The Machine Learning Workshop - Second Edition

By : Hyatt Saleh

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.

Preface

About the Book

1. Introduction to Scikit-Learn

Introduction

Introduction to Machine Learning

Supervised and Unsupervised Learning

Summary

Free Chapter

2. Unsupervised Learning – Real-Life Applications

Introduction

Clustering

Exploring a Dataset – Wholesale Customers Dataset

Data Visualization

Mean-Shift Algorithm

DBSCAN Algorithm

Evaluating the Performance of Clusters

Summary

3. Supervised Learning – Key Steps

Introduction

Supervised Learning Tasks

Model Validation and Testing

Evaluation Metrics

Error Analysis

Summary

4. Supervised Learning Algorithms: Predicting Annual Income

Introduction

Exploring the Dataset

The Naïve Bayes Algorithm

The Decision Tree Algorithm

The Support Vector Machine Algorithm

Error Analysis

Summary

5. Supervised Learning – Key Steps

Introduction

Artificial Neural Networks

Applying an Artificial Neural Network

Performance Analysis

Summary

6. Building Your Own Program

Introduction

Program Definition

Saving and Loading a Trained Model

Interacting with a Trained Model

Summary

Appendix

1. Introduction to Scikit-Learn

2. Unsupervised Learning – Real-Life Applications

3. Supervised Learning – Key Steps

4. Supervised Learning Algorithms: Predicting Annual Income

5. Artificial Neural Networks: Predicting Annual Income

6. Building Your Own Program

Customer Reviews

5 star

4 star

3 star

2 star

1 star

5. Artificial Neural Networks: Predicting Annual Income

Activity 5.01: Training an MLP for Our Census Income Dataset

Solution:

Import all the elements required to load and split a dataset, to train an MLP, and to measure accuracy:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score

Using the preprocessed Census Income Dataset, separate the features from the target, creating the variables X and Y:
```
data = pd.read_csv("census_income_dataset_preprocessed.csv")
X = data.drop("target", axis=1)
Y = data["target"]
```
As explained previously, there are several ways to achieve the separation of X and Y, and the main thing to consider is that X should contain the features for all instances, while Y should contain the class label of all instances.
Divide the dataset into training, validation, and testing sets, using a split ratio of 10...

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Related Content you might be interested in

Current Title:

The Machine Learning Workshop - Second Edition

The Deep Learning with PyTorch Workshop

Applied Deep Learning with PyTorch

The Supervised Learning Workshop

5. Artificial Neural Networks: Predicting Annual Income

Activity 5.01: Training an MLP for Our Census Income Dataset