Tag Archives: Machine Learning

 I just realized that I am using Streamlit since almost one year now, posted about in Twitter or LinkedIn several times, but never wrote a blog post about it before. Communication in Data Science and Machine Learning is the key. Being able to showcase work in progress and share results with the business makes the difference. Verbal and non-verbal communication ...

 Faker is an Open Source Python package that generates synthetic data that could be used for many things such as populating a database, do load testing or anonymize production data for development or ML purposes. Generating fully random data isn’t a good choice: with Faker you can drive the generation process and tailor the generated data to your specific needs: ...

Machine learning is recognizing increased use across industries for various reasons. It helps to gather vast amounts of data. Especially in healthcare, Machine Learning has commenced to compelling new developments that could redefine diagnosis and treatment in the upcoming years. Machine learning can increase access to treatment in remote locations where specialist health care services are scarce. Besides, in many ...

I watched (COVID19-era version of “attended”) the latest spark Summit and in one of the keynotes Reynold Xin from Databricks, presented the following two images comparing spark usage on their platform on 2013 vs. 2020: While Databricks’ platform is, of course, not the whole spark community, I would wager that they have enough users to represent the overall trend. Incidentally, ...

Building a recommendation system with Spark is a simple task. Spark’s machine learning library already does all the hard work for us. In this study I will show you how to build a scalable application for Big Data using the following technologies: Scala LanguageSpark with Machine LearningAkka with ActorsCassandra A recommendation system is an information filtering mechanism that attempts to ...

In the 3rd part of this series I am going to complete the description started in part 2 of the data preparation process for training and evaluation purposes of a LSTM model in time series forecasting. The data set used is the same as for part 1 and part 2. Same as for all of the post of this series, ...

In the second post of this series we are going to learn how to prepare data for training and evaluation of a LSTM neural network for time series forecasting. Same as for any other post of this series I am referring to Python 3. The data set used is the same as for part 1.LSTM (Long-Short Term Memory) neural networks are a ...

During the last part of my career I had a chance to work with Data Scientists having strong skills in Python. My tech background, after a start with C/C++, is in JVM programming languages mostly (but I had to touch several others during my career), so it was a great chance for me to learn more about practical Python, at ...

Disruptive technologies like artificial intelligence, machine learning, the Internet of Things has the potential to fall into the endless pit of buzzword-vagueness. The world is changing at a breathtaking pace. Within a blink of an eye, you will have missed yet another disruption, another tech-driven innovation. But instead of running away, it’s time to embrace it! The digital revolution has ...