Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R (Paperback)

Chapter 1: Basic statistics Chapter Goal: Build the statistical foundation for machine learning No of pages : 20 Sub -Topics 1. Introduction to various statistical functions 1. Introduction to distributions 2. Hypothesis testing 3. Case classes Chapter 2: Linear regression Chapter Goal: Help the reader master linear regression with the theory & practical concepts No of pages: 25 Sub - Topics 1. Introduction to regression 2. Least squared error 3. Implementing linear regression in Excel & R & Python 4. Measuring error Chapter 3: Logistic regression Chapter Goal: Help the reader master logistic regression with the theory & practical concepts No of pages: 25 Sub - Topics: 1. Introduction to logistic regression 2. Cross entropy error 3. Implementing logistic regression in Excel & R & Python 4. Area under the curve calculation Chapter 4: Decision tree Chapter Goal: Help the reader master decision tree with the theory & practical concepts No of pages: 40 Sub - Topics: 1. Introduction to decision tree 2. Information gain 3. Decision tree for classification & regression 4. Implementing decision tree in Excel & R & Python 5. Measuring error Chapter 5: Random forest Chapter Goal: Help the reader master random forests with the theory & practical concepts No of pages: 15 Sub - Topics: 1. Moving from decision tree to random forests 2. Implement random forest in R & Python using decision tree functionalities Chapter 6: GBM Chapter Goal: Help the reader master GBM with the theory & practical concepts No of pages: 20 Sub - Topics: 1. Understanding gradient boosting process 2. Difference between gradient boost & adaboost 3. Implement GBM in R & Python using decision tree functionalities Chapter 7: Neural network Chapter Goal: Help the reader master neural network with the theory & practical concepts No of pages: 30 Sub - Topics: 1. Forward propagation 2. Backward propagation 3. Impact of epochs and learning rate 4. Implement Neural network in Excel, R & Python Chapter 8: Convolutional neural network Chapter Goal: Help the reader master CNN with the theory & practical concepts No of pages: 30 Sub - Topics: 1. Moving from NN to CNN 2. Key parameters within CNN 3. Implement CNN in Excel & Python Chapter 9: RNN Chapter Goal: Help the reader master RNN with the theory & practical concepts No of pages: 25 Sub - Topics: 1. Need for RNN 2. Key variations of RNN 3. Implementing RNN in Excel & Python Chapter 10: word2vec Chapter Goal: Help the reader master word2vec with the theory & practical concepts No of pages: 20 1. Need for word2vec 2. Implementing word2vec in Excel & Python Chapter 11: Unsupervised learning - clustering Chapter Goal: Help the reader master clustering with the theory & practical concepts No of pages: 15 Sub - Topics: 1. k-Means clustering 2. Hierarchical clustering 3. Implement clustering in Excel, R & Python Chapter 12: PCA Chapter Goal: Help the reader master PCA with the theory & practical concepts No of pages: 15 Sub - Topics: 1. Dimensionality reduction using PCA 2. Implement PCA in Excel, R & Python Chapter 13: Recommender systems Chapter Goal: Help the reader master recommender systems with the theory & practical concepts No of pages: 25 Sub - Topics: 1. user based collaborative filtering 2. Item based collaborative filtering 3. Matrix factorization 4. Implementing the above algorithms in Excel, R & Python Chapter 14: Implement algorithms in the cloud Chapter Goal: Help the reader understand the ways to implement algorithms in the cloud No of pages: 30 Sub - Topics: 1. Implementing machine learning algorithms in AWS 2. Implementing machine learning algorithms in Azure 3. Implementing machine learning algorithms in GCP