A guide on how to handle missing data using Scikit-Learn and Pandas libraries. — Machine learning frameworks don’t work with missing data. Before you build a machine learning model, you must decide how to handle missing data. In this post, I’ll talk about how to handle missing data with Scikit-Learn and Pandas libraries in Python. Analyzes made without handling missing data may be biased…

SQL is a language that communicates with databases. Despite the emergence of new technologies, knowing SQL is still important for data analysis. You need to know SQL queries to pull data with cloud-based solutions like Amazon Redshift and Google BigQuery. A database is a place to store data in an…

Image classification with PyTorch using transfer learning. — Artificial intelligence applications such as recommendation systems, web search, language translation, and driverless cars have changed our lives. One of the hottest subfields of AI is deep learning. Deep learning is an extension of artificial neural networks. …

MLOps is a bridge between model building and exporting the model to production. In this article, I’ll talk about a roadmap for learning MLOs: Tools, books, and more. — A machine learning project that is not deployed to a production environment is a dead project. In this article, I’ll introduce the discipline of MLOps, and recommend 5 tools and 3 books for MLOps. MLOps, or Machine Learning Operations, is critical to the success of data science projects in the…

In this blog post, I’m going to talk about machine learning and show steps to a machine learning project. — Artificial intelligence is one of the most exciting technologies to emerge in recent years. Great advances such as driverless cars, voice assistants, language-to-language translation, recommendation systems have been made using ai. Machine learning is a really cool field. …

In this blog post, I’ll do a machine learning project and talk about the linear regression algorithm. For this project, I’ll use a medical cost personal dataset. — Machine Learning is a subset of Artificial Intelligence that provides a machine the ability to learn automatically from experience without being explicitly programmed. Machine learning is a great field. It has applications in many fields. …

Machine learning is a subfield of AI that aims at automatically building models using data. In this post, I’ll show the 6 steps to becoming a machine learning expert. — Machine learning finds patterns in data to make a suitable decision. Deep learning is a branch of machine learning that uses neural networks with many layers. Today, problems such as computer vision, natural language processing, and driverless cars can be solved with deep learning.

Machine learning algorithms are a huge part of machine learning. In this post, I will talk about 8 algorithms that data scientists, machine learning, and artificial intelligence engineers must know. — Artificial intelligence emerged with the question of whether machines can think like humans. Artificial intelligence is one of the hottest fields of study today. Machine learning is the science of extracting information from data and is a subfield of artificial intelligence. …

In this article, I will talk about 10 Python libraries that data scientists should know. I will also recommend 5 books for data science. — With the development of technology and smart applications, the amount of data produced has increased especially in the last 10 years. With the increase in data, the field of data science emerged. It is not enough to know programming languages ​​such as Python or R to learn data science. …