Python is a dynamic programming language used in a wide range of domains by programmers who find it simple yet powerful. In today's world, everyone wants to gain insights from the deluge of data coming their way. Data mining provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. In this course, you will discover the key concepts of data mining and learn how to apply different data mining techniques to find the valuable insights hidden in real-world data.

Do enterprises need a chief AI officer (CAIO) to shepherd their forward progress in a world of increasing automation? Sandy Carter, tech veteran and author of Extreme Innovation, said that "new roles like the CAIO will become essential for leveraging the big data coming into companies from all angles." Andrew Ng, Stanford computer science professor and cofounder of online learning provider Coursera, has also been a proponent of assigning an executive to transform data into value by making sure AI is applied across all data silos. But with the role still nascent, many experts continue to debate this topic. A quick public search on LinkedIn shows that only a dozen professionals currently hold the title.

Learn how to engineer features and build more powerful machine learning models. This is the most comprehensive, yet easy to follow, course for feature engineering available online. Throughout this course you will learn a variety of techniques used worldwide for data cleaning and feature transformation, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, a variety of techniques that you can apply to capture as much insight as you possibly can with the features of your data set. The course starts describing the most simple and widely used methods for feature engineering, and then describes more advanced and innovative techniques that automatically capture insight from your variables.

About this course: In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications Course objectives This course teaches participants the following skills: Use best practices for application development. Choose the appropriate data storage option for application data. Develop loosely coupled application components or microservices. Debug, trace, and monitor applications.

Get your team access to Udemy's top 2,000 courses anytime, anywhere. This course has two parts. Part one refers to Dummy Variable Regression and part two refers to conjoint analysis. Let me give you details of what you are going to get in each part. How to know, what kind of situation you have.

In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.

About this course: If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales' forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.

Organizations are teams of teams. By definition, a manager gets work done not only through one's own resources and efforts, but also through others. In other words, you are required to work effectively with people outside your team. These are individuals and groups within the organization and also outside. You have to influence people at different levels and functions, build collaborative relationships wherever possible, negotiate wisely, handle difficult conversations and make decisions in the face of uncertainty and complexity.

R is a data analysis tool, graphical environment, and programming language. Without any prior experience in programming or statistical software, this video tutorial will help you quickly become a knowledgeable user of R. Now is the time to take control of your data and start producing superior statistical analysis with R. In this video tutorial, you will start with a quick refresher on programming in R. You will learn to set up your R development environment, as well as work on a few simple R programs. After that you will dive right into working with different types of data structures in R, such as vectors, lists, matrices, etc. You will explore how to import and export data for your data analysis project, and also connect to databases such as PostgreSQL.

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more. In this course, you'll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples.