09-20-2024, 06:26 PM
![[Image: 359020115_tuto.jpg]](https://img87.pixhost.to/images/599/359020115_tuto.jpg)
960.12 MB | 00:27:43 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
Chapter 1 How this book is organized (27.82 MB)
Chapter 1 Introduction to feature engineering (29.85 MB)
Chapter 1 Summary (2.18 MB)
Chapter 1 The feature engineering pipeline (30.22 MB)
Chapter 2 How to evaluate feature engineering efforts (18.45 MB)
Chapter 2 Summary (2.62 MB)
Chapter 2 The basics of feature engineering (14 MB)
Chapter 2 The four levels of data (35.56 MB)
Chapter 2 The types of feature engineering (24.9 MB)
Chapter 3 Answers to exercises (1.1 MB)
Chapter 3 Building our feature engineering pipeline (14.75 MB)
Chapter 3 Exploratory data analysis (8.07 MB)
Chapter 3 Feature construction (30.41 MB)
Chapter 3 Feature improvement (19.65 MB)
Chapter 3 Feature selection (9.69 MB)
Chapter 3 Healthcare Diagnosing COVID-19 (28.32 MB)
Chapter 3 Summary (2.45 MB)
Chapter 4 Answers to exercises (1.18 MB)
Chapter 4 Bias and fairness Modeling recidivism (18.68 MB)
Chapter 4 Building a baseline model (35.31 MB)
Chapter 4 Building a bias-aware model (27.3 MB)
Chapter 4 Exploratory data analysis (12.97 MB)
Chapter 4 Measuring bias and fairness (18.11 MB)
Chapter 4 Mitigating bias (5.49 MB)
Chapter 4 Summary (1.75 MB)
Chapter 5 Answers to exercises (1.4 MB)
Chapter 5 Feature extraction (10.77 MB)
Chapter 5 Feature improvement (16.07 MB)
Chapter 5 Feature learning (38.22 MB)
Chapter 5 Natural language processing Classifying social media sentiment (17.01 MB)
Chapter 5 Summary (1.82 MB)
Chapter 5 Text vectorization (36.49 MB)
Chapter 5 Text vectorization recap (3.19 MB)
Chapter 6 Answers to exercises (999.27 KB)
Chapter 6 Computer vision Object recognition (9.34 MB)
Chapter 6 Feature construction Pixels as features (9.32 MB)
Chapter 6 Feature extraction Histogram of oriented gradients (28.03 MB)
Chapter 6 Feature learning with VGG-11 (36.64 MB)
Chapter 6 Image vectorization recap (2.31 MB)
Chapter 6 Summary (1.12 MB)
Chapter 7 Answers to exercises (2.87 MB)
Chapter 7 Conclusion (3.97 MB)
Chapter 7 Feature construction (64.94 MB)
Chapter 7 Feature extraction (16.98 MB)
Chapter 7 Feature selection (11.31 MB)
Chapter 7 Summary (3.82 MB)
Chapter 7 Time series analysis Day trading with machine learning (21.88 MB)
Chapter 8 Answer to exercise (1.07 MB)
Chapter 8 Creating training data in Hopsworks (19.1 MB)
Chapter 8 Feature stores (41.08 MB)
Chapter 8 Setting up a feature store with Hopsworks (46.92 MB)
Chapter 8 Summary (3.04 MB)
Chapter 9 Data type-specific feature engineering techniques (19.26 MB)
Chapter 9 Frequently asked questions (12.02 MB)
Chapter 9 Further reading material (2.47 MB)
Chapter 9 Key takeaways (10.45 MB)
Chapter 9 Other feature engineering techniques (24.35 MB)
Chapter 9 Putting it all together (4.12 MB)
Chapter 9 Recap of feature engineering (14.01 MB)
Chapter 9 Summary (2.96 MB)
Chapter 1 How this book is organized (27.82 MB)
Chapter 1 Introduction to feature engineering (29.85 MB)
Chapter 1 Summary (2.18 MB)
Chapter 1 The feature engineering pipeline (30.22 MB)
Chapter 2 How to evaluate feature engineering efforts (18.45 MB)
Chapter 2 Summary (2.62 MB)
Chapter 2 The basics of feature engineering (14 MB)
Chapter 2 The four levels of data (35.56 MB)
Chapter 2 The types of feature engineering (24.9 MB)
Chapter 3 Answers to exercises (1.1 MB)
Chapter 3 Building our feature engineering pipeline (14.75 MB)
Chapter 3 Exploratory data analysis (8.07 MB)
Chapter 3 Feature construction (30.41 MB)
Chapter 3 Feature improvement (19.65 MB)
Chapter 3 Feature selection (9.69 MB)
Chapter 3 Healthcare Diagnosing COVID-19 (28.32 MB)
Chapter 3 Summary (2.45 MB)
Chapter 4 Answers to exercises (1.18 MB)
Chapter 4 Bias and fairness Modeling recidivism (18.68 MB)
Chapter 4 Building a baseline model (35.31 MB)
Chapter 4 Building a bias-aware model (27.3 MB)
Chapter 4 Exploratory data analysis (12.97 MB)
Chapter 4 Measuring bias and fairness (18.11 MB)
Chapter 4 Mitigating bias (5.49 MB)
Chapter 4 Summary (1.75 MB)
Chapter 5 Answers to exercises (1.4 MB)
Chapter 5 Feature extraction (10.77 MB)
Chapter 5 Feature improvement (16.07 MB)
Chapter 5 Feature learning (38.22 MB)
Chapter 5 Natural language processing Classifying social media sentiment (17.01 MB)
Chapter 5 Summary (1.82 MB)
Chapter 5 Text vectorization (36.49 MB)
Chapter 5 Text vectorization recap (3.19 MB)
Chapter 6 Answers to exercises (999.27 KB)
Chapter 6 Computer vision Object recognition (9.34 MB)
Chapter 6 Feature construction Pixels as features (9.32 MB)
Chapter 6 Feature extraction Histogram of oriented gradients (28.03 MB)
Chapter 6 Feature learning with VGG-11 (36.64 MB)
Chapter 6 Image vectorization recap (2.31 MB)
Chapter 6 Summary (1.12 MB)
Chapter 7 Answers to exercises (2.87 MB)
Chapter 7 Conclusion (3.97 MB)
Chapter 7 Feature construction (64.94 MB)
Chapter 7 Feature extraction (16.98 MB)
Chapter 7 Feature selection (11.31 MB)
Chapter 7 Summary (3.82 MB)
Chapter 7 Time series analysis Day trading with machine learning (21.88 MB)
Chapter 8 Answer to exercise (1.07 MB)
Chapter 8 Creating training data in Hopsworks (19.1 MB)
Chapter 8 Feature stores (41.08 MB)
Chapter 8 Setting up a feature store with Hopsworks (46.92 MB)
Chapter 8 Summary (3.04 MB)
Chapter 9 Data type-specific feature engineering techniques (19.26 MB)
Chapter 9 Frequently asked questions (12.02 MB)
Chapter 9 Further reading material (2.47 MB)
Chapter 9 Key takeaways (10.45 MB)
Chapter 9 Other feature engineering techniques (24.35 MB)
Chapter 9 Putting it all together (4.12 MB)
Chapter 9 Recap of feature engineering (14.01 MB)
Chapter 9 Summary (2.96 MB)
Screenshot
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