Machine Learning Using R

coming out from our data.  No of pages: 50Sub - Topics: 1.Scatterplot, Histogram and Box Plot2.Heat maps and Waterfall Charts3.Dendrogram for Clustering4.Bubble Chart and Word Cloud5.Sankey Diagrams6.Time Series Graphs7.Cohort Diagram
Chapter 5: Feature Engineering Chapter Goal: One more challenge in the real world dataset is the number of features it contains. There might be hundreds of feature in a dataset but not all of it is useful for building our model. So, in order to select the features which explain our dataset more than the other features, and hence give a more accurate result, we have certain well proven technique derived from statistics. The feature engineering has now become an unavoidable step in our Machine Learning Model building process.No of pages:  40Sub - Topics:1.Feature Ranking2.Variable Subset Selection 3.Dimensionality Reduction
Chapter 6: Machine Learning Models: Theory and PracticeChapter Goal: This chapter is the core of this book. After we had the fair understanding of our data and performed the feature engineering, it's now time to build some really powerful Machine Learning Models. This chapter lists all the ML algorithms under one header. A clear demarcation will be drawn for explaining how each of these ML algorithms are different from each other and which algorithm suits the given use-cases.No of pages: 150Sub - Topics: 1.Linear, Logistic and Polynomial Regression Models2.Decision Tree3.Clustering Algorithms4.Text Mining Approaches5.Neural Networks6.Support Vector Machine7.Association Rule Mining8.Deep Learning9.Online Machine Learning Algorithm
Chapter 7: Machine Learning Model EvaluationChapter Goal: At all times, our job doesn't just end with building a Machine Learning Model but it further goes in evaluating the model's efficacy. A model is considered the best only when it crosses the benchmark accuracy and performs better than the existing models. The significance of evaluating the model increases, even more, when we want to set a common ground of comparing many different models coming out from a research and experimental project.No of pages: 45Sub - Topics: 1.k-fold Cross Validation2.Bootstrap sampling3.ROC Curve4.Accuracy, Precision and Recall5.Sensitivity and Specificity 
Chapter 8: Model Performance ImprovementChapter Goal: Once we have performed our evaluations, its time to think on how to further improve the model accuracy. And experiences show that, in many cases, we get a significant improvement over accuracy from our base models when we apply methods like Boosting and Ensemble models. This chapter will take a detailed discussion on these methods.No of pages: 60Sub - Topics:1.Parameter Tuning2.Ensemble based ML Model3.Bagging Technique4.Boosting Methods
Chapter 9: Time Series ModellingChapter Goal: So far, we have explored the entire ML process flow in good depth along with studying numerous algorithms and approaches. However, in order place this book in a unique fusion of contemporary and legacy techniques from Statistics, Machine Learning and Computer Science, this chapter will touch upon a powerful statistical modeling technique called Time Series. It has its applications in demand-supply planning, stock-market predictions, weather forecast and many other numerous places where one can establish the dependency of a variable with respect to time. Time series models identify the trend, seasonality and random component in the variable of your interest and thus capturing the pattern emerging out from the data from the past to take decision for the future.No of pages: 40Sub - Topics:1.White noise, autoregressive (AR) models, moving average (MA) models, ARMA models 2.Stationarity, differencing, detrending, seasonality3.Dickey-Fuller test for stationarity4.Autocorrelation function (ACF) and partial autocorrelation function (PACF)5.Box-Jenkins methodology for selecting an ARIMA model
Chapter 10: Scalable Machine Learning and related technologyChapter Goal: In the concluding chapter, we will discuss some of the contemporary technologies and architectures used for building scalable Machine Learning models. This chapter will give an emphasis on how the Machine Learning algorithms are going through the changes required for accommodating the new Big Data age. And how the new domains likes Data Science is gaining the popularity with just using the classic ML algorithms.No of pages: 80Sub - Topics:1.Introduction to Map Reduce Architecture2.Understanding basics of Apache Hadoop, Hive and Pig3.Integrating Apache Hadoop and R4.Parallel Processing using R5.Machine Learning using Apache Spark and its tools
Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlowChapter Goal: Certain problems which were thought to be highly complex and computationally infeasible to be solved by either by sophisticated heuristic or traditional Machine Learning algorithms, are now becoming possible to be solved using Deep Learning (DL) algorithms. Although DL as a subject derives its root from the Neural Network models of Machine Learning, its architecture is trying to mimic the way human brain works. Tasks that we humans do quite effortlessly, like driving a car, processing speech and differentiating apples from oranges requires enormous amount of cognitive ability which we never realize. DL algorithms are getting better in performing such tasks more efficiently than humans now.      No of pages: 50Sub - Topics:1.Using Keras and TensorFlow with R2.Overview of RNN, CNN, LSTMs networks3.Question Answering using Memory Network4.Text and Image processing using Keras