#A.I. Building machine learning projects using real-world datasets is an effective way to apply what you’ve learned. Working with real-world datasets will help you learn a great deal about cleaning and analyzing messy data, handling class imbalance...

#A.I. 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, under...

#A.I. 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, feat...

#A.I. 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 b...