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
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.Dipanjan has been an analytics practitioner for several years, specializing in statistical, predictive, and text analytics. Having a passion for data science and education, he is a Data Science Mentor at Springboard, helping people up-skill on areas like Data Science and Machine Learning. Dipanjan has also authored several books on R, Python, Machine Learning and Analytics, including Text Analytics with Python, Apress 2016. Besides this, he occasionally reviews technical books and acts as a course beta tester for Coursera. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science and more recently, artificial intelligence and deep learning.
Raghav Bali has a master's degree (gold medalist) in InformationTechnology from International Institute of Information Technology, Bangalore. He is a Data Scientist at Intel, where he works on analytics, business intelligence, and application development to develop scalable machine learning-based solutions. He has also worked as an analyst and developer in domains such as ERP, finance, and BI with some of the leading organizations in the world.
Raghav is a technology enthusiast who loves reading and playing around with new gadgets and technologies. He has also authored several books on R, Machine Learning and Analytics. He is a shutterbug, capturing moments when he isn't busy solving problems.Tushar Sharma has a master’s degree from International Institute of Information Technology, Bangalore. He works as a Data Scientist with Intel. His work involves developing analytical solutions at scale using enormous volumes of infrastructure data. In his previous role, he has worked in the financial domain developing scalable machine learning solutions for major financial organizations. He is proficient in Python, R and Big Data frameworks like Spark and Hadoop.
Apart from work Tushar enjoys watching movies, playing badminton and is an avid reader. He has also authored a book on R and social media analytics.
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.
The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.
Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning.
At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
What You’ll LearnMaster fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks
Who This Book Is ForSoftware engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.
As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.What You'll Learn Understand machine learning algorithms using R
Who This Book is For
Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.
It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise.What You Will LearnLearn how to tap into data from diverse social media platforms using the R ecosystemUse social media data to formulate and solve real-world problemsAnalyze user social networks and communities using concepts from graph theory and network analysisLearn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channelsUnderstand the art of representing actionable insights with effective visualizationsAnalyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so onLearn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many moreIn Detail
The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.
The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.Style and approach
This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.
This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed.What You Will LearnExploit the power of Python to handle data extraction, manipulation, and exploration techniquesUse Python to visualize data spread across multiple dimensions and extract useful featuresDive deep into the world of analytics to predict situations correctlyImplement machine learning classification and regression algorithms from scratch in PythonBe amazed to see the algorithms in actionEvaluate the performance of a machine learning model and optimize itSolve interesting real-world problems using machine learning and Python as the journey unfoldsIn Detail
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.
This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.
Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.Style and approach
This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work—giving you a deep insight into the world of machine learning. With simple yet rich language—Python—you will understand and be able to implement the examples with ease.
This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.What You Will LearnExplore classification algorithms and apply them to the income bracket estimation problemUse predictive modeling and apply it to real-world problemsUnderstand how to perform market segmentation using unsupervised learningExplore data visualization techniques to interact with your data in diverse waysFind out how to build a recommendation engineUnderstand how to interact with text data and build models to analyze itWork with speech data and recognize spoken words using Hidden Markov ModelsAnalyze stock market data using Conditional Random FieldsWork with image data and build systems for image recognition and biometric face recognitionGrasp how to use deep neural networks to build an optical character recognition systemIn Detail
Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.Style and approach
You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.
If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.What You Will LearnUtilize the power of R to handle data extraction, manipulation, and exploration techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresExplore the underlying mathematical and logical concepts that drive machine learning algorithmsDive deep into the world of analytics to predict situations correctlyImplement R machine learning algorithms from scratch and be amazed to see the algorithms in actionWrite reusable code and build complete machine learning systems from the ground upSolve interesting real-world problems using machine learning and R as the journey unfoldsHarness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data scienceIn Detail
Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.
This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.
You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.
Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.Style and approach
The book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.