Key Features
<ul>
<li>Become competent at implementing regression analysis in Python</li>
<li>Solve some of the complex data science problems related to predicting outcomes</li>
<li>Get to grips with various types of regression for effective data analysis</li>
</ul>Book DescriptionRegression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.What you will learn<ul>
<li>Format a dataset for regression and evaluate its performance</li>
<li>Apply multiple linear regression to real-world problems</li>
<li>Learn to classify training points</li>
<li>Create an observation matrix, using different techniques of data analysis and cleaning</li>
<li>Apply several techniques to decrease (and eventually fix) any overfitting problem</li>
<li>Learn to scale linear models to a big dataset and deal with incremental data</li>
</ul>About the AuthorLuca Massaron is a data scientist and a marketing research director who is specialized in multivariate statistical analysis, machine learning, and customer insight with over a decade of experience in solving real-world problems and in generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about everything regarding data and its analysis and also about demonstrating the potential of datadriven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essentials.
Alberto Boschetti is a data scientist, with an expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces daily challenges that span from natural language processing (NLP) and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.Table of Contents<ol>
<li>Regression – The Workhorse of Data Science</li>
<li>Approaching Simple Linear Regression</li>
<li>Multiple Regression in Action</li>
<li>Logistic Regression</li>
<li>Data Preparation</li>
<li>Achieving Generalization</li>
<li>Online and Batch Learning</li>
<li>Advanced Regression Methods</li>
<li>Real-world Applications for Regression Models</li>
</ol>

Regression Analysis with Python: Learn the Art of Regression Analysis With Python

Computers & Technology

Massaron, Luca

Boschetti, Alberto

Programming

Packt Pub Ltd

Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsAbout This Book<ul>
<li>Design, engineer and deploy scalable machine learning solutions with the power of Python</li>
<li>Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework</li>
<li>Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale</li>
</ul>Who This Book Is ForThis book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.What You Will Learn<ul>
<li>Apply the most scalable machine learning algorithms</li>
<li>Work with modern state-of-the-art large-scale machine learning techniques</li>
<li>Increase predictive accuracy with deep learning and scalable data-handling techniques</li>
<li>Improve your work by combining the MapReduce framework with Spark</li>
<li>Build powerful ensembles at scale</li>
<li>Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine</li>
</ul>In DetailLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.
Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.Style and ApproachThis efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.
Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production.
This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

Large Scale Machine Learning with Python

Sjardin, Bastiaan

Computer Science

Packt Publishing

Key Features
<ul>
<li>Quickly get familiar with data science using Python 3.5</li>
<li>Save time (and effort) with all the essential tools explained</li>
<li>Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience</li>
</ul>Book DescriptionFully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow.
Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.What you will learn<ul>
<li>Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux</li>
<li>Get data ready for your data science project</li>
<li>Manipulate, fix, and explore data in order to solve data science problems</li>
<li>Set up an experimental pipeline to test your data science hypotheses</li>
<li>Choose the most effective and scalable learning algorithm for your data science tasks</li>
<li>Optimize your machine learning models to get the best performance</li>
<li>Explore and cluster graphs, taking advantage of interconnections and links in your data</li>
</ul>About the AuthorAlberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
Luca Massaron is a data scientist and marketing research director specializing in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience of solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about every aspect of data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, Luca believes that a lot can be achieved in data science just by doing the essentials.Table of Contents<ol>
<li>First Steps</li>
<li>Data Munging</li>
<li>The Data Pipeline</li>
<li>Machine Learning</li>
<li>Social Network Analysis</li>
<li>Visualization, Insights, and Results</li>
<li>Strengthen Your Python Foundations</li>
</ol>

Python Data Science Essentials - Second Edition: Learn the fundamentals of Data Science with Python

Databases & Big Data

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenariosKey Features<ul>
<li>Build efficient deep learning pipelines using the popular Tensorflow framework</li>
<li>Train neural networks such as ConvNets, generative models, and LSTMs</li>
<li>Includes projects related to Computer Vision, stock prediction, chatbots and more</li>
</ul>Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.
TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.What you will learn<ul>
<li>Set up the TensorFlow environment for deep learning</li>
<li>Construct your own ConvNets for effective image processing</li>
<li>Use LSTMs for image caption generation</li>
<li>Forecast stock prediction accurately with an LSTM architecture</li>
<li>Learn what semantic matching is by detecting duplicate Quora questions</li>
<li>Set up an AWS instance with TensorFlow to train GANs</li>
<li>Train and set up a chatbot to understand and interpret human input</li>
<li>Build an AI capable of playing a video game by itself –and win it!</li>
</ul>Who This Book Is ForThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.Table of Contents<ol>
<li>Recognizing traffic signs using Convnets</li>
<li>Annotating Images with Object Detection API</li>
<li>Caption generation for images</li>
<li>Building GANs for Conditional Image Creation</li>
<li>Stock Price Prediction with LSTM </li>
<li>Create &amp; Train Machine Translation Systems</li>
<li>Train and set up a Chatbot, able to discuss like a human</li>
<li>Detecting Duplicate Quora Questions</li>
<li>Building a TensorFlow Recommender Systems </li>
<li>Video Games by Reinforcement learning</li>
</ol>

TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

Thakur, Abhishek

Gain useful insights from your data using popular data science tools Key Features <ul>
<li>A one-stop guide to Python libraries such as pandas and NumPy </li> <li>Comprehensive coverage of data science operations such as data cleaning and data manipulation </li> <li>Choose scalable learning algorithms for your data science tasks </li> </ul> Book Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You'll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learn <ul>
<li>Set up your data science toolbox on Windows, Mac, and Linux </li> <li>Use the core machine learning methods offered by the scikit-learn library </li> <li>Manipulate, fix, and explore data to solve data science problems </li> <li>Learn advanced explorative and manipulative techniques to solve data operations </li> <li>Optimize your machine learning models for optimized performance </li> <li>Explore and cluster graphs, taking advantage of interconnections and links in your data</li>
</ul> Who this book is for If you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book. Table of Contents <ol>
<li>First Steps</li> <li>Data Munging</li> <li>The Data Pipeline</li> <li>Machine Learning</li> <li>Visualization, Insights, and Results</li> <li>Social Network Analysis</li> <li>Deep Learning Beyond the Basics</li> <li>Spark for Big Data</li> <li>Appendix A: Strengthen Your Python Foundations</li>
</ol>

Python Data Science Essentials