Ayyadevara, V Kishore 　著

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 classesChapter 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 errorChapter 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 calculationChapter 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 & PythonChapter 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 & PythonChapter 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 & PythonChapter 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 & PythonChapter 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