Data Science is one of the most-sought after professions today. Universities across the world are offering courses in this discipline which stands testimony to this emerging profession. There are a very few professionals with the required skill and the demand for data scientists is racing ahead. The tutorial wil give a brief understanding about Data Science. 'Implementing Machine Learning Algorithms on Larger Data Sets with Apache Mahout' have been widely covered in our course'Data Science'.

The rapid pace of innovation and the constantly exploding collection of possibilities is a major contributor to the fun we all have in digital jobs. There is never a boring moment, there is never time when you can't do something faster or smarter. The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. They never had to worry that they have to be in a persistent forward motion… sometimes just to stay current. This reality powers my impostor syndrome, and (yet?) it is the reason that I love working in every dimension of digital. We are at an inflection point in humanity's evolution where in small and big ways, we can actually change the world. With that context, this post is all about career management in the digital space. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. I would offer that the higher-order-bits in each of the three sections will provide valuable food-for-thought for anyone in a digital role.

Having an intuition for how machine learning algorithms work -- even in the most general sense -- is becoming an important business skill. As Andrew Ng has written: "Almost all of AI's recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B)." But how does this work? As you might imagine, many exciting machine learning problems can't be reduced to a simple equation like y mx b. But at their essence, supervised machine learning algorithms are solving for complex versions of m, based on labeled values for x and y, so that they can predict future y's from future x's.

"PREDICTION IS VERY difficult, especially if it's about the future," said Physics Nobel Laureate Niels Bohr. Bohr was presumably talking about the vagaries of quantum mechanical subatomic life, but the statement holds true at other scales too. Predicting the future is tough, and any good scientist knows enough to hedge his or her bets. That's what error bars are all about. It's why science usually proceeds methodically: hypotheses are formulated, experiments conducted, observations collated, and data evaluated.

Things are about to get interesting. You've likely heard that Google's DeepMind recently beat the world's best Go player. But in far more practical and pervasive ways, artificial intelligence (AI) is creeping into every aspect of life--every screen you view, every search, every purchase, and every customer service contact. It's the confluence of several technologies--Moore's law made storage, computing, and access devices almost free. This Venn diagram illustrates how deep learning is a subset of AI and how, when combined with big data, can inform enabling technologies in many sectors.

Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle. Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures.

Do you know OpenCV, Machine Learning and Image Processing and you find it difficult to come up with cool amazing projects? Basically, he is a beginner in Python with experience in Image Processing and a little bit in machine learning. He has designed a very simple classification programs like spam detection and sentiment analysis using machine learning in Python. Using image processing he has also designed a very simple gesture recognition system. He has also designed a gesture-driven keyboard. And presently he is working on an app that he calls NFS Most Wanted 2013 Remote, that can control the cars in the game using your phone's accelerometer. He also revealed some tips that will help a lot of programmers out there, especially the newbies.

A job search is just a numbers game with plenty of conversion rates. Today, we'll look at how you can improve your rate of Applications Interviews by writing a winning data scientist resume. We've compiled our favorite tips for writing the perfect data scientist CV, and they're broken into 3 sections: Resumes are often misused as a "credential dump," a hodge-podge of skills and experiences. Instead, your resume should tell a persuasive story with YOU as the protagonist. Each section should work in harmony and each bullet point should add colorful details.

Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solutions to the GLB problem in two respects. First, unlike existing GLBs, whose per-time-step space and time complexity grow at least linearly with time $t$, we propose a new algorithm that performs online computations to enjoy a constant space and time complexity. At its heart is a novel Generalized Linear extension of the Online-to-confidence-set Conversion (GLOC method) that takes \emph{any} online learning algorithm and turns it into a GLB algorithm. As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work. Second, for the case where the number $N$ of arms is very large, we propose new algorithms in which each next arm is selected via an inner product search. Such methods can be implemented via hashing algorithms (i.e., "hash-amenable") and result in a time complexity sublinear in $N$. While a Thompson sampling extension of GLOC is hash-amenable, its regret bound for $d$-dimensional arm sets scales with $d^{3/2}$, whereas GLOC's regret bound scales with $d$. Towards closing this gap, we propose a new hash-amenable algorithm whose regret bound scales with $d^{5/4}$. Finally, we propose a fast approximate hash-key computation (inner product) with a better accuracy than the state-of-the-art, which can be of independent interest. We conclude the paper with preliminary experimental results confirming the merits of our methods.

We analyze the learning properties of the stochastic gradient method when multiple passes over the data and mini-batches are allowed. We study how regularization properties are controlled by the step-size, the number of passes and the mini-batch size. In particular, we consider the square loss and show that for a universal step-size choice, the number of passes acts as a regularization parameter, and optimal finite sample bounds can be achieved by early-stopping. Moreover, we show that larger step-sizes are allowed when considering mini-batches. Our analysis is based on a unifying approach, encompassing both batch and stochastic gradient methods as special cases. As a byproduct, we derive optimal convergence results for batch gradient methods (even in the non-attainable cases).