Have you been using your loss function for evaluating your machine learning system's performance? That's a mistake, but don't worry, you're not alone. It's a widespread misunderstanding that may have something to do with software defaults, college course format, and decision-maker absenteeism in AI. In this article, I'll explain why you need two separate model scoring functions for evaluation and optimization… and possibly a third one for statistical testing. Throughout data science, you'll see scoring functions (like the MSE, for example) being used for three main purposes: These three are subtly -- but importantly -- different from one another, so let's take a deeper look at what makes a function "good" for each purpose.

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It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine

Data analysing, irrespective of its form, can be extremely chaotic and challenging. This is where feature engineering steps in. A method to ease data analysis, feature engineering simplifies data reading for machine learning models. A feature or variable is nothing but the numerical representation of all kinds of data– structured and unstructured. Feature engineering is a vital part of the process of predictive modelling.

Andrew Ng's DeepLearning.AI, in partnership with Stanford Online, recently announced a new Machine Learning Specialisation course on Coursera. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The 3-course program is a new version of Ng's pioneering machine learning course, taken by over 4.8 million learners since 2012. The program provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation. The new Machine Learning Specialization by @DeepLearningAI_ & @StanfordOnline is now available on @Coursera!

Artificial Intelligence (AI) is rapidly changing the way we live, work and learn. If you're looking into ways how you can pursue a career in this booming field, then consider our popular MSc in AI degree programme. Through this 4-semester, part-time programme of studies you will learn the skills you need as it consists of both a taught and a research component. Lectures for the taught component are held after 17:00 to allow people that are already working in the IT industry (and not only) to follow the MSc. The course content aims to further improve your knowledge and expertise in AI.

Data Science is a bona-fide field conjoining domain expertise, programming skills, and knowledge of mathematics and statistics to extricate useful insights from data. Well, there's no doubt that this specific technology has grabbed a lot of attention, and if you still want to know What Is Data Science? Yes, by enrolling in its professional course, you will get proper in-depth information concerning its section. You will learn about deep neural networks, execute linear and logistic regressions in Python, imply your skills to real-life business cases, etc.

Looking for a faster way to understand data issues and patterns before diving into the fun part of training your ML model? Wanna learn how to train better ML models, by finding and fixing issues in your data? You've come to the right place. In this article, you will learn how to do data exploration at the speed of light. Let's go through a hands-on example and code you can find in this GitHub repository.

We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.

Convert a molecule from the SMILES string to an rdkit object 3.2.2. Working with the rdkit object 3.2.3. Convert list of molecules to rdkit object 3.3.