If you need answers to one or more of these questions, you have come to the right place! Machine learning and neural networks are the hottest topic out there. Self driving cars, image recognition, ecommerce, predicting customer behavior, stock market predictions, you name it! Google, Facebook, Tesla, Amazon, Alibaba,... all great companies are working on this topic. Because of that we all should familiarize ourselves with this topic.

About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.

However I tells you all about software you should install for machine learning & neural networks. Hope that serves you well.

Data Mining and Machine Learching are a hot topics on business intelligence strategy on many companies in the world. These fields give to data scientists the opportunity to explore on a deep way the data, finding new valuable information and constructing intelligence algorithms who can "learn" since the data and make optimal decisions for classification or forecasting tasks. This course is focused on practical approach, so i'll supply you useful snippet codes and i'll teach you how to build professional desktop applications for machine learning and datamining with python language. We'll also manage real data from an example of a real trading company and presenting our results in a professional view with very illustrated graphical charts. We'll initiate at the basic level covering the main topics of Python Language and also the needing programs to develop our applications.

Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data. This tutorial will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN's and RNN's.

Learn the basics of Deep Learning with hands on exercises using the Caffe deep learning framework and the DIGITS visual interface. Build your own model and start classifying images. Artificial intelligence, machine learning and deep learning are in the news and all around us. They give us the promise of computers solving tasks that until recently were very hard for computers: speech recognition, translation, object recognition, image classification, autonomous driving cars. Caffe framework is free, open sourced, continuously improved, has good documentation and even has an entire zoo of pre trained deep neural network models for image classification and other computer vision tasks.

TensorFlow has been gaining immense popularity over the past few months, owing to its power and ease of use. This video aims to help you leverage the power of TensorFlow to perform image processing. Beginning with an introduction to image processing, the video will take you through TensorFlow's API-like graph tensor, which can be used for image classification. Starting off with basic 2D images, the video will gradually take you through recognizing more complex images, colors, shapes, and so on. Making use of the Python API, you will move on to classifying and training your model to identify more complex images such as face and expression detection, while you will also perform classification using regression.

We've designed these tutorials so that you can traverse the curriculum in more than one way. This evolving creature is a collaborative effort (see contributors tab).

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.

Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math.