A curated list of repositories for my book Machine Learning Solutions.

You will get the chance to learn how to develop the cutting-edge data science applications using various Machine Learning (ML) techniques. This book is practical guide which can help you to build and optimize your data science applications.

• Chapter 1, Credit risk modeling, in this chapter we will build the predictive analytics model which can help us to predict the weather customer will default the loan or not. We will be using outlier detection, features transformation, ensemble machine learning algorithms and so on to get the best possible solution.

  • Credit risk modeling

• Chapter 2, Stock market price prediction, in this chapter we will build predictive model which can predict the stock index price based on historical dataset. We will use neural networks to get the best possible solution.

  • Stock index prediction

• Chapter 3, Customer analytics, in this chapter we will explore how to build the 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.

  • Customer segmentation
  • Customer lifetime value analysis
  • Customer churn analusis

• Chapter 4, Recommendation systems for e-commence, in this chapter we will be building recommendation engine for e-commerce platform. We will build recommendation engine which can recommend similar books. We will be using concepts like correlation, TF-IDF, cosine similarity to build the application.

  • Recommendation engine for e-commerce products
  • Book recommendation engine

• Chapter 5, Sentiment analysis, in this chapter we will be generating sentiment score for movie reviews. In order to get the best solution, we will be using recurrent neural networks and Long-short term memory units.

  • Sentiment analysis

• Chapter 6, Job recommendation engine, in this chapter we will build our own dataset which can be used to make job recommendation engine. We will also use already available dataset in order to build the job recommendation system. We will be using basic statistical techniques to get the best possible solution.

  • Basic job recommendation engine using TF-IDF and cosine similarity
  • Job recommendation engine

• Chapter 7, Text summarization, in this chapter we will build the application which generate the extractive summary of medical transcription. We will be using already available python libraries for our base-line approach. After that we will be using various vectorization and ranking technique to get the summary for medical document. We will also generate summary for amazon's product reviews.

  • Extractive summarization of medical transcription
  • summarization of Amazon's product reviews

• Chapter 8, Developing chatbots, in this chapter we will develop chatbot using rule-based approach and deep learning-based approach. We will be using TensorFlow and Keras to build chatbots.

  • Rule-based chatbots
  • Build a chatbot using TensorFlow
  • Build a chatbot using Keras, This bot is having reasoning ability...!

• Chapter 9, Building a real-time object recognition app, in this chapter we will learn about transfer learning. We will learn about convolutional networks and YOLO (You Only Look Once) algorithms. We will be using pre-trained models in order to develop the application.

  • Real time object detection using OpenCV
  • Real time object detection using YOLO

• Chapter 10, Face recognition and face emotion recognition, in this first half of the chapter we will build the application which can recognize the human faces. During second half of the chapter we will be developing application which can recognize facial expression of human. We will be using OpenCV, Keras and TensorFlow to build this application.

  • Simple face recognition
  • Facial emotion recognition using TensorFlow
  • Facial emotion recognition using Keras

• Chapter 11, Building gaming bots, in this chapter we will be learning about Reinforcement Learning. Here we will be using gym or universe library to get the gaming environment. We first understood Q-learning algorithm and later on we will implement the same to train our gaming bot. Here we are building bot for atari games.

  • Q-Learning for simple Atari games
  • Space Invaders gaming bot
  • Ping Pong gaming bot
  • Flappy Bird gaming bot

• Appendix A, List of cheat sheets, in this chapter we will get the list of cheat sheets for various python libraries which we are frequently using in data science applications.

  • Cheat sheets for python libraries
  • List of awesome Python frameworks, libraries, software and resources

• Appendix B, Strategy for wining hackathons, in this chapter we will get to know what the possible strategy for winning hackathons can be. I have also listed down some of the cool resources which can help you to update yourself.