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

Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Basics of Machine Learning
	Regression and Classification
		Training and Testing Data
		The Need for Validation Dataset
		Measures of Accuracy
			Absolute Error
			Root Mean Square Error
			Confusion Matrix
		AUC Value and ROC Curve
	Unsupervised Learning
	Typical Approach Towards Building a Model
		Where Is the Data Fetched From?
		Which Data Needs to Be Fetched?
		Pre-processing the Data
		Feature Interaction
		Feature Generation
		Building the Models
		Productionalizing the Models
		Build, Deploy, Test, and Iterate
	Summary
Chapter 2: Linear Regression
	Introducing Linear Regression
		Variables: Dependent and Independent
		Correlation
		Causation
	Simple vs. Multivariate Linear Regression
	Formalizing Simple Linear Regression
		The Bias Term
		The Slope
	Solving a Simple Linear Regression
	More General Way of Solving a Simple Linear Regression
		Minimizing the Overall Sum of Squared Error
		Solving the Formula
	Working Details of Simple Linear Regression
		Complicating Simple Linear Regression a Little
		Arriving at Optimal Coefficient Values
		Introducing Root Mean Squared Error
	Running a Simple Linear Regression in R
		Residuals
		Coefficients
		SSE of Residuals (Residual Deviance)
		Null Deviance
		R Squared
		F-statistic
	Running a Simple Linear Regression in Python
	Common Pitfalls of Simple Linear Regression
	Multivariate Linear Regression
		Working details of Multivariate Linear Regression
		Multivariate Linear Regression in R
		Multivariate Linear Regression in Python
		Issue of Having a Non-significant Variable in the Model
		Issue of Multicollinearity
		Mathematical Intuition of Multicollinearity
		Further Points to Consider in Multivariate Linear Regression
	Assumptions of Linear Regression
	Summary
Chapter 3: Logistic Regression
	Why Does Linear Regression Fail for Discrete Outcomes?
	A More General Solution: Sigmoid Curve
		Formalizing the Sigmoid Curve (Sigmoid Activation)
		From Sigmoid Curve to Logistic Regression
		Interpreting the Logistic Regression
		Working Details of Logistic Regression
		Estimating Error
			Scenario 1
			Scenario 2
		Least Squares Method and Assumption of Linearity
	Running a Logistic Regression in R
	Running a Logistic Regression in Python
	Identifying the Measure of Interest
	Common Pitfalls
		Time Between Prediction and the Event Happening
		Outliers in Independent variables
	Summary
Chapter 4: Decision Tree
	Components of a Decision Tree
	Classification Decision Tree When There Are Multiple Discrete Independent Variables
		Information Gain
		Calculating Uncertainty: Entropy
		Calculating Information Gain
		Uncertainty in the Original Dataset
		Measuring the Improvement in Uncertainty
		Which Distinct Values Go to the Left and Right Nodes
			Gini Impurity
			Splitting Sub-nodes Further
		When Does the Splitting Process Stop?
	Classification Decision Tree for Continuous Independent Variables
	Classification Decision Tree When There Are Multiple Independent Variables
	Classification Decision Tree When There Are Continuous and Discrete Independent Variables
	What If the Response Variable Is Continuous?
		Continuous Dependent Variable and Multiple Continuous Independent Variables
		Continuous Dependent Variable and Discrete Independent Variable
		Continuous Dependent Variable and Discrete, Continuous Independent Variables
	Implementing a Decision Tree in R
	Implementing a Decision Tree in Python
	Common Techniques in Tree Building
	Visualizing a Tree Build
	Impact of Outliers on Decision Trees
	Summary
Chapter 5: Random Forest
	A Random Forest Scenario
		Bagging
		Working Details of a Random Forest
	Implementing a Random Forest in R
		Parameters to Tune in a Random Forest
		Variation of AUC by Depth of Tree
	Implementing a Random Forest in Python
	Summary
Chapter 6: Gradient Boosting Machine
	Gradient Boosting Machine
	Working details of GBM
	Shrinkage
	AdaBoost
		Theory of AdaBoost
		Working Details of AdaBoost
	Additional Functionality for GBM
	Implementing GBM in Python
	Implementing GBM in R
	Summary
Chapter 7: Artificial Neural Network
	Structure of a Neural Network
	Working Details of Training a Neural Network
		Forward Propagation
		Applying the Activation Function
		Back Propagation
		Working Out Back Propagation
		Stochastic Gradient Descent
		Diving Deep into Gradient Descent
		Why Have a Learning Rate?
	Batch Training
		The Concept of Softmax
	Different Loss Optimization Functions
		Scaling a Dataset
			Scenario Without Scaling the Input
			Scenario with Input Scaling
	Implementing Neural Network in Python
	Avoiding Over-fitting using Regularization
	Assigning Weightage to Regularization term
	Implementing Neural Network in R
	Summary
Chapter 8: Word2vec
	Hand-Building a Word Vector
	Methods of Building a Word Vector
	Issues to Watch For in a Word2vec Model
		Frequent Words
		Negative Sampling
	Implementing Word2vec in Python
	Summary
Chapter 9: Convolutional Neural Network
	The Problem with Traditional NN
		Scenario 1
		Scenario 2
		Scenario 3
		Scenario 4
	Understanding the Convolutional in CNN
		From Convolution to Activation
		From Convolution Activation to Pooling
		How Do Convolution and Pooling Help?
	Creating CNNs with Code
	Working Details of CNN
	Deep Diving into Convolutions/Kernels
	From Convolution and Pooling to Flattening: Fully Connected Layer
		From One Fully Connected Layer to Another
		From Fully Connected Layer to Output Layer
	Connecting the Dots: Feed Forward Network
	Other Details of CNN
	Backward Propagation in CNN
	Putting It All Together
	Data Augmentation
	Implementing CNN in R
	Summary
Chapter 10: Recurrent Neural Network
	Understanding the Architecture
	Interpreting an RNN
	Working Details of RNN
		Time Step 1
		Time Step 2
		Time Step 3
	Implementing RNN: SimpleRNN
		Compiling a Model
		Verifying the Output of RNN
	Implementing RNN: Text Generation
	Embedding Layer in RNN
	Issues with Traditional RNN
		The Problem of Vanishing Gradient
		The Problem of Exploding Gradients
	LSTM
	Implementing Basic LSTM in keras
	Implementing LSTM for Sentiment Classification
	Implementing RNN in R
	Summary
Chapter 11: Clustering
	Intuition of clustering
		Building Store Clusters for Performance Comparison
		Ideal Clustering
		Striking a Balance Between No Clustering and Too Much Clustering: K-means Clustering
	The Process of Clustering
	Working Details of K-means Clustering Algorithm
		Applying the K-means Algorithm on a Dataset
		Properties of the K-means Clustering Algorithm
			Totss (Total Sum of Squares)
			Cluster Centers
			Tot.withinss
			Betweenss
	Implementing K-means Clustering in R
	Implementing K-means Clustering in Python
	Significance of the Major Metrics
	Identifying the Optimal K
	Top-Down Vs. Bottom-Up Clustering
		Hierarchical Clustering
		Major Drawback of Hierarchical Clustering
	Industry Use-Case of K-means Clustering
	Summary
Chapter 12: Principal Component Analysis
	Intuition of PCA
	Working Details of PCA
	Scaling Data in PCA
	Extending PCA to Multiple Variables
	Implementing PCA in R
	Implementing PCA in Python
	Applying PCA to MNIST
	Summary
Chapter 13: Recommender Systems
	Understanding k-nearest Neighbors
	Working Details of User-Based Collaborative Filtering
		Euclidian Distance
			Normalizing for a User
			Issue with Considering a Single User
		Cosine Similarity
			Weighted Average Rating Calculation
			Choosing the Right Approach
			Calculating the Error
		Issues with UBCF
	Item-Based Collaborative Filtering
	Implementing Collaborative Filtering in R
	Implementing Collaborative Filtering in Python
	Working Details of Matrix Factorization
	Implementing Matrix Factorization in Python
	Implementing Matrix Factorization in R
	Summary
Chapter 14: Implementing Algorithms in the Cloud
	Google Cloud Platform
	Microsoft Azure Cloud Platform
	Amazon Web Services
	Transferring Files to the Cloud Instance
	Running Instance Jupyter Notebooks from Your Local Machine
	Installing R on the Instance
	Summary
Appendix: Basics of Excel, R, and Python
	Basics of Excel
	Basics of R
		Downloading R
		Installing and Configuring RStudio
		Getting Started with RStudio
	Basics of Python
		Downloading and installing Python
		Basic operations in Python
		Numpy
		Number generation using Numpy
		Slicing and indexing
		Pandas
		Indexing and slicing using Pandas
		Summarizing data
Index