It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

In the deep learning journey so far on this website, I've introduced dense neural networks and convolutional neural networks (CNNs) which explain how to perform classification tasks on static images. We've seen good results, especially with CNN's. However, what happens if we want to analyze dynamic data? There are ways to do some of this using CNN's, but the most popular method of performing classification and other analysis on sequences of data is recurrent neural networks. This tutorial will be a very comprehensive introduction to recurrent neural networks and a subset of such networks – long-short term memory networks (or LSTM networks). I'll also show you how to implement such networks in TensorFlow – including the data preparation step. It's going to be a long one, so settle in and enjoy these pivotal networks in deep learning – at the end of this post, you'll have a very solid understanding of recurrent neural networks and LSTMs. As always, all the code for this post can be found on this site's Github repository. Recommended online course: If you are more of a video course learner, I'd recommend this inexpensive Udemy course: Deep Learning: Recurrent Neural Networks in Python A recurrent neural network, at its most fundamental level, is simply a type of densely connected neural network (for an introduction to such networks, see my tutorial). However, the key difference to normal feed forward networks is the introduction of time – in particular, the output of the hidden layer in a recurrent neural network is fed back into itself. In the diagram above, we have a simple recurrent neural network with three input nodes. These input nodes are fed into a hidden layer, with sigmoid activations, as per any normal densely connected neural network. What happens next is what is interesting – the output of the hidden layer is then fed back into the same hidden layer.

The world is emitting data at a very high pace and everyone wants to gain insights from the huge number of data coming their way. Data mining provides a way of finding these insights and R has become the go-to-tool for it among the data analysts and data scientists. If you're looking forward to working on complex data mining projects and gaining deeper insights of data, then 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. Let's get on this data mining journey together!

R is a popular choice of tool for analysts, offering a large variety of libraries pertaining to each and every task in data analysis. With practical projects based on various real-world domains and use-cases where web analytics can be used, this video will be your companion for implementing the various web analytics techniques using the free, open source libraries provided by R. This video will start by understanding the basics of web analytics, and why it is used in businesses worldwide. This will be followed by an introduction to R and its libraries, and understanding why R is a great choice for performing real-time analysis of your data. Analyse your website's traffic and understand customers' behaviour in real time, and use this information to optimize the performance of your website.

This is an introductory course to Simple Linear Regression, one of the most basic courses in Statistics. This course is most suitable for students of professionals with basic knowledge or beginning level in Statistics and R coding. It also fits for anybody who want to explore the field of statistics using R in any discipline. We will start with an introduction section where I will explain the regression equation with a detailed example. Next, we will cover the essentials of modeling.

In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%).

About this course: In our 6 week Robotics Capstone, we will give you a chance to implement a solution for a real world problem based on the content you learnt from the courses in your robotics specialization. It will also give you a chance to use mathematical and programming methods that researchers use in robotics labs. You will choose from two tracks - In the simulation track, you will use Matlab to simulate a mobile inverted pendulum or MIP. The material required for this capstone track is based on courses in mobility, aerial robotics, and estimation. In the hardware track you will need to purchase and assemble a rover kit, a raspberry pi, a pi camera, and IMU to allow your rover to navigate autonomously through your own environment Hands-on programming experience will demonstrate that you have acquired the foundations of robot movement, planning, and perception, and that you are able to translate them to a variety of practical applications in real world problems.

Nowadays web-sites needs to handle huge amount of traffic. We can leverage that fact and capture user interactions with the application. Next, we can analyze users behavior and capture patterns on which we are able to react properly. In applications that needs to deal with huge amount of traffic it is very hard to detect anomalies. We'll learn how to apply clustering to find anomalies in web traffic.

Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role of the recurrent neural network in addressing the problem. In this post, you will discover the inject and merge architectures for the encoder-decoder recurrent neural network models on caption generation. Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model Photo by Bernard Spragg.

About this course: Control of Mobile Robots is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts - to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge we have set out to address through the different pieces in the course.