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Аннотация
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
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Оглавление
- Preface
- Introduction to Scikit-Learn
- Introduction
- Scikit-Learn
- Advantages of Scikit-Learn
- Disadvantages of Scikit-Learn
- Data Representation
- Tables of Data
- Features and Target Matrices
- Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices
- Activity 1: Selecting a Target Feature and Creating a Target Matrix
- Data Preprocessing
- Messy Data
- Exercise 2: Dealing with Messy Data
- Dealing with Categorical Features
- Exercise 3: Applying Feature Engineering over Text Data
- Rescaling Data
- Exercise 4: Normalizing and Standardizing Data
- Activity 2: Preprocessing an Entire Dataset
- Scikit-Learn API
- How Does It Work?
- Supervised and Unsupervised Learning
- Supervised Learning
- Unsupervised Learning
- Summary
- 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 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset
- Activity 3: Using Data Visualization to Aid the Preprocessing Process
- k-means Algorithm
- Understanding the Algorithm
- Exercise 6: Importing and Training the k-means Algorithm over a Dataset
- Activity 4: Applying the k-means Algorithm to a Dataset
- Mean-Shift Algorithm
- Understanding the Algorithm
- Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset
- Activity 5: Applying the Mean-Shift Algorithm to a Dataset
- DBSCAN Algorithm
- Understanding the Algorithm
- Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset
- Activity 6: Applying the DBSCAN Algorithm to the Dataset
- Evaluating the Performance of Clusters
- Available Metrics in Scikit-Learn
- Exercise 9: Evaluating the Silhouette Coefficient Score and Calinski–Harabasz Index
- Activity 7: Measuring and Comparing the Performance of the Algorithms
- Summary
- Supervised Learning: Key Steps
- Introduction
- Model Validation and Testing
- Data Partition
- Split Ratio
- Exercise 10: Performing Data Partition over a Sample Dataset
- Cross Validation
- Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set
- Activity 8: Data Partition over a Handwritten Digit Dataset
- Evaluation Metrics
- Evaluation Metrics for Classification Tasks
- Exercise 12: Calculating Different Evaluation Metrics over a Classification Task
- Choosing an Evaluation Metric
- Evaluation Metrics for Regression Tasks
- Exercise 13: Calculating Evaluation Metrics over a Regression Task
- Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset
- Error Analysis
- Bias, Variance, and Data Mismatch
- Exercise 14: Calculating the Error Rate over Different Sets of Data
- Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits
- Summary
- Supervised Learning Algorithms: Predict Annual Income
- Introduction
- Exploring the Dataset
- Understanding the Dataset
- Naïve Bayes Algorithm
- How Does It Work?
- Exercise 15: Applying the Naïve Bayes Algorithm
- Activity 11: Training a Naïve Bayes Model for Our Census Income Dataset
- Decision Tree Algorithm
- How Does It Work?
- Exercise 16: Applying the Decision Tree Algorithm
- Activity 12: Training a Decision Tree Model for Our Census Income Dataset
- Support Vector Machine Algorithm
- How Does It Work?
- Exercise 17: Applying the SVM Algorithm
- Activity 13: Training an SVM Model for Our Census Income Dataset
- Error Analysis
- Accuracy, Precision, and Recall
- Summary
- Artificial Neural Networks: Predict Annual Income
- Introduction
- Artificial Neural Networks
- How Do They Work?
- Understanding the Hyperparameters
- Applications
- Limitations
- Applying an Artificial Neural Network
- Scikit-Learn's Multilayer Perceptron
- Exercise 18: Applying the Multilayer Perceptron Classifier Class
- Activity 14: Training a Multilayer Perceptron for Our Census Income Dataset
- Performance Analysis
- Error Analysis
- Hyperparameter Fine-Tuning
- Model Comparison
- Activity 15: Comparing Different Models to Choose the Best Fit for the Census Income Data Problem
- Summary
- Building Your Own Program
- Introduction
- Program Definition
- Building a Program: Key Stages
- Understanding the Dataset
- Activity 16: Performing the Preparation and Creation Stages for the Bank Marketing Dataset
- Saving and Loading a Trained Model
- Saving a Model
- Exercise 19: Saving a Trained Model
- Loading a Model
- Exercise 20: Loading a Saved Model
- Activity 17: Saving and Loading the Final Model for the Bank Marketing Dataset
- Interacting with a Trained Model
- Exercise 21: Creating a Class and a Channel to Interact with a Trained Model
- Activity 18: Allowing Interaction with the Bank Marketing Dataset Model
- Summary
- Appendix
- Index
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