Machine learning (ML) is a part of artificial intelligence (AI) that teaches the computer to work and make decisions based on historical data. A ML algorithm learns from historical data to generate a predictive model used to forecast the future outcome. Advanced forms of ML models could be applied in AI applications, such as Recommender System, Text Processing and Image Recognition. To work with ML, a data scientist should have a good knowledge of mathematics and statistics, and the ability to process data and interpret the results. To process the data, you have to use specific tools or be able to program.

Data scientists are known for creating their own bubble within the 3I structure -- Implement, Integrate, and Innovate. I personally lean towards the last two Is: Integrate new technologies for constant experimentation and Innovate to attain remarkable results. I have been working with Jupyter Notebook for the last 4–5 years and I feel very comfortable working with it. On the other hand, I share a lot of work projects with my teammate Paolo, who is an expert in building KNIME workflows. You'd think this could be a problem … it's not!

Collaborative filtering (CF) based on the alternating least squares (ALS) technique is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating). The underlying assumption of the CF approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than a randomly chosen person. This algorithm gained a lot of traction in the data science community after it was used by the team winner of the Netflix Prize. The CF algorithm has also been implemented in Spark MLlib with the aim of addressing fast execution on very large datasets.

Most of the time, the raw data that we need for our data science project is not organized in a neat, well-structured, and insightful table. Rather, this is sometimes stored as text in a scanned document. Words in the document must then be extracted one by one to form a text formatted data cell. This is the task performed by Optical Character Recognition (OCR). As you read the words of this article, be it text or number, your eyes are able to process them by recognizing light and dark patterns that make up characters (e.g., letters, number, punctuation marks, etc.).

This article was published as a part of the Data Science Blogathon. In the last article A Friendly Introduction to KNIME Analytics Platform I provided a brief insight into the open-source software KNIME Analytics Platform and what it is capable of. With the help of a customer segmentation example, I showed the general functions of KNIME Analytics Platform. This article takes up a topic that was briefly mentioned at the end of the last article: Components. I'll provide an in-depth explanation of what components are, what functionalities they have, and why they are useful.

PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. Its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end machine learning pipelines will amaze you. PyCaret is an alternate low-code library that can replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient. PyCaret is simple and easy to use.

In this blog post I want to introduce some basic concepts of reinforcement learning, some important terminology, and show a simple use case where I create a game playing AI in KNIME Analytics Platform. After reading this, I hope you'll have a better understanding of the usefulness of reinforcement learning, as well as some key vocabulary to facilitate learning more. You may have heard of Reinforcement Learning (RL) being used to train robots to walk or gently pick up objects; or perhaps you may have heard of it's uses in the discovery of new chemical compounds for medical use. It's even being applied to regular vehicle and network traffics! Reinforcement learning is an area of Machine Learning and has become a broad field of study with many different algorithmic frameworks.

Data Analytics and Mining is often perceived as an extremely tricky task cut out for Data Analysts and Data Scientists having a thorough knowledge encompassing several different domains such as mathematics, statistics, computer algorithms and programming. However, there are several tools available today that make it possible for novice programmers or people with no absolutely no algorithmic or programming expertise to carry out Data Analytics and Mining. One such tool which is very powerful and provides a graphical user interface and an assembly of nodes for ETL: Extraction, Transformation, Loading, for modeling, data analysis and visualization without, or with only slight programming is the KNIME Analytics Platform. KNIME, or the Konstanz Information Miner, was developed by the University of Konstanz and is now popular with a large international community of developers. Initially KNIME was originally made for commercial use but now it is available as an open source software and has been used extensively in pharmaceutical research since 2006 and also a powerful data mining tool for the financial data sector. It is also frequently used in the Business Intelligence (BI) sector.

With rapid advancements in machine learning (ML) techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. A study conducted by PwC Global concludes that by the end of this decade, the total positive impact of artificial intelligence (AI) on the global economy could be above $15 trillion, driven mostly by enhancements in consumer products. To make that happen, however, businesses must make strategic investments in the type of technology that moves AI projects into production (productionizing) and helps customers deploy them. Unfortunately, PwC's survey reveals the percentage of executives planning to deploy AI has gone down from 20 percent a year ago to only 4 percent at the beginning of 2020. The primary reason for this decrease is the gap between the growing volume of data and data-driven modeling capabilities, and the necessary skills and toolsets.

We will provide a dataset, jump-start workflows, and final solutions for the proposed tasks, and of course data visualization and ML experts. Before the event, we will share the link to download the workshop material (jump-start workflows and instructions). This is a free event, open to everybody who is interested. Food and drinks will be provided.