Supervised and unsupervised learning end-to-end.
What You'll Learn
Master the ML workflow: data → features → model → evaluation → deployment
Implement supervised learning: regression and classification algorithms
Apply unsupervised learning: clustering and dimensionality reduction
Tune hyperparameters and prevent overfitting with cross-validation
Evaluate models with precision, recall, F1-score, and ROC-AUC
Build end-to-end ML pipelines with Scikit-learn
Deploy a simple ML model as a web API
Course Curriculum
Projects You'll Build
Spam email detectorCustomer churn predictorMovie recommendation engineCredit card fraud detection
Tools & Technologies
PythonScikit-learnXGBoostPandasMatplotlibFastAPIRenderJupyterKaggle
Prerequisites
- â–¸Python for AI & Data Science (or equivalent)
- â–¸Comfort with NumPy, Pandas, and basic statistics
Who It's For
- â–¸College students (2nd year and above)
- â–¸Working professionals entering data science
- â–¸Developers adding ML to their skill set
Enroll Now
Creates your CodeMind login automatically
Machine Learning Essentials
12 weeks
Duration
Intermediate
Level
Yes
Certificate
Online / Offline
Mode