Image by Author | Midjourney Data cleaning: whether you love it or hate it, you likely spend a lot of time doing it. It’s what we signed up for. There’s no understanding, analyzing, or modeling data without first cleaning it. Making sure we have reusable tools handy for data cleaning is essential. To that end, here are 5 DIY functions to give you a some examples and starting points for building up your own data cleaning tool chest. The functions […]

Image by Editor | Midjourney & Canva If you’re new to machine learning, understanding basic terms is crucial. Knowing key terms can help you understand the basics better. Here are 7 essential terms every beginner should know. These terms will give you a solid foundation to build your machine learning knowledge. 1. Algorithm An algorithm is a set of rules a computer uses to solve a problem. It finds patterns in data and makes predictions. There are several types of […]

Image by Editor | Midjourney Large Language Models (LLMs) are a hot topic right now, and everyone is getting involved in this new trend. Companies are searching for LLM engineers who can develop and implement AI solutions to optimize their workflow and reduce costs through automation, customer service, recommendations, issue resolution, and debugging. Instead of worrying that AI will take your job, why not upskill and join the race? In this blog, we will provide a review of free courses, […]

Image by Author | Created on Canva Machine learning presents transformative opportunities for businesses and organizations across various industries. From improving customer experiences to optimizing operations and driving innovation, the applications of machine learning are vast. However, adopting machine learning solutions is not without challenges. These challenges span across data quality, technical complexities, infrastructure requirements, and cost constraints amongst others. Understanding these challenges is important to come up with effective strategies to adopt ML solutions. Challenges in ML Adoption | […]

Image by Editor | Midjourney In machine learning projects, achieving optimal model performance requires paying attention to various steps in the training process. But before focusing on the technical aspects of model training, it is important to define the problem, understand the context, and analyze the dataset in detail. Once you have a solid grasp of the problem and data, you can proceed to implement strategies that’ll help you build robust and efficient models. Here, we outline five actionable tips […]

Image by Author | Midjourney Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network. Contrary to the traditional machine learning model, deep learning can automatically find feature representations from data. That’s why many domains, including computer vision, speech recognition, text generation, and many more, use deep learning as their technology basis. With […]

Image by Author Feature engineering is an important step in the machine learning pipeline. It is the process of transforming data in its native format into meaningful features to help the machine learning model learn better from the data. If done right, feature engineering can significantly enhance the performance of machine learning algorithms. Beyond the basics of understanding your data and preprocessing, effective feature engineering involves creating interaction terms, generating indicator variables, and binning features into buckets. These techniques help […]

Image by Author Using machine learning to solve real-world problems is exciting. But most eager beginners jump straight to model building—overlooking the fundamentals—resulting in models that aren’t very helpful. From understanding the data to choosing the best machine learning model for the problem, there are some common mistakes that beginners often tend to make. But before we go over them, you should understand the problem—it is step 0 if you will—you are trying to solve. Ask yourself enough questions to […]

Image by Author | Midjourney & Canva 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 new practitioners make informed decisions when choosing machine learning models. 1. Understand Your Data Understanding the type and […]

Image by Author 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 some rare disease, which only 1% of the population has. Would a predictive model that only ever predicts “no disease” still be […]