For resources, the single best thing you can do is find people who can challenge you and make you think. These can be collaborators that you work with in "real life," or folks online (say, for example, contributing to open source projects). I've also found that the projects that turn out the best for me are the ones that I find most interesting or exciting, so I've grown to put a lot of effort into reading about many different things so I can find out what seems most cool or fun and then go after that--at first it felt a little backward, like instead I should be reading up to find out what I "should" be excited about and then letting that guide my choices, but I've found that thinking about it instead from the perspective of "what makes me excited, and let's think of a way to apply machine learning or data science to that" is way more fun for me. That's not really a resource, sorry, but I think it's important. For resources, I love online courses (like Udacity of course, but there are lots of good ones out there), podcasts (I have to say that, since I host one as a side project–Linear Digressions), and there are some excellent blogs out there too.

Always wanted to compete in a Kaggle competition, but not sure where to get started? Together with the team at Kaggle, we have developed a free interactive Machine Learning tutorial in Python that can be used in your Kaggle competitions! Step by step, through fun coding challenges, the tutorial will teach you how to predict survival rate for Kaggle's Titanic competition using Python and Machine Learning. DataCamp's interactive UI makes it easy to follow along from start to random forest! In this Machine Learning tutorial, you will gradually learn how basic machine learning techniques can help you to make better predictions.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

Machine Learning for Data Science and Analytics is a free, self-paced online course conducted by the Columbia University. This course helps you learn the principles of machine learning and the importance of algorithms. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

Everyone knows about the giant skills gap that is haunting the IT sector worldwide. According to IoT company PTC, it is estimated that in the next ten years more than two million IT and communication jobs will be unfulfilled. To address this, several universities have come up with degrees that address the different skills needed in the IoT market, including user interfaces, networks, artificial intelligence, networking, and others. CBR lists ten courses being taught in the UK institutions. Offering both a full time or part time (12 and 24 months respectively) course, University of London's Royal Holloway has built a degree based on computer science, technology and engineering.

This article is an effort to make you into a "semi-expert" in artificial intelligence, cognitive computing, deep learning and neural networks from scratch. Here I will share a few cool learning resources for these topics. These resources include documentaries, TED talks, online lecture videos, and books. There are several videos and online books included in this post to help you learn these concepts. These resources vary from introductory to advanced learning.

Coursera Statistics, Making Sense of Data: A applied Statistics course that teaches the complete pipeline of statistical analysis MIT: Statistical Thinking and Data Analysis: Introduction to probability, sampling, regression, common distributions, and inference. While R is the de facto standard for performing statistical analysis, it has quite a high learning curve and there are other areas of data science for which it is not well suited. To avoid learning a new language for a specific problem domain, we recommend trying to perform the exercises of these courses with Python and its numerous statistical libraries. You will find that much of the functionality of R can be replicated with NumPy, @SciPy, @Matplotlib, and @Python Data Analysis Library Books Well-written books can be a great reference (and supplement) to these courses, and also provide a more independent learning experience. These may be useful if you already have some knowledge of the subject or just need to fill in some gaps in your understanding: O'Reilly Think Stats: An Introduction to Probability and Statistics for Python programmers Introduction to Probability: Textbook for Berkeley's Stats 134 class, an introductory treatment of probability with complementary exercises.

First of all let me tell you what is Open CV and what are the things that we can do using OpenCV. OpenCV is a open source C library for digital image processing and computer vision, which can be used to create real time face recognisation and using it with embedded robotics and micro controllers for purpose like differentiating a specific color from an image having various colors. Solution to all this we will cover in this course. "Few years back, I started learning programming and spent couple of months just to learn the basics. Then, for again a couple of months I spent my time learning advance of Open CV. Being in the same field for almost one year, I decided to start my own project. But I keep on stuck at various steps of my project as many of concepts were not cleared. I was not able to develop a simple software from the knowledge I gained. I was depressed and thinking to leave the programming. Then one day, I decided to give it one more try. I wrote down all the parts of my programming knowledge where I had weak concepts. I started visiting forums and posting my questions to sharpen my skills and doubt clearance. And again tried to create that project with fewer difficulties. I repeated the same method again and dig a lot. Now I got success, I am a professional programmer in C and OpenCV and now working with two companies."

This is a compilation has everything you need to jumpstart your skills in the core tasks of data transformation, modeling, and visualization. MODELING Below is a list of popular analysis from Rexer's 2013 survey. The table is biased towards customer transaction, text, and social media data. CRAN has pages dedicated to each typical task of statistical computing http://cran.r-project.org/web/views/ Python has several packages tailored for statistical analysis including Pandas, Orange, PyBrain and Scikit-learn TRANSFORMATION OpenRefine is designed to help journalists and other non technical people organize incomplete data from different sources.