Chapter 1: Introduction to Machine Learning
Chapter Goal: This chapter walks through the What, Why, Where and How kind of questions, generally asked by many beginners in Machine Learning. The answers will set the momentum and direction for the chapters to follow.
No of pages: 25
Sub -Topics
1. What does a Machine really learn?
2. Why is Machine Learning so popular?
3. Where do we use Machine Learning?
4. How is Machine Learning changing our way of life?
5. Machine Learning Tools and Software
6. Machine Learning using R
Chapter 2: Data Exploration and Preparation
Chapter Goal: The basis for building a good Machine Learning model is to have a clear understanding and well preparedness of data. This chapter will explain ways to explore the data for understanding and how to deal with the inconsistencies present in the data.
No of pages: 50
Sub - Topics
1. Various Data Formats
2. Summary Statistics
3. Missing Values
4. Data Imputation
5. Transforming Unstructured Data
Chapter 3: Sampling and Resampling Techniques
Chapter Goal: In many real-world dataset, the biggest challenge is the sheer volume of the data. This volume makes the computational limitations more evident for building the Machine Learning Models. In order to reduce the need for computational power and at the same time not compromising the efficacy of the model, this chapter explains some sampling techniques for selecting a smaller dataset from the bigger dataset. We will also explore the idea of resampling which increases the accuracy of many Machine Learning Models.
No of pages: 50
Sub - Topics:
1. Simple Random Sampling
2. Systematic Sampling
3. Stratified Sampling
4. Cluster Sampling
5. Bootstrap sampling
Chapter 4: Visualization of Data
Chapter Goal: Visualization is a powerful tool to see through things in our data which might not be very evident when a manual exploration is carried out. This chapter will explain some of the commonly used plots and diagrams to see visually appealing insights coming out from our data.
No of pages: 50
Sub - Topics:
1. Scatterplot, Histogram and Box Plot
2. Heat maps and Waterfall Charts
3. Dendrogram for Clustering
4. Bubble Chart and Word Cloud
5. Sankey Diagrams
6. Time Series Graphs
7. 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: 40
Sub - Topics:
1. Feature Ranking
2. Variable Subset Selection
3. Dimensionality Reduction
Chapter 6: Machine Learning Models: Theory and Practice
Chapter 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: 150
Sub - Topics:
1. Linear, Logistic and Polynomial Regression Models
2. Decision Tree
3. Clustering Algorithms
4. Text Mining Approaches
5. Neural Networks
6. Support Vector Machine
7. Association Rule Mining
8. Deep Learning
9. Online Machine Learning Algorithm
Chapter 7: Machine Learning Model Evaluation
Chapter 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: 45
Sub - Topics:
1. k-fold Cross Validation
2. Bootstrap sampling
3. ROC Curve
4. Accuracy, Precision and Recall
5. Sensitivity and Specificity
Chapter 8: Model Performance Improvement
Chapter 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: 60
Sub - Topics:
1. Parameter Tuning
2. Ensemble based ML Model
3. Bagging Technique
4. Boosting Methods
Chapter 9: Time Series Modelling
Chapter 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: 40
Sub - Topics:
1. White noise, autoregressive (AR) models, moving average (MA) models, ARMA models
2. Stationarity, differencing, detrending, seasonality
3. Dickey-Fuller test for stationarity
4. Autocorrelation function (ACF) and partial autocorrelation function (PACF)
5. Box-Jenkins methodology for selecting an ARIMA model
Chapter 10: Scalable Machine Learning and related technology
Chapter 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: 80
Sub - Topics:
1. Introduction to Map Reduce Architecture
2. Understanding basics of Apache Hadoop, Hive and Pig
3. Integrating Apache Hadoop and R
4. Parallel Processing using R
5. Machine Learning using Apache Spark and its tools
Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow
Chapter 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: 50
Sub - Topics:
1. Using Keras and TensorFlow with R
2. Overview of RNN, CNN, LSTMs networks
3. Question Answering using Memory Network
4. Text and Image processing using Keras
