Fundamentals of Machine Learning using Python
Authors: Euan Russano – Elaine Ferreira Avelino
ISBN-10: 177407365X
ISBN-13: 9781774073650
Publication Date 出版日期: 2019-11-01
Print Length 页数: 290 pages
Book Description
By finelybook
Fundamentals of Machine Learning discusses the basics of python,use of python in computing and provides a general outlook on machine learning. This book provides an insight into concepts such as linear regression with one variable,linear algebra,and linear regression with multiple inputs. The classification with logistics regression model,regularization,neural networks,decision trees are explained in this book. The introduction to several concepts of machine learning such as component analysis,classification using k-Nearest Algorithm,k Means Clustering,computing with Tensor flow and natural language processing have been explained. This book explains the fundamental concepts of machine learning.
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 Introduction to Python
Chapter 2 Computing Things With Python
Chapter 3 A General Outlook on Machine Learning
Chapter 4 Elements of Machine Learning
Chapter 5 Linear Regression With One Variable
Chapter 6 A General Review On Linear Algebra
Chapter 7 Linear Regression With Multiple Inputs/Features
Chapter 8 Classification Using Logistic Regression Model
Chapter 9 Regularization
Chapter 10 Introduction To Neural Networks
Chapter 11 Introduction To Decision Trees and Random Forest
Chapter 12 Principal Component Analysis
Chapter 13 Classification Using K-Nearest Neighbor Algorithm
Chapter 14 Introduction To Kmeans Clustering
Chapter 15 Computing With Tensorflow: Introduction And Basics
Chapter 16 Tensorflow: Activation Functions And Optimization
Chapter 17 Introduction To Natural Language Processing
Chapter 18 Project: Recognize Handwritten Digits Using Neural Networks
AppendixA
Bibliography
Index
Back Cover
