Nulled Oreilly – Feature Engineering Bookcamp, Video Edition 2022-9

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Oreilly – Feature Engineering Bookcamp, Video Edition 2022-9

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Description

Feature Engineering Bookcamp Video Edition course. This course is like an audio book with video. In each section, the speaker reads the book and simultaneously the content, diagrams, codes and text are displayed on the screen. With this course, you can make significant improvements to your machine learning pipelines without spending a lot of time tuning parameters.

What you will learn

• Identify and implement attribute transformations: You will learn how to transform the attributes of your data to improve the performance of your models.
• Building machine learning pipelines with unstructured data: You will work with text and image data and create powerful pipelines.
• Quantifying and Reducing Bias in the Machine Learning Pipeline: Learn how to identify and reduce bias in your data.
• Using feature stores to build a real-time feature engineering pipeline: By using feature stores, you’ll dynamically update your pipeline in real-time.
• Improving an existing pipeline by manipulating input data: Learn how to improve the performance of your models by changing input data.
• Using advanced deep learning models to extract hidden patterns in data: Using deep learning models, you will extract valuable information from your data.

This course is suitable for people who

• They have experience working with machine learning and Python programming language.

Features of the Feature Engineering Bookcamp Video Edition course

• Publisher: Oreilly
• Lecturer: Sinan Ozdemir
• Training level: beginner to advanced
• Training duration: 6 hours and 30 minutes

Course headings

• Chapter 1. Introduction to feature engineering
• Chapter 1. The feature engineering pipeline
• Chapter 1. How this book is organized
• Chapter 1. Summary
• Chapter 2. The basics of feature engineering
• Chapter 2. The four levels of data
• Chapter 2. The types of feature engineering
• Chapter 2. How to evaluate feature engineering efforts
• Chapter 2. Summary
• Chapter 3. Healthcare: Diagnosing COVID-19
• Chapter 3. Exploratory data analysis
• Chapter 3. Feature improvement
• Chapter 3. Feature construction
• Chapter 3. Building our feature engineering pipeline
• Chapter 3. Feature selection
• Chapter 3. Answers to exercises
• Chapter 3. Summary
• Chapter 4. Bias and fairness: Modeling recidivism
• Chapter 4. Exploratory data analysis
• Chapter 4. Measuring bias and fairness
• Chapter 4. Building a baseline model
• Chapter 4. Mitigating bias
• Chapter 4. Building a bias-aware model
• Chapter 4. Answers to exercises
• Chapter 4. Summary
• Chapter 5. Natural language processing: Classifying social media sentiment
• Chapter 5. Text vectorization
• Chapter 5. Feature improvement
• Chapter 5. Feature extraction
• Chapter 5. Feature learning
• Chapter 5. Text vectorization recap
• Chapter 5. Answers to exercises
• Chapter 5. Summary
• Chapter 6. Computer vision: Object recognition
• Chapter 6. Feature construction: Pixels as features
• Chapter 6. Feature extraction: Histogram of oriented gradients
• Chapter 6. Feature learning with VGG-11
• Chapter 6. Image vectorization recap
• Chapter 6. Answers to exercises
• Chapter 6. Summary
• Chapter 7. Time series analysis: Day trading with machine learning
• Chapter 7. Feature construction
• Chapter 7. Feature selection
• Chapter 7. Feature extraction
• Chapter 7. Conclusion
• Chapter 7. Answers to exercises
• Chapter 7. Summary
• Chapter 8. Feature stores
• Chapter 8. Setting up a feature store with Hopsworks
• Chapter 8. Creating training data in Hopsworks
• Chapter 8. Answer to exercise
• Chapter 8. Summary
• Chapter 9. Putting it all together
• Chapter 9. Key takeaways
• Chapter 9. Recap of feature engineering
• Chapter 9. Data type-specific feature engineering techniques
• Chapter 9. Frequently asked questions
• Chapter 9. Other feature engineering techniques
• Chapter 9. Further reading material
• Chapter 9. Summary

Course images

Sample video of the course

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download link

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