Testing and Debugging in Machine Learning
Testing and debugging in machine learning mean evaluating and improving models to make sure they work correctly. Testing checks how well the model predicts new data, while debugging finds and fixes errors in the model's implementation. These steps are crucial for reliable and effective machine learning. This course will take approximately 4 hours.
Recommendation systems suggest items to users based on their preferences, behavior, or similar users. They are used in platforms like e-commerce websites, streaming services, and social media for personalized recommendations. Course duration: 4 hours.
When it comes to framing a machine learning problem, your main goal is to define the task and understand the problem you want to solve. This includes figuring out what type of problem it is (like classification, regression, or clustering), choosing the right data to use, and deciding on the evaluation metrics to measure how well your model performs. This course should take about 1 hour to complete.
Clustering is a way to group similar data based on their patterns. It helps organize items into clusters with shared characteristics. The goal is to find the structure in data without prior knowledge of the groups. This course takes about 4 hours.