Machine learning is at the core of AI, powering recommendation systems, fraud detection, and medical diagnostics. This course provides a structured, hands-on approach to understanding machine learning fundamentals. By the end, you will be able to train and evaluate machine learning models and apply them to real-world datasets.
What You’ll Learn:
- Fundamentals of Machine Learning: Supervised vs. Unsupervised Learning
- Core Algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors
- Data Preprocessing Techniques: Feature Engineering and Data Cleaning
- Evaluating Model Performance: Accuracy, Precision, Recall, and F1 Score
- Hands-on Project: Develop and test a predictive machine learning model
Certification Requirement:
- Pass the ML Fundamentals Assessment
- Submit and defend a working ML model
Average Review Score:
★★★★★
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