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Machine Learning Solutions
Machine Learning Solutions

Machine Learning Solutions: Expert techniques to tackle complex machine learning problems using Python

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Machine Learning Solutions

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Key benefits

  • • 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

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.

Who is this book 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 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 27, 2018
Length: 566 pages
Edition : 1st
Language : English
ISBN-13 : 9781788398893
Category :
Languages :

What do you get with eBook?

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Product Details

Publication date : Apr 27, 2018
Length: 566 pages
Edition : 1st
Language : English
ISBN-13 : 9781788398893
Category :
Languages :

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Frequently bought together


Stars icon
Total 111.97
Mastering Machine Learning Algorithms
€36.99
Machine Learning Algorithms
€41.99
Machine Learning Solutions
€32.99
Total 111.97 Stars icon
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Table of Contents

11 Chapters
Credit Risk Modeling Chevron down icon Chevron up icon
Stock Market Price Prediction Chevron down icon Chevron up icon
Customer Analytics Chevron down icon Chevron up icon
Recommendation Systems for E-Commerce Chevron down icon Chevron up icon
Sentiment Analysis Chevron down icon Chevron up icon
Job Recommendation Engine Chevron down icon Chevron up icon
Text Summarization Chevron down icon Chevron up icon
Developing Chatbots Chevron down icon Chevron up icon
Building a Real-Time Object Recognition App Chevron down icon Chevron up icon
Face Recognition and Face Emotion Recognition Chevron down icon Chevron up icon
Building Gaming Bot Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(5 Ratings)
5 star 60%
4 star 40%
3 star 0%
2 star 0%
1 star 0%
EDDY GIMENEZ Dec 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
there is no waste in it, everything is clear and very well documented for the purpose of learning the different approaches to apply in machine learning algorithms.
Amazon Verified review Amazon
Amazon buyer Jul 12, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book.
Amazon Verified review Amazon
Anish Shah Sep 20, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A practical approach towards Machine learning...The use of real-world datasets for various applications is very thoughtful by the author as it greatly reduces the learning curve. Highly recommended for those who want to practice real projects using scikit-learn library of Python...
Amazon Verified review Amazon
Dr. Franco Arda Sep 10, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I did not like Jalay's first book on NLP, but this one is pretty good.This book covers 11 topics in ML.It's impossible to be an expert at all 11 topics ranging from structured data to NLP and RL.Again, NLP is not her forte - see chapter 5 on Sentiment Analysis.I particular loved Chapter 3: CUSTOMER ANALYTICS. The author is very strong at structured data analysis, creating hypothesis, visualizing ....check our her corresponding Notebook on GitHub. Anyone who thinks he's good at ML because of a high score on a Kaggle competition will face the brutal reality of a Data Scientist with chapter 3.Jalay labels churn customers manually. That's really hard as there's no clear cut to when a customer turns profitable or not.It's data wrangling at it's best. Really impressive.Kudos
Amazon Verified review Amazon
Amazon Customer Aug 30, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
It's good in understanding the ML in hands on wise. However it would be helpful if parallel codes available in github are updated according to Tensorflow latest version.when I trying the samples available for seq2seq in github doesnt work for Tensorflow latest version.
Amazon Verified review Amazon
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