Machine Learning Solutions

▶Book Description
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job.

You'll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you'll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples.

The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions.

In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.

▶What You Will Learn
⦁ Select the right algorithm to derive the best solution in ML domains
⦁ Perform predictive analysis effciently using ML algorithms
⦁ Predict stock prices using the stock index value
⦁ Perform customer analytics for an e-commerce platform
⦁ Build recommendation engines for various domains
⦁ Build NLP applications for the health domain
⦁ Build language generation applications using different NLP techniques
⦁ Build computer vision applications such as facial emotion recognition

▶Key Features
⦁ Master the advanced concepts, methodologies, and use cases of machine learning
⦁ Build ML applications for analytics, NLP and computer vision domains
⦁ Solve the most common problems in building machine learning models

▶Who This Book Is For
This book is for the intermediate users such as machine learning engineers, data engineers, data scientists, and more, who want to solve simple to complex machine learning problems in their day-to-day work and build powerful and efficient machine learning models. A basic understanding of the machine learning concepts and some experience with Python programming is all you need to get started with this book.

▶What this book covers
⦁ 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 shortterm 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.