling to median and quantiles.mp4 13 MB
10. Feature Scaling/10. Scaling to median and quantiles.srt 3.1 KB
10. Feature Scaling/10. Scaling to median and quantiles.vtt 2.8 KB
10. Feature Scaling/11. Robust Scaling Demo.mp4 16.6 MB
10. Feature Scaling/11. Robust Scaling Demo.srt 2.4 KB
10. Feature Scaling/11. Robust Scaling Demo.vtt 2.2 KB
10. Feature Scaling/12. Scaling to vector unit length.mp4 31.9 MB
10. Feature Scaling/12. Scaling to vector unit length.srt 6.6 KB
10. Feature Scaling/12. Scaling to vector unit length.vtt 5.9 KB
10. Feature Scaling/13. Scaling to vector unit length Demo.mp4 46.3 MB
10. Feature Scaling/13. Scaling to vector unit length Demo.srt 6 KB
10. Feature Scaling/13. Scaling to vector unit length Demo.vtt 5.4 KB
10. Feature Scaling/14. Additional reading resources.html 1.3 KB
10. Feature Scaling/2. Standardisation.mp4 26.5 MB
10. Feature Scaling/2. Standardisation.srt 6.6 KB
10. Feature Scaling/2. Standardisation.vtt 5.9 KB
10. Feature Scaling/3. Standardisation Demo.mp4 41.6 MB
10. Feature Scaling/3. Standardisation Demo.srt 5.5 KB
10. Feature Scaling/3. Standardisation Demo.vtt 4.9 KB
10. Feature Scaling/4. Mean normalisation.mp4 19.8 MB
10. Feature Scaling/4. Mean normalisation.srt 4.9 KB
10. Feature Scaling/4. Mean normalisation.vtt 4.4 KB
10. Feature Scaling/5. Mean normalisation Demo.mp4 45.1 MB
10. Feature Scaling/5. Mean normalisation Demo.srt 6.2 KB
10. Feature Scaling/5. Mean normalisation Demo.vtt 5.5 KB
10. Feature Scaling/6. Scaling to minimum and maximum values.mp4 17.1 MB
10. Feature Scaling/6. Scaling to minimum and maximum values.srt 3.8 KB
10. Feature Scaling/6. Scaling to minimum and maximum values.vtt 3.4 KB
10. Feature Scaling/7. MinMaxScaling Demo.mp4 25.9 MB
10. Feature Scaling/7. MinMaxScaling Demo.srt 3.5 KB
10. Feature Scaling/7. MinMaxScaling Demo.vtt 3.1 KB
10. Feature Scaling/8. Maximum absolute scaling.mp4 14.6 MB
10. Feature Scaling/8. Maximum absolute scaling.srt 3.3 KB
10. Feature Scaling/8. Maximum absolute scaling.vtt 2.9 KB
10. Feature Scaling/9. MaxAbsScaling Demo.mp4 31.5 MB
10. Feature Scaling/9. MaxAbsScaling Demo.srt 4.6 KB
10. Feature Scaling/9. MaxAbsScaling Demo.vtt 4 KB
11. Engineering mixed variables/1. Engineering mixed variables.mp4 15.3 MB
11. Engineering mixed variables/1. Engineering mixed variables.srt 4 KB
11. Engineering mixed variables/1. Engineering mixed variables.vtt 3.6 KB
11. Engineering mixed variables/2. Engineering mixed variables Demo.mp4 45.5 MB
11. Engineering mixed variables/2. Engineering mixed variables Demo.srt 7.2 KB
11. Engineering mixed variables/2. Engineering mixed variables Demo.vtt 6.5 KB
12. Engineering datetime variables/1. Engineering datetime variables.mp4 23.2 MB
12. Engineering datetime variables/1. Engineering datetime variables.srt 5.5 KB
12. Engineering datetime variables/1. Engineering datetime variables.vtt 4.9 KB
12. Engineering datetime variables/2. Engineering dates Demo.mp4 54 MB
12. Engineering datetime variables/2. Engineering dates Demo.srt 9.1 KB
12. Engineering datetime variables/2. Engineering dates Demo.vtt 8 KB
12. Engineering datetime variables/3. Engineering time variables and different timezones.mp4 33.5 MB
12. Engineering datetime variables/3. Engineering time variables and different timezones.srt 5.4 KB
12. Engineering datetime variables/3. Engineering time variables and different timezones.vtt 4.7 KB
13. Assembling a feature engineering pipeline/1. Classification pipeline.mp4 136 MB
13. Assembling a feature engineering pipeline/1. Classification pipeline.srt 15.6 KB
13. Assembling a feature engineering pipeline/1. Classification pipeline.vtt 13.9 KB
13. Assembling a feature engineering pipeline/2. Regression pipeline.mp4 157.6 MB
13. Assembling a feature engineering pipeline/2. Regression pipeline.srt 16.8 KB
13. Assembling a feature engineering pipeline/2. Regression pipeline.vtt 14.8 KB
13. Assembling a feature engineering pipeline/3. Beat the performance by engineering features.html 155 B
14. Final section Next steps/1. BONUS Discounts on my other courses!.html 1 KB
2. Variable Types/1. Variables Intro.mp4 15.3 MB
2. Variable Types/1. Variables Intro.srt 3.5 KB
2. Variable Types/1. Variables Intro.vtt 3.1 KB
2. Variable Types/2. Numerical variables.mp4 26.9 MB
2. Variable Types/2. Numerical variables.srt 6.7 KB
2. Variable Types/2. Numerical variables.vtt 6 KB
2. Variable Types/3. Categorical variables.mp4 18.4 MB
2. Variable Types/3. Categorical variables.srt 4.6 KB
2. Variable Types/3. Categorical variables.vtt 4.1 KB
2. Variable Types/4. Date and time variables.mp4 9.8 MB
2. Variable Types/4. Date and time variables.srt 2.4 KB
2. Variable Types/4. Date and time variables.vtt 2.1 KB
2. Variable Types/5. Mixed variables.mp4 11.3 MB

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