TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models..

TensorFlow is an open source software library for Machine Intelligence. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through video on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.This guide covers important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production.

Pure excellence from the presenter!!!! Great content!!! Buy this course, you won't regret it. Almost perfect, I feel like there can be more to the course but it is short and sweet. Welcome to Building Deep Neural Networks in Keras Master Class. In this course, we are going to build an then tune Keras models. The area of study which involves extracting knowledge from data is called as Data Science and people practicing in this field are called as Data Scientists.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. Learning deep learning regression is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for algorithm learning to achieve greater effectiveness. This practical course contains 33 lectures and 4 hours of content.

Have an app idea that requires machine learning? Then this course is for you! Join me as we dive into Apple's latest iOS 11 API - CoreML - a native iOS framework built with Swift. "I am about a third through this course and I have learned so much. This course is worth way more than what it cost but I'm thankful prices are low or I might have passed it up in the first place not knowing what I would get. I have used a couple Udemy courses and countless youtube tutorials. This is the best course I've ever took." - Jeffrey Nelson "The course offers interesting concepts coupled with a teacher that explains things clearly. You get to make a bunch of interesting apps and expand your skills. "Clear tutorials, the lecturer explains everything well.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today Let's parse that.

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well.

Get started on one of the cornerstone skills for a data scientist - Catch the full course at Udemy - https://www.udemy.com/tensorflow-gett... Tensorflow. This webinar series will lay a solid foundation to tensorflow. Please subscribe to keep getting these awesome videos!

Big Data is the future, and it's right here, right now! There's no doubt about it that Big Data is a powerful discovery tool, but all too often when you analyse a lot of data, you end up with a lot of results - too many, in fact, to be able to hold them all in your head simultaneously. So I'll amend my earlier statement: Data Visualisation is the future, and it's right here, right now! Apparently, visuals are processed 60,000 times faster in the brain than text, and are more easily committed to long-term memory. Visuals also make it easier to tell stories with data. Hey - I think I've heard that before somewhere...(see website footer for a clue!). Most of all though - visuals can help to simplify complex information.

Stepwise regression analysis, a technique that helps you select the best subset of predictors for a regression analysis, when you have a big number of predictors. This way you can create regression models that are both parsimonious and effective. After finishing this course, you will be able to fit any nonlinear regression model using SPSS. K nearest neighbor, a very popular predictive technique used mostly for classification purposes. So you will learn how to predict the values of a categorical variable with this method.