Russano, Euan, author.
Fundamentals of machine learning using Python / Euan Russano and Elaine Ferreira Avelino. - Canada : Arcler Press, c2020. - xix, 324 pages : color illustrations
To access the E-Book : https://www.bibliotex.com/ (Log-in/Register is required).
Includes bibliographical references and index.
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 : Recognize Handwritten Digits Using Neural Networks
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.
In English text.
Euan Russano and Elaine Ferreira Avelino. author
EBOP QA 76.73 / R88 2020
Fundamentals of machine learning using Python / Euan Russano and Elaine Ferreira Avelino. - Canada : Arcler Press, c2020. - xix, 324 pages : color illustrations
To access the E-Book : https://www.bibliotex.com/ (Log-in/Register is required).
Includes bibliographical references and index.
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 : Recognize Handwritten Digits Using Neural Networks
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.
In English text.
Euan Russano and Elaine Ferreira Avelino. author
EBOP QA 76.73 / R88 2020