TY  - BOOK
AU  - Johnston,Benjamin
AU  - Mathur,Ishita
TI  - Applied Supervised Learning with Python: Use Scikit-Learn to Build Predictive Models from Real-world Datasets and Prepare Yourself for the Future of Machine Learning
SN  - 9781789955835
AV  - Q325.5 .S253 2019 
PY  - 2019///
CY  - Birmingham
PB  - Packt Publishing, Limited
KW  - Electronic books
N1  - Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Python Machine Learning Toolkit -- Introduction -- Supervised Machine Learning -- When to Use Supervised Learning -- Why Python? -- Jupyter Notebooks -- Exercise 1: Launching a Jupyter Notebook -- Exercise 2: Hello World -- Exercise 3: Order of Execution in a Jupyter Notebook -- Exercise 4: Advantages of Jupyter Notebooks -- Python Packages and Modules -- pandas -- Loading Data in pandas -- Exercise 5: Loading and Summarizing the Titanic Dataset -- Exercise 6: Indexing and Selecting Data -- Exercise 7: Advanced Indexing and Selection -- pandas Methods -- Exercise 8: Splitting, Applying, and Combining Data Sources -- Lambda Functions -- Exercise 9: Lambda Functions -- Data Quality Considerations -- Managing Missing Data -- Class Imbalance -- Low Sample Size -- Activity 1: pandas Functions -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization -- Introduction -- Exploratory Data Analysis (EDA) -- Exercise 10: Importing Libraries for Data Exploration -- Summary Statistics and Central Values -- Standard Deviation -- Percentiles -- Exercise 11: Summary Statistics of Our Dataset -- Missing Values -- Finding Missing Values -- Exercise 12: Visualizing Missing Values -- Imputation Strategies for Missing Values -- Exercise 13: Imputation Using pandas -- Exercise 14: Imputation Using scikit-learn -- Exercise 15: Imputation Using Inferred Values -- Activity 2: Summary Statistics and Missing Values -- Distribution of Values -- Target Variable -- Exercise 16: Plotting a Bar Chart -- Categorical Data -- Exercise 17: Datatypes for Categorical Variables -- Exercise 18: Calculating Category Value Counts -- Exercise 19: Plotting a Pie Chart -- Continuous Data -- Exercise 20: Plotting a Histogram -- Exercise 21: Skew and Kurtosis; Activity 3: Visually Representing the Distribution of Values -- Relationships within the Data -- Relationship between Two Continuous Variables -- Exercise 22: Plotting a Scatter Plot -- Exercise 23: Correlation Heatmap -- Exercise 24: Pairplot -- Relationship between a Continuous and a Categorical Variable -- Exercise 25: Bar Chart -- Exercise 26: Box Plot -- Relationship between Two Categorical Variables -- Exercise 27: Stacked Bar Chart -- Activity 4: Relationships Within the Data -- Summary -- Chapter 3: Regression Analysis -- Introduction -- Regression and Classification Problems -- Data, Models, Training, and Evaluation -- Linear Regression -- Exercise 28: Plotting Data with a Moving Average -- Activity 5: Plotting Data with a Moving Average -- Least Squares Method -- The scikit-learn Model API -- Exercise 29: Fitting a Linear Model Using the Least Squares Method -- Activity 6: Linear Regression Using the Least Squares Method -- Linear Regression with Dummy Variables -- Exercise 30: Introducing Dummy Variables -- Activity 7: Dummy Variables -- Parabolic Model with Linear Regression -- Exercise 31: Parabolic Models with Linear Regression -- Activity 8: Other Model Types with Linear Regression -- Generic Model Training -- Gradient Descent -- Exercise 32: Linear Regression with Gradient Descent -- Exercise 33: Optimizing Gradient Descent -- Activity 9: Gradient Descent -- Multiple Linear Regression -- Exercise 34: Multiple Linear Regression -- Autoregression Models -- Exercise 35: Creating an Autoregression Model -- Activity 10: Autoregressors -- Summary -- Chapter 4: Classification -- Introduction -- Linear Regression as a Classifier -- Exercise 36: Linear Regression as a Classifier -- Logistic Regression -- Exercise 37: Logistic Regression as a Classifier - Two-Class Classifier -- Exercise 38: Logistic Regression - Multiclass Classifier; Activity 11: Linear Regression Classifier - Two-Class Classifier -- Activity 12: Iris Classification Using Logistic Regression -- Classification Using K-Nearest Neighbors -- Exercise 39: K-NN Classification -- Exercise 40: Visualizing K-NN Boundaries -- Activity 13: K-NN Multiclass Classifier -- Classification Using Decision Trees -- Exercise 41: ID3 Classification -- Exercise 42: Iris Classification Using a CART Decision Tree -- Summary -- Chapter 5: Ensemble Modeling -- Introduction -- Exercise 43: Importing Modules and Preparing the Dataset -- Overfitting and Underfitting -- Underfitting -- Overfitting -- Overcoming the Problem of Underfitting and Overfitting -- Bagging -- Bootstrapping -- Bootstrap Aggregation -- Exercise 44: Using the Bagging Classifier -- Random Forest -- Exercise 45: Building the Ensemble Model Using Random Forest -- Boosting -- Adaptive Boosting -- Exercise 46: Adaptive Boosting -- Gradient Boosting -- Exercise 47: GradientBoostingClassifier -- Stacking -- Exercise 48: Building a Stacked Model -- Activity 14: Stacking with Standalone and Ensemble Algorithms -- Summary -- Chapter 6: Model Evaluation -- Introduction -- Exercise 49: Importing the Modules and Preparing Our Dataset -- Evaluation Metrics -- Regression -- Exercise 50: Regression Metrics -- Classification -- Exercise 51: Classification Metrics -- Splitting the Dataset -- Hold-out Data -- K-Fold Cross-Validation -- Sampling -- Exercise 52: K-Fold Cross-Validation with Stratified Sampling -- Performance Improvement Tactics -- Variation in Train and Test Error -- Hyperparameter Tuning -- Exercise 53: Hyperparameter Tuning with Random Search -- Feature Importance -- Exercise 54: Feature Importance Using Random Forest -- Activity 15: Final Test Project -- Summary -- Appendix -- Index
N2  - Applied Supervised Learning with Python provides you a rich understanding of machine learning, one of the most pursued topics in information science, and Python, one of the most popular scripting languages. Through this book, you'll learn Jupyter Notebooks, the technology used in academic and commercial circles with in-line code running support
UR  - https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5763194
ER  -