Practical Machine Learning with Python - Sarkar, Dipanjan; Bali, Raghav; Sharma, Tushar
41,99 €
versandkostenfrei*

inkl. MwSt.
Sofort lieferbar
21 °P sammeln

    Broschiertes Buch

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive…mehr

Produktbeschreibung
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
  • Produktdetails
  • Verlag: Springer, Berlin; Apress
  • Artikelnr. des Verlages: 978-1-4842-3206-4
  • 1st ed.
  • Erscheinungstermin: Januar 2018
  • Englisch
  • Abmessung: 254mm x 177mm x 32mm
  • Gewicht: 1062g
  • ISBN-13: 9781484232064
  • ISBN-10: 1484232062
  • Artikelnr.: 49126256
Autorenporträt
Dipanjan Sarkar is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera.
Inhaltsangabe
PART I - Understanding Machine Learning Chapter 1: Machine Learning Basics Chapter Goal: This chapter familiarizes and acquaints readers with the basics of machine learning, industry standard workflows followed for machine learning processes and expands on the different types of machine learning and deep learning algorithms No of pages: 50-60 Sub -Topics 1. Brief on machine learning, definitions and concepts 2. Industry standard for data mining processes - CRISP - DM and adoption in ML 3. Brief on data processing, visualization, feature extraction\engineering concepts 4. Types of learning algorithms - supervised, unsupervised, reinforcement learning 5. Advanced models - time series, deep learning 6. Model building and validation concepts 7. Applications of machine learning Chapter 2: The Python Machine Learning Ecosystem Chapter Go al: This chapter introduces readers to the python language and the entire ecosystem built around machine learning with python tools, frameworks and libraries. Overview and code samples are given for each tool to depict its usage and effectiveness No of pages: 50 - 60 Sub - Topics 1. Brief on Python 2. Why is Python effective for machine learning and data science 3. Brief overview on the python ecosystem followed by data scientists (includes anaconda distribution) 4. Reproducible research with ipython 5. Data processing and computing with pandas, numpy, scipy 6. Statistical learning with statsmodels 7. ML frameworks - scikit-learn, pyml etc 8. NLP frameworks - nltk, pattern, spacy 9. DL frameworks - theano, tensorflow, keras PART II - The Machine Learning Pipeline Chapter 3: Processing, wrangling and visualizing data& amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;Sub - Topics: 1. Data Retrieval mechanisms (crawling, databases, APIs etc) 2. Data processing (handling various forms of data - SQL, JSON, XML, Images) 3. Data attributes and features (numeric, categorical etc) 4. Data Wrangling (cleaning, handling missing values, normalizing data) 5. Data Summarization 6. Data Visualization (bar, histogram, boxplot, line, scatter etc) Chapter 4: Feature Engineering and Selection Chapter Goal: T his chapter focuses on the next stage in the ML pipeline, feature extraction, engineering and selection. Readers will learn about both basic and advanced feature engineering methods for different data formats including numeric, text and images. We will also focus on methods for effective feature selection No of pages: 50 - 60 Sub - Topics: 1. Features - understanding your v2. Basic Feature engineering 3. Extracting features from numeric, categorical variables 4. Extracting features from date\timestamp variables 5. Extracting Basic features from textual data (bag of words) 6. Advanced Feature engineering 7. Extracting complex features from textual data (word vectorization, tfidf, topic models) 8. Extracting features from images (pixels, edge detection, shapes) 9. Time series features 10. Feature scaling and standardization 11 Feature se lection techniques 12 Using forward\backward selection techniques 13 Using machine learning models like random forests 14 Other methods Chapter 5: Building, tuning and deploying models Chapter Goal: This chapter focuses on the final stage in the ML pipeline where readers will learn how to fit and build models on data features, how to optimize and tun e models and f learn ways of deploying models to use them in real-world scenarios for predictions\insights No of pages : 50-60 Sub - Topics: 1. Fitting and building models 2. Model evaluation techniques 3. Model optimization methods like gradient descent 4. Model tuning methodologies like cross validation, grid search 5. How to save and load models 6. Deploying models in action PART III - Real-world case studies in applied machine learning Chapt er 6: Analyzing bike sharing trends Chapter Goal: This chapter will focus on a real-world case study of analyzing and predicting bike sharing trends with a focus on regression models No