David Yunger is CEO of AI and software development firm Vaital. We were days away from IPO. We had raised $100 million in funding and exploded from a team of 50 in a garage to 600 in 18 months. One million technologists joined our platform. We were the next big deal.

I just won a gold medal on Talking Data competition on Kaggle, finishing 6th. My approach and solution is described here. The part that triggered most interest from readers is where I used matrix factorization techniques to generate additional features. Before that, let me briefly explain what this competition was about. To support your modeling, they have provided a generous dataset covering approximately 200 million clicks over 4 days!

Machine Learning and Deep Learning are very promising technologies. Every week comes with its new hyped successes. Yet, when it comes to applying machine learning and deep learning many people keep making the same mistakes. Here is one that is particularly troublesome: people often miss that you need to provide examples to learn from. They expect systems to learn from raw data without any supervision or feedback.

Do you have spare time on evenings and week ends? Here is a great way to use it: enter machine learning competitions. That's what I do since a year, as often as I can. The latest competition I entered, the Quora competition on Kaggle, was quite good for me as my team finished in gold, being 12th among more than 3,300 teams. Over 100 million people visit Quora every month, so it's no surprise that many people ask similarly worded questions.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data. I will use the trend search available on indeed.com.

Feature engineering and feature extraction are key, and time consuming, parts of the machine learning workflow. They are about transforming training data, augmenting it with additional features, in order to make machine learning algorithms more effective. Deep learning is changing that according to its promoters. With deep learning, one can start with raw data as features will be automatically created by the neural network when it learns. The feature engineering approach was the dominant approach till recently when deep learning techniques started demonstrating recognition performance better than the carefully crafted feature detectors.

Python is a popular language for machine learning. It is even the most popular one according to a study of mine recently published here and on KDnuggets. The above study generated quite a few reactions on social media. I just recently switched to Scala. Somewhat similar to python but with a number of advanced concepts.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data. I will use the trend search available on indeed.com.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.

A lot of state of the art machine learning algorithms are available via open source software. Many open source software are designed to be used via a command line interface. I much prefer to use Python as I can mix many packages together, and I can use a combination of Numpy, Pandas, and Scikit-Learn to orchestrate my machine learning pipelines. I am not alone, and as a result, many open source machine learning software provide a Python api. For instance Vowpal Wabbit does not support a Python API that works with Anaconda.