Data Science for Marketing Analytics: Achieve your marketing goals with the data analytics power of Python Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.

Machine Learning, as we know it is the new buzz word in the Industry today. This is practiced in every sector of business imaginable to provide data driven solutions to complex business problems.This This is a extensive and well thought course created & designed by UNP's elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry which is summarized the below sentence: "I HAVE THE MACHINE LEARNING MODEL, IT IS WORKING AS EXPECTED!! NOW WHAT?????" This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers which eventually will expose your model as a service (API) which can be used by all who wish for it. Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it.

Practical Python and OpenCV serves as a gentle introduction to the world of computer vision and image processing. If you're new computer vision, you should go with this book. The course covers 13 modules broken out into 168 lessons. Everything covered in Practical Python and OpenCV is also covered in the Gurus course (and in more detail). Deep Learning for Computer Vision with Python is a deep dive into the world of computer vision and deep learning.

Join Professor Gary Shiffman for a lecture on how AI will impact the coming national security world, and what role humans will play on it. This lecture will provide an introduction to some of the key battlefields that will play out over AI, and how countries and policymakers can adapt to it. This event is part of FAST's 101 lecture series, providing accessible introductory lectures on key tech themes, so don't worry if you're not a tech expert. Foreign Affairs Science and Tech is a Graduate student club dedicated to hosting events and lectures about the cutting edge of international relations and tech. Professor Gary M. Shiffman is a professor int he School of Foreign Service who explores relationships between economic science and national security.

I have spent the last two years learning Reinforcement Learning. I created Marathon Environments to help explore the applicability of robotics and locomotion research to Video Games in the domain of Active Ragdoll and Virtual Agents. This tutorial provides a primer on Marathon Environments. Marathon Environments re-implements the classic set of Continuous Control benchmarks typically seen in Deep Reinforcement Learning literature as Unity environments using the ML-Agents toolkit. Marathon Environments was released alongside Unity ML- Agents v0.5 and includes four continuous control environments.

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. It is closely related to but is different from KL divergence that calculates the relative entropy between two probability distributions, whereas cross-entropy can be thought to calculate the total entropy between the distributions. Cross-entropy is also related to and often confused with logistic loss, called log loss. Although the two measures are derived from a different source, when used as loss functions for classification models, both measures calculate the same quantity and can be used interchangeably. In this tutorial, you will discover cross-entropy for machine learning.

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. It is closely related to but is different from KL divergence that calculates the relative entropy between two probability distributions, whereas cross-entropy can be thought to calculate the total entropy between the distributions. Cross-entropy is also related to and often confused with logistic loss, called log loss. Although the two measures are derived from a different source, when used as loss functions for classification models, both measures calculate the same quantity and can be used interchangeably. In this tutorial, you will discover cross-entropy for machine learning.

In this tutorial, attendees will learn how to use Ray to scale their new and existing Python code. It will cover the Ray system architecture, example applications, GPU support, and best practices. It will also include material for more comprehensive exercises. Attendees will also receive a tutorial on Modin, and how Pandas workflows can be scaled by changing a single line of code.

Based on Apache OpenWhisk, IBM Cloud Functions is a Functions as a Service (FaaS) platform that makes it easy to build and deploy serverless applications. In this tutorial, you'll build a serverless application using IBM Cloud Functions that monitors the content of a Cloud Object Storage bucket and analyzes the content of images that are uploaded to the bucket by a human or an automated process. For illustrative purposes, analysis is performed by a deep learning microservice from the Model Asset eXchange and analysis results are stored as JSON files in the same bucket. You can easily adapt the outlined approach to take advantage of hosted cognitive services, such as those provided by IBM Watson, and to store results in a NoSQL datastore like Cloudant or a relational database. By completing this introductory tutorial, you learn how to monitor a Cloud Object Storage bucket for changes (new objects, updated objects, or deleted objects) using Cloud Functions and how to use deep learning microservices from the Model Asset eXchange to automatically analyze those objects in near real time.

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There are many techniques for solving density estimation, although a common framework used throughout the field of machine learning is maximum likelihood estimation. Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters. This flexible probabilistic framework also provides the foundation for many machine learning algorithms, including important methods such as linear regression and logistic regression for predicting numeric values and class labels respectively, but also more generally for deep learning artificial neural networks.