Introduction Here’s a something that new machine learning practitioners figure out almost immediately: not all datasets are created equal. It may now seem obvious to you, but had you considered this before undertaking machine learning projects on a real world dataset? As an example of a single class vastly outnumbering the rest, take for instance […]
Introduction Choosing the right machine learning model for your data is of major importance in any data science project. The model you select will have a significant impact on the insights you derive from your data, and ultimately determine the usefulness of a project. In this article, we aim to provide practical tips to help […]
If you are a machine learning student, researcher, or practitioner, it is crucial for your career growth to have a deep understanding of how each algorithm works and the various techniques to enhance model performance. Nowadays, many individuals tend to focus solely on the code, data, and pre-trained models, often without fully comprehending the machine […]
As a data professional, you should also know how to build predictive models with machine learning to solve business problems. And if you’re interested in machine learning, you’re probably also looking for the best resources to get going. Well, you can always choose a self-paced online course that best aligns with your learning preferences. But […]
If you’re looking to make a career in data science, you probably know that machine learning is one of the most in-demand skills. Whether you are a beginner looking to break into the field or an experienced professional aiming to level up your expertise, selecting the right machine learning course is super important. So how […]
Reinforcement learning (RL) is a subfield of machine learning where an agent learns to make decisions by interacting with its environment rather than relying solely on pre-existing data. It is an area that blends trial-and-error learning with feedback from actions to improve future performance. In this blog, we will explore 5 free courses that I […]
Deep learning models achieve state-of-the-art performance in several computer vision and natural language processing tasks. If you want to become proficient in deep learning, you should first understand how neural networks work and then proceed to explore the different types and neural network architectures for specific tasks. To help you get started, we’ve compiled a […]
After completing a course and mastering the essentials of machine learning, it is time to start building machine learning models using real-world datasets. While accessing most real-world datasets can be expensive, platforms like Kaggle offer access to the latest datasets for free on a wide variety of machine learning tasks. In this blog, we will […]
Collaborating on a machine learning project is a bit different from collaborating on a traditional software project. In a machine learning project, engineers are working with data, models, and source code. Additionally, they are also sharing features, model experiment results, and pipelines. You can’t just use any code-sharing platform for a machine learning project; you […]
A loss function in machine learning is a mathematical formula that calculates the difference between the predicted output and the actual output of the model. The loss function is then used to slightly change the model weights and then check whether it has improved the model’s performance. The goal of machine learning algorithms is to […]
