Book details
File Size: 69 MB
Format: epub
Print Length: 568 pages
Publisher: Packt Publishing; 1 edition (April 27, 2018)
Publication Date: April 27, 2018
Language: English
ASIN: B07CVXTHLJ
Who This Book Is For
This book is for 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.
Practical, hands-on solutions in Python to overcome any problem in Machine Learning
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
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 andNLP,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 outprocessing, 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
Table of Contents
Credit Risk Modeling
Stock Market Price Prediction
Customer Analytics
Recommendation Systems for E-Commerce
Sentiment Analysis
Jobs Recommendation Engine
Text Summarization
Developing Chatbots
Building a Real-Time Object Recognition App
Face Recognition and Face Emotion Recognition
Building Gaming bot
Appendix A
Appendix B
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