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 […]