Statistics for machine learning build supervised, unsupervised, and reinforcement learning models using both Python and R
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clus...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt Publishing
2017.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630711306719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Journey from Statistics to Machine Learning
- Statistical terminology for model building and validation
- Machine learning
- Major differences between statistical modeling and machine learning
- Steps in machine learning model development and deployment
- Statistical fundamentals and terminology for model building and validation
- Bias versus variance trade-off
- Train and test data
- Machine learning terminology for model building and validation
- Linear regression versus gradient descent
- Machine learning losses
- When to stop tuning machine learning models
- Train, validation, and test data
- Cross-validation
- Grid search
- Machine learning model overview
- Summary
- Chapter 2: Parallelism of Statistics and Machine Learning
- Comparison between regression and machine learning models
- Compensating factors in machine learning models
- Assumptions of linear regression
- Steps applied in linear regression modeling
- Example of simple linear regression from first principles
- Example of simple linear regression using the wine quality data
- Example of multilinear regression - step-by-step methodology of model building
- Backward and forward selection
- Machine learning models - ridge and lasso regression
- Example of ridge regression machine learning
- Example of lasso regression machine learning model
- Regularization parameters in linear regression and ridge/lasso regression
- Summary
- Chapter 3: Logistic Regression Versus Random Forest
- Maximum likelihood estimation
- Logistic regression - introduction and advantages
- Terminology involved in logistic regression
- Applying steps in logistic regression modeling.
- Example of logistic regression using German credit data
- Random forest
- Example of random forest using German credit data
- Grid search on random forest
- Variable importance plot
- Comparison of logistic regression with random forest
- Summary
- Chapter 4: Tree-Based Machine Learning Models
- Introducing decision tree classifiers
- Terminology used in decision trees
- Decision tree working methodology from first principles
- Comparison between logistic regression and decision trees
- Comparison of error components across various styles of models
- Remedial actions to push the model towards the ideal region
- HR attrition data example
- Decision tree classifier
- Tuning class weights in decision tree classifier
- Bagging classifier
- Random forest classifier
- Random forest classifier - grid search
- AdaBoost classifier
- Gradient boosting classifier
- Comparison between AdaBoosting versus gradient boosting
- Extreme gradient boosting - XGBoost classifier
- Ensemble of ensembles - model stacking
- Ensemble of ensembles with different types of classifiers
- Ensemble of ensembles with bootstrap samples using a single type of classifier
- Summary
- Chapter 5: K-Nearest Neighbors and Naive Bayes
- K-nearest neighbors
- KNN voter example
- Curse of dimensionality
- Curse of dimensionality with 1D, 2D, and 3D example
- KNN classifier with breast cancer Wisconsin data example
- Tuning of k-value in KNN classifier
- Naive Bayes
- Probability fundamentals
- Joint probability
- Understanding Bayes theorem with conditional probability
- Naive Bayes classification
- Laplace estimator
- Naive Bayes SMS spam classification example
- Summary
- Chapter 6: Support Vector Machines and Neural Networks
- Support vector machines working principles
- Maximum margin classifier
- Support vector classifier.
- Support vector machines
- Kernel functions
- SVM multilabel classifier with letter recognition data example
- Maximum margin classifier - linear kernel
- Polynomial kernel
- RBF kernel
- Artificial neural networks - ANN
- Activation functions
- Forward propagation and backpropagation
- Optimization of neural networks
- Stochastic gradient descent - SGD
- Momentum
- Nesterov accelerated gradient - NAG
- Adagrad
- Adadelta
- RMSprop
- Adaptive moment estimation - Adam
- Limited-memory broyden-fletcher-goldfarb-shanno - L-BFGS optimization algorithm
- Dropout in neural networks
- ANN classifier applied on handwritten digits using scikit-learn
- Introduction to deep learning
- Solving methodology
- Deep learning software
- Deep neural network classifier applied on handwritten digits using Keras
- Summary
- Chapter 7: Recommendation Engines
- Content-based filtering
- Cosine similarity
- Collaborative filtering
- Advantages of collaborative filtering over content-based filtering
- Matrix factorization using the alternating least squares algorithm for collaborative filtering
- Evaluation of recommendation engine model
- Hyperparameter selection in recommendation engines using grid search
- Recommendation engine application on movie lens data
- User-user similarity matrix
- Movie-movie similarity matrix
- Collaborative filtering using ALS
- Grid search on collaborative filtering
- Summary
- Chapter 8: Unsupervised Learning
- K-means clustering
- K-means working methodology from first principles
- Optimal number of clusters and cluster evaluation
- The elbow method
- K-means clustering with the iris data example
- Principal component analysis - PCA
- PCA working methodology from first principles
- PCA applied on handwritten digits using scikit-learn
- Singular value decomposition - SVD.
- SVD applied on handwritten digits using scikit-learn
- Deep auto encoders
- Model building technique using encoder-decoder architecture
- Deep auto encoders applied on handwritten digits using Keras
- Summary
- Chapter 9: Reinforcement Learning
- Introduction to reinforcement learning
- Comparing supervised, unsupervised, and reinforcement learning in detail
- Characteristics of reinforcement learning
- Reinforcement learning basics
- Category 1 - value based
- Category 2 - policy based
- Category 3 - actor-critic
- Category 4 - model-free
- Category 5 - model-based
- Fundamental categories in sequential decision making
- Markov decision processes and Bellman equations
- Dynamic programming
- Algorithms to compute optimal policy using dynamic programming
- Grid world example using value and policy iteration algorithms with basic Python
- Monte Carlo methods
- Comparison between dynamic programming and Monte Carlo methods
- Key advantages of MC over DP methods
- Monte Carlo prediction
- The suitability of Monte Carlo prediction on grid-world problems
- Modeling Blackjack example of Monte Carlo methods using Python
- Temporal difference learning
- Comparison between Monte Carlo methods and temporal difference learning
- TD prediction
- Driving office example for TD learning
- SARSA on-policy TD control
- Q-learning - off-policy TD control
- Cliff walking example of on-policy and off-policy of TD control
- Applications of reinforcement learning with integration of machine learning and deep learning
- Automotive vehicle control - self-driving cars
- Google DeepMind's AlphaGo
- Robo soccer
- Further reading
- Summary
- Index.
