Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R
著者
書誌事項
Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R
（Books for professionals by professionals）
Apress, c2018
- : pbk
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注記
Includes index
内容説明・目次
内容説明
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R.
You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers.
You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence.
What You Will Learn
Get an in-depth understanding of all the major machine learning and deep learning algorithms
Fully appreciate the pitfalls to avoid while building models
Implement machine learning algorithms in the cloud
Follow a hands-on approach through case studies for each algorithm
Gain the tricks of ensemble learning to build more accurate models
Discover the basics of programming in R/Python and the Keras framework for deep learning
Who This Book Is For
Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.
目次
Chapter 1: Basic statisticsChapter Goal: Build the statistical foundation for machine learning No of pages : 20Sub -Topics1. Introduction to various statistical functions1. Introduction to distributions2. Hypothesis testing3. Case classes
Chapter 2: Linear regression Chapter Goal: Help the reader master linear regression with the theory & practical conceptsNo of pages: 25Sub - Topics 1. Introduction to regression 2. Least squared error3. Implementing linear regression in Excel & R & Python4. Measuring error
Chapter 3: Logistic regressionChapter Goal: Help the reader master logistic regression with the theory & practical concepts No of pages: 25Sub - Topics: 1. Introduction to logistic regression 2. Cross entropy error3. Implementing logistic regression in Excel & R & Python4. Area under the curve calculation
Chapter 4: Decision treeChapter Goal: Help the reader master decision tree with the theory & practical concepts No of pages: 40Sub - Topics: 1. Introduction to decision tree 2. Information gain3. Decision tree for classification & regression4. Implementing decision tree in Excel & R & Python5. Measuring errorChapter 5: Random forestChapter Goal: Help the reader master random forests with the theory & practical concepts No of pages: 15Sub - Topics: 1. Moving from decision tree to random forests2. Implement random forest in R & Python using decision tree functionalities Chapter 6: GBMChapter Goal: Help the reader master GBM with the theory & practical concepts No of pages: 20Sub - Topics: 1. Understanding gradient boosting process2. Difference between gradient boost & adaboost3. Implement GBM in R & Python using decision tree functionalities Chapter 7: Neural networkChapter Goal: Help the reader master neural network with the theory & practical conceptsNo of pages: 30Sub - Topics: 1. Forward propagation2. Backward propagation3. Impact of epochs and learning rate4. Implement Neural network in Excel, R & Python Chapter 8: Convolutional neural networkChapter Goal: Help the reader master CNN with the theory & practical conceptsNo of pages: 30Sub - Topics: 1. Moving from NN to CNN2. Key parameters within CNN3. Implement CNN in Excel & Python
Chapter 9: RNNChapter Goal: Help the reader master RNN with the theory & practical conceptsNo of pages: 25Sub - Topics: 1. Need for RNN2. Key variations of RNN3. Implementing RNN in Excel & Python Chapter 10: word2vecChapter Goal: Help the reader master word2vec with the theory & practical conceptsNo of pages: 201. Need for word2vec2. Implementing word2vec in Excel & Python
Chapter 11: Unsupervised learning - clusteringChapter Goal: Help the reader master clustering with the theory & practical conceptsNo of pages: 15Sub - Topics: 1. k-Means clustering2. Hierarchical clustering3. Implement clustering in Excel, R & Python
Chapter 12: PCAChapter Goal: Help the reader master PCA with the theory & practical conceptsNo of pages: 15Sub - Topics: 1. Dimensionality reduction using PCA2. Implement PCA in Excel, R & Python
Chapter 13: Recommender systemsChapter Goal: Help the reader master recommender systems with the theory & practical conceptsNo of pages: 25Sub - Topics: 1. user based collaborative filtering2. Item based collaborative filtering3. Matrix factorization4. Implementing the above algorithms in Excel, R & Python
Chapter 14: Implement algorithms in the cloudChapter Goal: Help the reader understand the ways to implement algorithms in the cloudNo of pages: 30Sub - Topics: 1. Implementing machine learning algorithms in AWS2. Implementing machine learning algorithms in Azure3. Implementing machine learning algorithms in GCP
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