My journey through the ML Zoomcamp - Cohort 2025

 Module 4: Evaluation Metrics for Classification This module was about how to properly evaluate classification models and handle imbalanced datasets.  The following were covered Accuracy, precision, recall, F1-score ROC curves and AUC Cross-validation Confusion matrices Class imbalance handling

Module 3: Machine Learning for Classification In this module we create a customer churn prediction system using logistic regression and learn about feature selection.   Topics covered included: Logistic regression Feature importance and selection Categorical variable encoding Model interpretation

Module 2: Machine Learning for Regression This module introduced the concepts behind linear regression by build a car price prediction model. It involved the learning the mathematics behind learning linear regression, and writing the python functions to implement the model from first priciples. It also covered feature engineering, and regularization. Topics covered include: Linear regression (implemented from first principles) Exploratory data analysis (Visualizing the data to get a feel of how it is distributed) Feature engineering (sythesizing new new features from existing features) Regularization techniques Model validation

 As a Data Science enthusiast, I am always looking for opportunities to make myself better in the field. This quest led me to the Data Science Zoomcamp, Cohort 2025. I am looking forward to the 4 months of learning, unlearning and re-learning, and documenting my journey with key highlights and insights. The Youtube live pre-course Q&A session happened on August 19th, whose role was to set the stage for the course.  The  course officially kicked off on September 15th, and will follow the otline below: Module 1: Introduction to Machine Learning Module 2: Machine Learning for Regression Module 3: Machine Learning for Classification Module 4: Evaluation Metrics for Classification Module 5: Deploying Machine Learning Models Module 6: Decision Trees & Ensemble Learning Midterm Project Module 7: Neural Networks & Deep Learning Module 8: Serverless Deep Learning Module 9: Kubernetes & TensorFlow Serving Capstone Project