About this course: This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. This is the second course of the Deep Learning Specialization.

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.

Comprehensive Linear Modeling with R provides a wide overview of numerous contemporary linear and non-linear modeling approaches for the analysis of research data. These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. R Commander is a popular GUI-based "front-end" to the broad range of embedded statistical functionality in R software. R Commander is an'SPSS-like' GUI that enables the implementation of a large variety of statistical and graphical techniques using both menus and scripts. Please note that the R Commander GUI is written in the RGtk2 R-specific visual language (based on GTK) which is known to have problems running on a Mac computer.

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.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.

Get your team access to Udemy's top 2,000 courses anytime, anywhere. You'll start with the Neural Networks Review: You'll start your Convolutional Neural Networks endeavor by reviewing their history and motivation: You'll continue your Convolutional Neural Networks endeavor by going into all required concepts: Before jumping into code, you'll see some Convolutional Neural Networks action: Now it's time for you to code your own Convolutional Neural Networks app with your own images: Lastly, you can post questions or doubts, and I'll answer to you personally. Want to know how to navigate this course? For easier and prettier coding, install this Python IDE. You can work faster in PyCharm using these hotkeys, I'll use them in the course too This is the best stack for CNNs!

Driven by machine learning, the explosion of data has many companies feeling like they are being left behind. How can businesses derive value from these new technological developments? This course will address this issue and will help you understand what exactly machine learning and predictive analytics are, what are its limits and its potential risks, and why it may benefit your organization. Using real world case studies and many other examples of current and potential future industry usage, this course will help you better understand why many corporations are adopting, or should be adopting machine learning to better enable their future. Along the way you will learn the types of problems machine learning can solve, be conversant about the class of algorithms one can use, and the process for creating a successful project that incorporates machine learning.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

Bayesian Computational Analyses with R is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences about the likely parameters of the population. In contrast, the Bayesian approach uses both likelihood functions and a sample of observed data (the'prior') to estimate the most likely values and distributions for the estimated population parameters (the'posterior'). The course is useful to anyone who wishes to learn about Bayesian concepts and is suited to both novice and intermediate Bayesian students and Bayesian practitioners. It is both a practical, "hands-on" course with many examples using R scripts and software, and is conceptual, as the course explains the Bayesian concepts. All materials, software, R scripts, slides, exercises and solutions are included with the course materials.