“…What you will learn Learn how to compute and visualize marketing KPIs in Python and R Master what drives successful marketing campaigns with data science Use machine learning to predict customer engagement and lifetime value Make product recommendations that customers are most likely to buy Learn how to use A/B testing for better marketing decision making Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! …”

“…What you will learn Use tf.Keras for fast prototyping, building, and training deep learning neural network models Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style transfer for image hybridization using a neural network Code a recurrent neural network in TensorFlow to perform text-style generation Who this book is for Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. …”

“…What you will learn Use TensorFlow to build RNN models Use the correct RNN architecture for a particular machine learning task Collect and clear the training data for your models Use the correct Python libraries for any task during the building phase of your model Optimize your model for higher accuracy Identify the differences between multiple models and how you can substitute them Learn the core deep learning fundamentals applicable to any machine learning model Who this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. …”

“…Who this book is for This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning wi..…”

“…What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to us..…”

“…Build effective regression models in R to extract valuable insights from real data About This Book Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Who This Book Is For This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. …”

“…What you will learn Understand the basics and importance of clustering Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages Explore dimensionality reduction and its applications Use scikit-learn (sklearn) to implement and analyze principal component analysis (PCA) on the Iris dataset Employ Keras to build autoencoder models for the CIFAR-10 dataset Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data Who this book is for This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. …”

“…Who This Book Is For This book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. …”

“…While at the University of California at Berkeley, he implemented the 4.2BSD fast filesystem and was the Research Computer Scientist at the Berkeley Computer Systems Research Group (CSRG) overseeing the development and release of 4.3BSD and 4.4BSD. …”

“…User experience and predictive device behavior in the Internet of Things Mike Kuniavsky, Principal Scientist, PARC Understand the potential of UX design for dynamic, adaptive, and predictive devices. …”

“…Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes About This Book Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin Scale up your data anlytics infrastructure with practical recipes for Scala machine learning Recipes for every stage of the data analysis process, from reading and collecting data to distributed analytics Who This Book Is For This book shows data scientists and analysts how to leverage their existing knowledge of Scala for quality and scalable data analysis. …”

“…Developers of electronic design automation (EDA) tools. Engineers, scientists and students of various disciplines using SPICE-like simulators for research and development…”

“…Audience Whether you're a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. …”

“…What you will learn Discover different ways to transform data into valuable insights Explore the different types of regression techniques Grasp the basics of classification through Naive Bayes and decision trees Use clustering to group data based on similarity measures Perform data fitting, pattern recognition, and cluster analysis Implement feature selection and extraction for dimensionality reduction Harness MATLAB tools for deep learning exploration Who this book is for This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. …”

“…Co-authored by the most productive instructional research scientist in the world, Dr. Richard E Mayer, this book distills copious e-learning research into a practical manual for improving learning through optimal design and delivery. …”

“…What you will learn Ingest data from different sources and write it to the required sinks Profile and validate data pipelines for better quality control Get up to speed with grouping, merging, and joining structured data Handle missing values and outliers in structured datasets Implement techniques to manipulate and transform time series data Apply structure to text, image, voice, and other unstructured data Who this book is for Whether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. …”

“…Geoffrey Banda is Senior Lecturer, Science Technology and Innovation Studies (STIS) Department, University of Edinburgh, UK Maureen Mackintosh is Emeritus Professor of Economics, Open University, UK Mercy Karimi Njeru is Research Scientist, Kenya Medical Research Institute (KEMRI), Kenya. …”

“…Ivan Poupyrev (Google) is an award-winning technology leader, scientist and designer working at the cutting edge of interactive technologies and textiles. …”

“…What you will learn Use cluster algorithms to identify and optimize natural groups of data Explore advanced non-linear and hierarchical clustering in action Soft label assignments for fuzzy c-means and Gaussian mixture models Detect anomalies through density estimation Perform principal component analysis using neural network models Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. …”

“…Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. …”