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61

Neural Networks with Scikit-Learn, Keras, and H2O
Nokeri, Tshepo Chris ; Nokeri, Tshepo Chris
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn. :75-88

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62

Selecting Algorithms
Testas, Abdelaziz ; Testas, Abdelaziz
Distributed Machine Learning with PySpark : Migrating Effortlessly from Pandas and Scikit-Learn. :25-51

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63

PySpark Basics
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :29-59

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64

Setting Up the PySpark Environment
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :1-27

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65

Survival Analysis withPySpark and Lifelines
Nokeri, Tshepo Chris ; Nokeri, Tshepo Chris
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn. :29-37

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66

Big Data, Machine Learning, and Deep Learning Frameworks
Nokeri, Tshepo Chris ; Nokeri, Tshepo Chris
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn. :7-14

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67

Exploring Machine Learning
Nokeri, Tshepo Chris ; Nokeri, Tshepo Chris
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn. :1-5

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68

Automating the Machine Learning Process with H2O
Nokeri, Tshepo Chris ; Nokeri, Tshepo Chris
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn. :111-116

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69

Unsupervised Learning and Recommendation Algorithms
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :251-298

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70

Variable Selection
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :93-142

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71

Utility Functions and Visualizations
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :61-92

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72

Deploying Machine Learning Models
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :361-388

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73

Machine Learning Flow and Automated Pipelines
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :299-359

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74

Supervised Learning Algorithms
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :143-203

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75

Model Evaluation
Kakarla, Ramcharan ; Krishnan, Sundar ; Alla, Sridhar ; et al.
Applied Data Science Using PySpark : Learn the End-to-End Predictive Model-Building Cycle. :205-249

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76

Next-Generation IoT Intrusion Detection: Leveraging Hybrid Deep Learning Algorithms for Enhanced Security
Urvashi ; Agarwal, Parul ; Raghuvanshi, Kamlesh Kumar ; et al.
2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) Computational Intelligence, Communication Technology and Networking (CICTN), 2025 2nd International Conference on. :56-61 Feb, 2025

Konferenz
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77

Random Forests Using PySpark
Singh, Pramod ; Singh, Pramod
Machine Learning with PySpark : With Natural Language Processing and Recommender Systems. :105-126

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78

Introduction to Machine Learning
Singh, Pramod ; Singh, Pramod
Machine Learning with PySpark : With Natural Language Processing and Recommender Systems. :39-52

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79

IO in PySpark SQL
Mishra, Raju Kumar ; Raman, Sundar Rajan ; Mishra, Raju Kumar ; et al.
PySpark SQL Recipes : With HiveQL, Dataframe and Graphframes. :65-100

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80

Random Forests
Singh, Pramod ; Singh, Pramod
Machine Learning with PySpark : With Natural Language Processing and Recommender Systems. :99-122

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