Book description
With the flexibility and features of scikitlearn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level
Key Features
 Explore scikitlearn uniform API and its application into any type of model
 Understand the difference between supervised and unsupervised models
 Learn the usage of machine learning through realworld examples
Book Description
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikitlearn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over realworld datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.
The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikitlearn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.
By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.
What you will learn
 Understand the importance of data representation
 Gain insights into the differences between supervised and unsupervised models
 Explore data using the Matplotlib library
 Study popular algorithms, such as kmeans, MeanShift, and DBSCAN
 Measure model performance through different metrics
 Implement a confusion matrix using scikitlearn
 Study popular algorithms, such as NaiveBayes, Decision Tree, and SVM
 Perform error analysis to improve the performance of the model
 Learn to build a comprehensive machine learning program
Who this book is for
Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikitlearn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikitlearn or machine learning algorithms.
Publisher resources
Table of contents
 Preface
 Introduction to ScikitLearn
 Unsupervised Learning: RealLife Applications

Supervised Learning: Key Steps
 Introduction
 Model Validation and Testing

Evaluation Metrics
 Evaluation Metrics for Classification Tasks
 Exercise 12: Calculating Different Evaluation Metrics over a Classification Task
 Choosing an Evaluation Metric
 Evaluation Metrics for Regression Tasks
 Exercise 13: Calculating Evaluation Metrics over a Regression Task
 Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset
 Error Analysis
 Summary
 Supervised Learning Algorithms: Predict Annual Income
 Artificial Neural Networks: Predict Annual Income
 Building Your Own Program

Appendix
 Chapter 1: Introduction to scikitlearn

Chapter 2: Unsupervised Learning: Reallife Applications
 Activity 3: Using Data Visualization to Aid the Preprocessing Process
 Activity 4: Applying the kmeans Algorithm to a Dataset
 Activity 5: Applying the MeanShift Algorithm to a Dataset
 Activity 6: Applying the DBSCAN Algorithm to the Dataset
 Activity 7: Measuring and Comparing the Performance of the Algorithms
 Chapter 3: Supervised Learning: Key Steps
 Chapter 4: Supervised Learning Algorithms: Predict Annual Income
 Chapter 5: Artificial Neural Networks: Predict Annual Income
 Chapter 6: Building Your Own Program
Product information
 Title: Machine Learning Fundamentals
 Author(s):
 Release date: November 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789803556
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