2SI AASHTO Abaqus Actix Analyzer ADINA Adobe Acrobat Airports AISC Algorithms Aluminium Animation ANSYS APF Nexus Aquaveo Architecture Artificial Intelligence ASCE ASDIP Ashampoo Asphalt ASTM Autocad Autodesk Bentley BetonExpress BIMware MASTER BitCoin Blast Books Bridges Buildings CAD Calculus CCleaner Cement Chasm Consulting Civil 3D Clay Coastal Structures Codes ComingSoon Computer Engineering Concrete Conference Books CorelCAD Corrosion Courses/Lessons Cranes CSI CTI Vespa2 Daemon Tools Dams Data Analysis Data Mining Deep Freeze Deep Learning Design and Build Websites DiCad Strakon Dictionary Digital Canal DimSoln Dlubal Drainage Dredging Dynamics Earthquake Earthworks EC2 Reinforcement EC3 Steel Connections EC5 Timber Connections Economy Elasticity EnerCalc English Language Ensoft Entertainment Environmental Engineering Equipments Erosion ESPRI ETABS Eurocode Excavation Excel Expansive Soils FIDES DV-Partner Finite Element Model Fire Safety Fluid Mechanics Forensic Engineering Formulas Fortran Foundations Foxit Phantom FRP Game of Thrones Geo-Slope Geo5 Geoenvironmental Engineering Geographic Information Systems Geology Geomechanics Geomembranes Geosolve GeoStru GeoStudio Geosynthetics GeoTec Elpla Geotechnical Engineering Golden Software Graitec Grammar Grapher Ground Anchors Groundwater Grouting Gstarsoft Harry Potter Highways Historic Structures HTML5 Hydraulics Hydrology IBM IceCream Ebook Reader IDEA StatiCa IELTS IES Ikon Science InfraWorks Itasca Flac2D Java KESZ ConSteel Landslides LaTeX Limcon LimitState: GEO Lindo Linear Algebra Lingo Liquefaction LPile Lusas Malwarbytes Management Maple MapViewer Masonry Walls Materials Mathematica Maths MathType MATLAB Mechanical Engineering Mechanics Metaheuristic Algorithms Microsoft MicroStation Midas Minitab Money Movies Nanocomposites Neural Networks NovoTech Nuclear Power Plants Numerical Mathematics OaSys Octave Office Offshore OLGA Optimization Pavements PC Games PDF Phase2 Physics Piles PipeLines Pipesim Plants Plasticity Plaxis Polymath Polymer Power GEOPAK Powerpoint Precast Prestressed Concrete Pro Sap Proektsoft Programming Projects PROKON ProStructures ProtaStructure PTC MathCad Python QuickConcreteWall QuickConcretWall QuickFooting QuickMasonary QuickRWall R Language Radar System Railways RAM RCDC Regression Analysis Reinforced Concrete Reinforced Masonry Retaining Structures RetainPro Revit RISA Risk Analysis Roads RocDoc Rock Mechanics Rocscience Roofs S-Frame S.T.A. DATA 3Muri SAFE Safety Salford Predictive Modeler SAP2000 SCAD Office Schedule it Schlumberger SCIA Engineer Security Seepage Settle 3D Sewage ShapeBuilder Shotcrete Slide Slope Stability Sludge Smart Cities Snow Loads Softwares Soil Improvement Soil Mechanics SoilOffice SoilWorks SPSS STAAD.Foundation STAAD.Pro Standards Stat-Ease Stata Statics Statistics Steel Stone Strater StruCalc Structural Designer Structural Office StructurePoint Structures StruSoft Surfer Surveying Swarm Intelligence System Mechanic Tableau Technical Drawing Technology Tedds Tekla Testing The Big Bang Theory Thermodynamics Timber TOEFL Topology Torrent Traffic Transmission Lines Transportation Engineering Trimble Tunnels Turbo Pascal TV Series TweakBit Unsaturated Visual Basic Visual Integrity VisualAnalysis VisualFoundation VisualPlate VisualShearWall Water Welding Wind Loads Windows WinRAR Wolfram Wood Word

Introduction to Machine Learning with Python: A Guide for Data Scientists

September 22, 2019
Andreas Müller, Sarah Guido ... 400 pages - Publisher: O'Reilly Media; (October, 2016) ... Language: English - ISBN-10: 1449369413 - ISBN-13: 978-1449369415.

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn: Fundamental concepts and applications of machine learning + Advantages and shortcomings of widely used machine learning algorithms + How to represent data processed by machine learning, including which data aspects to focus on + Advanced methods for model evaluation and parameter tuning + The concept of pipelines for chaining models and encapsulating your workflow + Methods for working with text data, including text-specific processing techniques + Suggestions for improving your machine learning and data science skills.

Introduction to Machine Learning with Python: A Guide for Data Scientists, Andreas Müller, Sarah Guido


Post a Comment

[facebook]

Contact Form

Name

Email *

Message *

Theme images by latex. Powered by Blogger.