There is no shortage of text data available today. Vast amounts of text are created each and every day, with this data ranging from fully structured to semi-structured to fully unstructured. What can we do with this text? Well, quite a bit, actually; depending on exactly what your objectives are, there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. Let's start with some definitions.

Ankit currently works as a Senior Research Scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of Deep Learning methods to a variety of Uber's problems ranging from forecasting, food delivery to self driving cars. Previously, he has worked in variety of data science roles at Bank of America, Facebook and other startups. Additionally, he has been a featured speaker in many of the top AI conferences and universities across US including UC Berkeley, OReilly AI conference etc. He completed his MS from UC Berkeley and BS from IIT Bombay (India).

Create better design patterns and avoid duplicate or insecure code with Data Science Frameworks. The swiftly changing global marketplace requires companies to take a more sophisticated approach to market dominance. Innovate companies now use data science to attract new clients, recommend products, increase sales, and improve customer satisfaction, ultimately helping them gain a competitive advantage. Data Science is simply the study of data. It leverages domain expertise from mathematics, statistics, and programming to extract, analyze, visualize, and manage data to find unseen patterns, create insights and make powerful data-driven decisions.

Pandas is an amazing library in the Python ecosystem for data analytics and machine learning. They form the perfect bridge between the data world, where Excel/CSV files and SQL tables live, and the modeling world where Scikit-learn or TensorFlow perform their magic. A data science flow is most often a sequence of steps -- datasets must be cleaned, scaled, and validated before they can be ready to be used by that powerful machine learning algorithm. These tasks can, of course, be done with many single-step functions/methods that are offered by packages like Pandas but a more elegant way is to use a pipeline. In almost all cases, a pipeline reduces the chance of error and saves time by automating repetitive tasks.

GeoSoftware, part of CGG's Geoscience division, has announced that machine learning technology in Python ecosystems will be available in …

It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat. There's a lot of debate on the'best language for data science' (in fact, here's a take on why data scientists should learn Swift).

Already attracting considerable industry interest in GeoSoftware's PowerLog petrophysical software, Python ecosystems in HampsonRussell and Jason will let experts and data scientists completely customize machine learning and reservoir characterization workflows by using extensively available Python machine learning libraries and also their own proprietary code. Python ecosystems allow users to efficiently research and test various state-of-the-art machine learning workflows for proof-of-concept or commercial projects. G&G experts and data scientists can use Ecosystem workflows pre-built by CGG or they can build their own new reservoir characterization workflows using the latest open source machine learning packages, such as Google's TensorFlow. HampsonRussell and Jason users, even those with limited expertise in machine learning or Python scripting, will now benefit from complete control over input data and analysis output. With Python ecosystems, users can process data with pre-built or client-proprietary Python scripts or Jupyter notebooks, and store input and output data in either a HampsonRussell or Jason project database or a shared directory.

Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. I would say that it is "identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data" in the context of "mapping data from one'raw' form into another..." all the way up to "training a statistical model" which I like to think of data preparation as encompassing, or "everything from data sourcing right up to, but not including, model building."

It's never been easier to get started with machine learning. In addition to structured massive open online courses (MOOCs), there are a huge number of incredible, free resources available around the web. Here are a few that have helped me. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat.

It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat. While the debate rage, grab a coffee and read this insightful article to get an idea and see your choices.