Introduction to Machine Learning with Python

Introduction to Machine Learning with Python

Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many ap...

Full description

Saved in:
Bibliographic Details
Main Author: Chopra, Deepti
Other Authors: Khurana, Roopal
Format: eBook
Language:English
Published: Hershey : Bentham Science Publishers, 2023.
Subjects:
Machine learning.
Python (Computer program language)
Online Access:Click for online access
Table of Contents:
  • Cover
  • Title
  • Copyright
  • End User License Agreement
  • Contents
  • Foreword
  • Preface
  • CONSENT FOR PUBLICATION
  • CONFLICT OF INTEREST
  • ACKNOWLEDGEMENT
  • Introduction to Python
  • INTRODUCTION
  • Web Development
  • Game Development
  • Artificial Intelligence and Machine Learning
  • Desktop GUI
  • SETTING UP PYTHON ENVIRONMENT
  • Steps Involved In Installing Python On Windows Include The Following:
  • Steps involved in installing Python on Macintosh include the following
  • Setting Up Path
  • Setting Up Path In The Unix/linux
  • WHY PYTHON FOR DATA SCIENCE?
  • ECOSYSTEM FOR PYTHON MACHINE LEARNING
  • ESSENTIAL TOOLS AND LIBRARIES
  • Jupyter Notebook
  • Pip Install Jupiter
  • NumPy
  • Pandas
  • Scikit-learn
  • SciPy
  • Matplotlib
  • Mglearn
  • PYTHON CODES
  • CONCLUSION
  • EXERCISES
  • REFERENCES
  • Introduction To Machine Learning
  • INTRODUCTION
  • DESIGN A LEARNING SYSTEM
  • Selection Of Training Set
  • Selection Of Target Function
  • Selection Of A Function Approximation Algorithm
  • PERSPECTIVE AND ISSUES IN MACHINE LEARNING
  • Issues In Machine Learning
  • Quality of Data
  • Improve the Quality of Training
  • Overfitting the Training Data
  • Machine Learning Involves A Complex Process
  • Insufficient training data
  • Feasibility of Learning An Unknown Target Function
  • Collection of Data
  • Pre-processing of Data
  • Finding The Model That Will Be Best For The Data
  • Training and Testing Of The Developed Model Evaluation
  • In Sample Error and Out of Sample Error
  • APPLICATIONS OF MACHINE LEARNING
  • Virtual Personal Assistants
  • Traffic Prediction
  • Online Transportation Networks
  • Video Surveillance System
  • Social Media Services
  • People you May Know
  • Face Recognition
  • Similar Pins
  • Sentiment Analysis
  • Email Spam and Malware Filtering
  • Online Customer Support
  • Result Refinement of a Search Engine
  • Product Recommendations
  • Online Fraud Detection
  • Online Gaming
  • Financial Services
  • Healthcare
  • Oil and Gas
  • Self-driving Cars
  • Automatic Text Translation
  • Dynamic Pricing
  • Classification of News
  • Information Retrieval
  • Robot Control
  • CONCLUSION
  • EXERCISES
  • REFERENCES
  • Linear Regression and Logistic Regression
  • INTRODUCTION
  • LINEAR REGRESSION
  • Linear Regression In One Variable
  • Linear Regression In Multiple Variables
  • Overfitting and Regularization In Linear Regression
  • GRADIENT DESCENT
  • POLYNOMIAL REGRESSION
  • Features of Polynomial Regression
  • LOGISTIC REGRESSION
  • Overfitting and Regularisation in Logistic Regression
  • BINARY CLASSIFICATION AND MULTI-CLASS CLASSIFICATION
  • Binary Classification Tests
  • Classification Accuracy
  • Error Rate
  • Sensitivity
  • Specificity
  • PYTHON CODES
  • CONCLUSION
  • EXERCISES
  • REFERENCES
  • Support Vector Machine
  • INTRODUCTION
  • SUPPORT VECTOR CLASSIFICATION
  • The Maximal Margin Classifier
  • Soft Margin Optimization

Similar Items