Pro Machine Learning Algorithms:A Hands-On Approach to Implementing Algorithms in Python and R '18
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
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