In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

Another essential skill in data analysis is data . Visuals are extremely important for both exploratory data analysis, as well the communication of your results. Matplotlib is the most commonly used library for this in Python. Get inspired by viewing some plots and graphs: Matplotlib Gallery Take a look at some sample code: Matplotlib Examples Review the Matplotlib chapter on DataCamp: DataCamp Python for Data Science Come up with some visualizations for your toy dataset.

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. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

In the Game of Thrones-like artificial intelligence competition between Houses Amazon, Apple, Facebook, Google, and Microsoft, the company most reticent to speak about its technology has usually been the one that ships planeloads full of stuff to consumers, hosts thousands of companies in its data centers, greenlit Catastrophe, and has a breakaway hit product that answers questions, plays music, and 4,998 or so other things. Yes, for some time, Amazon has been even more shrouded than the famously secret Apple, which opened up about its machine learning programs earlier this year. Lately, however, Amazon's head scientist and vice president of Alexa, Rohit Prasad, has been speaking up in public, making the case for his company's prowess in voice recognition and natural language understanding. Alexa, of course, is the conversational platform that supports that aforementioned hit product, Echo. On Wednesday Prasad gave an Alexa "State of the Union" address at the Amazon Web Services conference in Las Vegas, announcing an improved version of the Alexa Skills Kit, which helps developers create the equivalent of apps for the platform; a beefed-up Alexa Voice Service, which will make it easier to transform third-party devices like refrigerators and cars into Alexa bots; a partnership with Intel; and the Alexa Accelerator that, with the startup incubator Techstars, will run a 13-week program to help newcomers build Alexa skills.

Don't forget to subscribe if you find this useful! Machine Learning for Everyday Tasks – Machine learning is often thought to be too complicated for everyday development tasks. I have always felt like we can benefit from using machine learning for simple tasks that we do regularly. An Interactive Tutorial on Numerical Optimization – Numerical Optimization is one of the central techniques in Machine Learning. I thought that it might be fun to provide some interactive visualizations of how these algorithms work.

Udacity 2,479 views What is machine learning?

For classification tasks there are three widely used algorithms; the Naive Bayes, Logistic Regression / Maximum Entropy and Support Vector Machines. We have already seen how the Naive Bayes works in the context of Sentiment Analysis. Although it is more accurate than a bag-of-words model, it has the assumption of conditional independence of its features. This is a simplification which makes the NB classifier easy to implement, but it is also unrealistic in most cases and leads to a lower accuracy. A direct improvement on the N.B. classifier, is an algorithm which does not assume conditional independence but tries to estimate the weight vectors (feature values) directly.

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. IBM makes quantum computing available in the cloud 2016 Big Data 100: 20 Coolest Platform And Tools Vendors The fight against antimicrobial resistance across Europe Cool video pie chart Inside Facebook's Biggest Artificial Intelligence Project Ever How to tell two radically different stories from the same dataset Data science, no coding required: DataRobot's automated platform Google launches new machine learning platform TechCrunch Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Scienc... Forbes Beyond the hype: the hard work behind analytics success MIT Sloan Deep learning will be huge -- and here's who will dominate it Years You Have Left to Live, Probably - Nice interactive chart by FlowingData Alooma gets $11.2 million Series A to solve data science pain points AI program wrote a short novel, and almost won a literary prize How facial recognition can expose your life to strangers Data science, no coding required: DataRobot's automated platform Deep learning will be huge -- and here's who will dominate it Alooma gets $11.2 million Series A to solve data science pain points

We are approaching the end of another year, and with that comes time for reflection on how far we've come in the last 12 months and speculation about what's ahead. If we look back on the last year in the world of sales and marketing, there's one trend in particular that's sparked notable enthusiasm from B2B marketers: the emergence of Account-Based Marketing (ABM) as a category. In 2016, we saw explosive growth in ABM acceleration and adoption, with more than 70 percent of B2B companies focused on driving ABM programs, according to a recent study by SiriusDecisions. This growth has definitely impacted how marketers structure their tech stacks today. ABM has gone from a "nice to have" marketing tactic to a key solution that marketing departments are actively working to execute within their organizations.