The the Machine Learning Workshop Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition
With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problem...
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| Main Author | |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Birmingham
Packt Publishing, Limited
2020
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| Edition | 2 |
| Online Access | Get full text |
| ISBN | 1839219068 9781839219061 |
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| Abstract | With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems. |
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| AbstractList | With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems. |
| Author | Saleh, Hyatt |
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| Snippet | With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing... |
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| Subtitle | Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition |
| TableOfContents | How Does the Naïve Bayes Algorithm Work? -- Exercise 4.01: Applying the Naïve Bayes Algorithm -- Activity 4.01: Training a Naïve Bayes Model for Our Census Income Dataset -- The Decision Tree Algorithm -- How Does the Decision Tree Algorithm Work? -- Exercise 4.02: Applying the Decision Tree Algorithm -- Activity 4.02: Training a Decision Tree Model for Our Census Income Dataset -- The Support Vector Machine Algorithm -- How Does the SVM Algorithm Work? -- Exercise 4.03: Applying the SVM Algorithm -- Activity 4.03: Training an SVM Model for Our Census Income Dataset -- Error Analysis -- Accuracy, Precision, and Recall -- Summary -- Chapter 5: Supervised Learning - Key Steps -- Introduction -- Artificial Neural Networks -- How Do ANNs Work? -- Forward Propagation -- Cost Function -- Backpropagation -- Updating the Weights and Biases -- Understanding the Hyperparameters -- Number of Hidden Layers and Units -- Activation Functions -- Regularization -- Batch Size -- Learning Rate -- Number of Iterations -- Applications of Neural Networks -- Limitations of Neural Networks -- Applying an Artificial Neural Network -- Scikit-Learn's Multilayer Perceptron -- Exercise 5.01: Applying the MLP Classifier Class -- Activity 5.01: Training an MLP for Our Census Income Dataset -- Performance Analysis -- Error Analysis -- Hyperparameter Fine-Tuning -- Model Comparison -- Activity 5.02: Comparing Different Models to Choose the Best Fit for the Census Income Data Problem -- Summary -- Chapter 6: Building Your Own Program -- Introduction -- Program Definition -- Building a Program - Key Stages -- Preparation -- Creation -- Interaction -- Understanding the Dataset -- Activity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing Dataset -- Saving and Loading a Trained Model -- Saving a Model -- Exercise 6.01: Saving a Trained Model -- Loading a Model Exercise 6.02: Loading a Saved Model -- Activity 6.02: Saving and Loading the Final Model for the Bank Marketing Dataset -- Interacting with a Trained Model -- Exercise 6.03: Creating a Class and a Channel to Interact with a Trained Model -- Activity 6.03: Allowing Interaction with the Bank Marketing Dataset Model -- Summary -- Appendix -- Index Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Scikit-Learn -- Introduction -- Introduction to Machine Learning -- Applications of ML -- Choosing the Right ML Algorithm -- Scikit-Learn -- Advantages of Scikit-Learn -- Disadvantages of Scikit-Learn -- Other Frameworks -- Data Representation -- Tables of Data -- Features and Target Matrices -- Exercise 1.01: Loading a Sample Dataset and Creating the Features and Target Matrices -- Activity 1.01: Selecting a Target Feature and Creating a Target Matrix -- Data Preprocessing -- Messy Data -- Missing Values -- Outliers -- Exercise 1.02: Dealing with Messy Data -- Dealing with Categorical Features -- Feature Engineering -- Exercise 1.03: Applying Feature Engineering to Text Data -- Rescaling Data -- Exercise 1.04: Normalizing and Standardizing Data -- Activity 1.02: Pre-processing an Entire Dataset -- Scikit-Learn API -- How Does It Work? -- Estimator -- Predictor -- Transformer -- Supervised and Unsupervised Learning -- Supervised Learning -- Unsupervised Learning -- Summary -- Chapter 2: Unsupervised Learning - Real-Life Applications -- Introduction -- Clustering -- Clustering Types -- Applications of Clustering -- Exploring a Dataset - Wholesale Customers Dataset -- Understanding the Dataset -- Data Visualization -- Loading the Dataset Using pandas -- Visualization Tools -- Exercise 2.01: Plotting a Histogram of One Feature from the Circles Dataset -- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process -- k-means Algorithm -- Understanding the Algorithm -- Initialization Methods -- Choosing the Number of Clusters -- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset -- Activity 2.02: Applying the k-means Algorithm to a Dataset -- Mean-Shift Algorithm -- Understanding the Algorithm Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset -- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset -- DBSCAN Algorithm -- Understanding the Algorithm -- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset -- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset -- Evaluating the Performance of Clusters -- Available Metrics in Scikit-Learn -- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index -- Activity 2.05: Measuring and Comparing the Performance of the Algorithms -- Summary -- Chapter 3: Supervised Learning - Key Steps -- Introduction -- Supervised Learning Tasks -- Model Validation and Testing -- Data Partitioning -- Split Ratio -- Exercise 3.01: Performing a Data Partition on a Sample Dataset -- Cross-Validation -- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set -- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset -- Evaluation Metrics -- Evaluation Metrics for Classification Tasks -- Confusion Matrix -- Accuracy -- Precision -- Recall -- Exercise 3.03: Calculating Different Evaluation Metrics on a Classification Task -- Choosing an Evaluation Metric -- Evaluation Metrics for Regression Tasks -- Exercise 3.04: Calculating Evaluation Metrics on a Regression Task -- Activity 3.02: Evaluating the Performance of the Model Trained on a Handwritten Dataset -- Error Analysis -- Bias, Variance, and Data Mismatch -- Exercise 3.05: Calculating the Error Rate on Different Sets of Data -- Activity 3.03: Performing Error Analysis on a Model Trained to Recognize Handwritten Digits -- Summary -- Chapter 4: Supervised Learning Algorithms: Predicting Annual Income -- Introduction -- Exploring the Dataset -- Understanding the Dataset -- The Naïve Bayes Algorithm |
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