Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video course will be your companion and ensure that you master various data mining and statistical techniques. The course starts by comparing and contrasting statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. You will then learn predictive/classification modeling, which is the most common type of data analysis project. As you move forward on this journey, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling.

In this video you'll work with categorical data to predict loan performance. Categorical, structured data often appears in spreadsheets and relational databases, common data sources in business. This technique can be used to effectively predict performance or detect potential fraud. You will also work with recurrent neural networks, which generate realistic test and placeholder data. This is useful to fill in systems with synthetic test data to simulate load and test the breadth of a working system andpredict one column from the others.

When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

Are you interested to enter into the world of data science and learn the most effective machine learning tools and techniques with Python? then you should surely go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning - the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! The resurgent interest in machine learning is due to the same factors that have made data science more popular than ever.

This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you're a practicing machine-learning engineer, a software developer, or a college student, you'll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. You'll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems.

Yes, I am a data scientist and yes, you did read the title correctly, but someone had to say it. We read so many stories about data science being thesexiest job of the 21st century and the attractive sums of money that you can make as a data scientist that it can seem like the absolute dream job. Factor in that the field contains an abundance of highly skilled people geeking out to solve complex problems (yes it's a positive thing to "geek out"), there is everything to love about the job. But the truth is that data scientists typically "spend 1–2 hours a week looking for a new job" as stated in this article by the Financial Times. Furthermore, the article also states that "Machine learning specialists topped its list of developers who said they were looking for a new job, at 14.3 per cent. Data scientists were a close second, at 13.2 per cent."

Deep learning is the next big thing. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians. This course will help you resolve problems during the execution of different tasks in deep learning, neural networks, and advanced machine learning techniques. We start with different packages in deep learning, neural networks, and structures.

TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you're a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing.

This course will cover the tools called Biteable and Lumen5 which are the world's simplest video makers offering hundreds of free animated, live action or photo scenes in lots of different styles made by top designers. I will teach you to easily add your own text, photos, colors and sound to customize your video and edit it all on a simple drag-and-drop timeline. Once, you've done that we will go through the process of choose a soundtrack from Biteable's huge, high-quality music library to match the look and feel of your video. With Biteable, you can convert any plain info graphic in fun slides that will not only explain and pass the information but also make the content more understandable. This course can change your life as a video creator.

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. Value estimation--one of the most common types of machine learning algorithms--can automatically estimate values by looking at related information.