We present HardML, a benchmark designed to evaluate the knowledge and reasoning abilities in the fields of data science and machine learning. HardML comprises a diverse set of 100 challenging multiple-choice questions, handcrafted over a period of 6 months, covering the most popular and modern branches of data science and machine learning. These questions are challenging even for a typical Senior Machine Learning Engineer to answer correctly. To minimize the risk of data contamination, HardML uses mostly original content devised by the author. Current state-of-the-art AI models achieve a 30% error rate on this benchmark, which is about 3 times larger than the one achieved on the equivalent, well-known MMLU-ML. While HardML is limited in scope and not aiming to push the frontier--primarily due to its multiple-choice nature--it serves as a rigorous and modern testbed to quantify and track the progress of top AI. While plenty benchmarks and experimentation in LLM evaluation exist in other STEM fields like mathematics, physics and chemistry, the sub-fields of data science and machine learning remain fairly underexplored.

Too Long; Didn't Read Pandas is a powerful, open-source library that provides data manipulation and analysis tools for Python. NumPy is used extensively in data science, machine learning, and scientific computing for linear algebra, Fourier analysis, and more. Matplotlib is a popular Python library for creating static, interactive, and animated visualizations.

Welcome to the world of data science and machine learning! These exciting fields are transforming industries around the globe, and there’s never been a better time to dive in and start learning. But…

If you're interested in pursuing a career in data science or machine learning, then learning Python is a great place to start. Python has become the go-to language for data analysis, visualization, and machine learning, and for good reason: it's user-friendly, versatile, and has a vast ecosystem of libraries and tools that make it easy to work with data. In recent years, the demand for data scientists and machine learning engineers has skyrocketed, with companies in virtually every industry looking to harness the power of data to drive their business decisions. And as more and more companies adopt data-driven strategies, the demand for professionals with expertise in Python for data science and machine learning is only going to continue to grow. One of the biggest advantages of Python is its ease of use.

We are on the brink of a massive technological revolution as we slowly move from the water and steam-powered first industrial revolution to the artificial intelligence-powered fourth industrial revolution. The theories backing data science and machine learning have existed for hundreds of years. There used to be times when proto-computers would take almost forever to compute a billion calculations. No one dared think of artificial intelligence or related technology. All thanks to machine learning and data science, we can now calculate data at a capacity of 5 billion calculations per second.

Digital transformation has become the new mantra for companies to thrive in the digital age. Data science and machine learning are two major assets in the digital transformation era. Digital transformation has become a necessity for businesses. It is the way forward for all businesses, regardless of size and scope. However, it should be more than simply digitizing your processes.

In data science and machine learning, simplicity is an important concept that can have significant impact on model characteristics such as performance and interpretability. Over-engineered solutions tend to adversely affect these characteristics by increasing the likelihood of overfitting, decreasing computational efficiency, and lowering the transparency of the model's output. The latter is particularly important for areas that require a certain degree of interpretability, such as medicine and healthcare, finance, or law. The inability to interpret and trust a model's decision -- and to ensure that this decision is fair and unbiased -- can have serious consequences for individuals whose fate depends on it. This article aims to highlight the importance of giving precedence to simplicity when it comes to implementing a data science or machine learning solution.

This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning! Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning. Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning.

Your new company For our Client, new IT Global Hub with location in Katowice, we are currently looking for Machine Learning Engineer. Your new role In this role you will work in a team of data scientists and data engineers on bringing Advanced Analytics models You will develop optimized, scalable, and maintainable Python code for preparing, delivering, and deploying ML models as well as organizing large amount of data You will provide advice to and share your knowledge with data analysts in different business units and in our community of data enthusiasts You will use state-of-the-art cloud technology and continuously extend your knowledge and skills What you'll need to succeed You have at least 3 years practical experience in software engineering You have a proven track record in designing software architecture and developing high quality code and can develop CI/CD and ML pipelines You have expertise in OOP concept and at least one relevant programming language (Python, Java, Scala) Your technological toolbox includes GitHub, CI/CD with GitHub Actions, MLflow, Kubernetes, Jira and Confluence You have an understanding of Data Science and Machine Learning and experiences with MLOps concepts Familiarity with big data technologies such as Apache Spark is a big plus Ideally you are familiar with cloud services and Data Science related components, preferably in MS Azure You bring ability to work in a team and sharing knowledge with team members, combined with a high degree of curiosity, initiative and the motivation to work in an agile and interdisciplinary environment What you'll get in return The company offers unique opportunity of professional development, stable work position in recognized company, additional benefits: private medical care, multi-sport card. The company is located in the center of Katowice's city. What you need to do now If you're interested in this role, click'apply now' to forward an up-to-date copy of your CV, or call us now. Mandatory legal footer to be added at the bottom of job description Hays Poland sp.

Today, We'll look after something very big that you might have never seen or rarely seen on the web. We have researched for more than 35 days to find out all the cheatsheets on machine learning, deep learning, data mining, neural networks, big data, artificial intelligence, python, tensorflow, scikit-learn, etc from all over the web. You can also download the pdf version of this cheat sheets (links are already provided below every image). Note: The list is long. So, If you are in hurry, Please check out all the cheat sheets directly on Table of Contents.