Book description
Practical, hands-on solutions in Python to overcome any problem in Machine Learning
About This Book- 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
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
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.
Style and approachThis book is a step-by-step guide on how to develop machine learning applications for various domains. Each chapter of this book contains the practical guide on how to build specific machine learning applications from its base-line approach to the best possible approach. Basic necessary concepts, conman mistakes for every approach and optimization techniques are discussed for each application.
Table of contents
-
Machine Learning Solutions
- Table of Contents
- Machine Learning Solutions
- Foreword
- Contributors
- Preface
-
1. Credit Risk Modeling
- Introducing the problem statement
-
Understanding the dataset
- Understanding attributes of the dataset
-
Data analysis
- Data preprocessing
-
Basic data analysis followed by data preprocessing
- Listing statistical properties
- Finding missing values
- Replacing missing values
- Correlation
- Detecting outliers
- Outliers detection techniques
- Percentile-based outlier detection
- Median Absolute Deviation (MAD)-based outlier detection
- Standard Deviation (STD)-based outlier detection
- Majority-vote-based outlier detection:
- Visualization of outliers
- Handling outliers
- Revolving utilization of unsecured lines
- Age
- Number of time 30-59 days past due not worse
- Debt ratio
- Monthly income
- Number of open credit lines and loans
- Number of times 90 days late
- Number of real estate loans or lines
- Number of times 60-89 days past due not worse
- Number of dependents
- Feature engineering for the baseline model
- Selecting machine learning algorithms
- Training the baseline model
- Understanding the testing matrix
- Testing the baseline model
- Problems with the existing approach
- Optimizing the existing approach
- Implementing the revised approach
- Best approach
- Summary
-
2. Stock Market Price Prediction
- Introducing the problem statement
- Collecting the dataset
- Understanding the dataset
- Data preprocessing and data analysis
- Feature engineering
- Selecting the Machine Learning algorithm
- Training the baseline model
- Understanding the testing matrix
- Testing the baseline model
- Exploring problems with the existing approach
- Understanding the revised approach
- Implementing the revised approach
- The best approach
- Summary
-
3. Customer Analytics
- Introducing customer segmentation
- Understanding the datasets
-
Building the baseline approach
- Implementing the baseline approach
- Understanding the testing matrix
- Testing the result of the baseline approach
- Problems with the baseline approach
- Optimizing the baseline approach
- Building the revised approach
- The best approach
- Customer segmentation for various domains
- Summary
-
4. Recommendation Systems for E-Commerce
- Introducing the problem statement
- Understanding the datasets
- Building the baseline approach
- Building the revised approach
- The best approach
- Summary
-
5. Sentiment Analysis
- Introducing problem statements
- Understanding the dataset
- Building the training and testing datasets for the baseline model
- Feature engineering for the baseline model
- Selecting the machine learning algorithm
- Training the baseline model
- Understanding the testing matrix
- Testing the baseline model
- Problem with the existing approach
- How to optimize the existing approach
-
Implementing the revised approach
- Importing the dependencies
- Downloading and loading the IMDb dataset
- Choosing the top words and the maximum text length
- Implementing word embedding
- Building a convolutional neural net (CNN)
- Training and obtaining the accuracy
- Testing the revised approach
- Understanding problems with the revised approach
- The best approach
- Summary
- 6. Job Recommendation Engine
- 7. Text Summarization
-
8. Developing Chatbots
- Introducing the problem statement
- Understanding datasets
- Building the basic version of a chatbot
- Implementing the rule-based chatbot
- Testing the rule-based chatbot
- Problems with the existing approach
- Implementing the revised approach
- Testing the revised approach
- Problems with the revised approach
- The best approach
- Discussing the hybrid approach
- Summary
-
9. Building a Real-Time Object Recognition App
- Introducing the problem statement
- Understanding the dataset
- Transfer Learning
- Setting up the coding environment
- Features engineering for the baseline model
- Selecting the machine learning algorithm
- Building the baseline model
- Understanding the testing metrics
- Testing the baseline model
- Problem with existing approach
- How to optimize the existing approach
- Implementing the revised approach
- The best approach
- Summary
-
10. Face Recognition and Face Emotion Recognition
- Introducing the problem statement
- Setting up the coding environment
- Understanding the concepts of face recognition
- Approaches for implementing face recognition
- Understanding the dataset for face emotion recognition
- Understanding the concepts of face emotion recognition
- Building the face emotion recognition model
- Understanding the testing matrix
- Testing the model
- Problems with the existing approach
- How to optimize the existing approach
- The best approach
- Summary
-
11. Building Gaming Bot
- Introducing the problem statement
- Setting up the coding environment
- Understanding Reinforcement Learning (RL)
- Basic Atari gaming bot
- Implementing the basic version of the gaming bot
- Building the Space Invaders gaming bot
- Implementing the Space Invaders gaming bot
- Building the Pong gaming bot
- Implementing the Pong gaming bot
- Just for fun - implementing the Flappy Bird gaming bot
- Summary
- A. List of Cheat Sheets
- B. Strategy for Wining Hackathons
- Index
Product information
- Title: Machine Learning Solutions
- Author(s):
- Release date: April 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788390040
You might also like
book
Machine Learning Systems
Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement …
book
Machine Learning
"Table of Contents: 1 Introduction to Machine Learning 2 Preparing to Model 3 Modelling and Evaluation …
book
Machine Learning for Business
Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures …
book
Graph-Powered Machine Learning
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. …