Price loading...

Applied Machine Learning for Data Science Practitioners

Computing & Internet

Price data last checked 102 day(s) ago - refreshing...

View at Amazon

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.

Product Specifications

Format: hardcover
ASIN: 1394155379
Category: Books > Subjects > Computing & Internet
Domain: Amazon UK
Release Date: 27 March 2025
Listed Since: 06 July 2024

Barcode

No barcode data available

Applied Machine Learning for Data Science Practitioners

Price History & Forecast

No Price Data Available

Price Distribution

Description

Product Specifications

Barcode

Similar Products You Might Like

Practical Machine Learning for Data Analysis Using Python

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems

The Data Science Manual: A Comprehensive Guide to Tools and Techniques for Data Analysis, Modeling, and Deployment with Python

Applied Machine Learning

The Data Science Handbook

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R

Data Science Handbook: A Practical Approach

Data Science Concepts and Techniques with Applications

Thinking Data Science: A Data Science Practitioner’s Guide (The Springer Series in Applied Machine Learning)

Python Machine Learning Case Studies: Five Case Studies for the Data Scientist

Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

The Beginner's Guide to Data Science

Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)

Apress Essential Data Analytics, Data Science, and AI Guide

Fundamentals of Data Science: Theory and Practice

Machine Learning with Python: Theory and Implementation

Building an Effective Data Science Practice: A Framework to Bootstrap and Manage a Successful Data Science Practice

Machine Learning Foundations: Volume 1: Supervised Learning

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Machine Learning for Healthcare Applications