Chapter 1, Credit Risk Modeling, builds the predictive analytics model to help us to predict whether the customer will default the loan or not. We will be using outlier detection, feature transformation, ensemble machine learning algorithms, and so on to get the best possible solution.

Chapter 2, Stock Market Price Prediction, builds a model to predict the stock index price based on a historical dataset. We will use neural networks to get the best possible solution.

Chapter 3, Customer Analytics, explores how to build customer segmentation so that marketing campaigns can be done optimally. Using various machine learning algorithms such as K-nearest neighbor, random forest, and so on, we can build the base-line approach. In order to get the best possible solution, we will be using ensemble machine learning algorithms.

Chapter 4, Recommendation Systems for E-commerce, builds a recommendation engine for e-commerce platform. It can recommend similar books. We will be using concepts such as correlation, TF-IDF, and cosine similarity to build the application.

Chapter 5, Sentiment Analysis, generates sentiment scores for movie reviews. In order to get the best solution, we will be using recurrent neural networks and Long short-term memory units.

Chapter 6, Job Recommendation Engine, is where we build our own dataset, which can be used to make a job recommendation engine. We will also use an already available dataset. We will be using basic statistical techniques to get the best possible solution.

Chapter 7, Text Summarization, covers an application to generate the extractive summary of a medical transcription. We will be using Python libraries for our base line approach. After that we will be using various vectorization and ranking techniques to get the summary for a medical document. We will also generate a summary for Amazon's product reviews.

Chapter 8, Developing Chatbots, develops a chatbot using the rule-based approach and deep learning-based approach. We will be using TensorFlow and Keras to build chatbots.

Chapter 9, Building a Real-Time Object Recognition App, teaches transfer learning. We learn about convolutional networks and YOLO (You Only Look Once) algorithms. We will be using pre-trained models to develop the application.

Chapter 10, Face Recognition and Face Emotion Recognition, covers an application to recognize human faces. During the second half of this chapter, we will be developing an application that can recognize facial expressions of humans. We will be using OpenCV, Keras, and TensorFlow to build this application.

Chapter 11, Building Gaming Bots, teaches reinforcement learning. Here, we will be using the gym or universe library to get the gaming environment. We'll first understand the Q-learning algorithm, and later on we will implement the same to train our gaming bot. Here, we are building bot for Atari games.

Appendix A, List of Cheat Sheets, shows cheat sheets for various Python libraries that we frequently use in data science applications.

Appendix B, Strategy for Wining Hackathons, tells you what the possible strategy for winning hackathons can be. I have also listed down some of the cool resources that can help you to update yourself.