AI & Machine Learning
Machine Learning with Python
Build, train, and deploy machine learning models using Python and Scikit-Learn.
5 DaysIntermediateTensorNova Certificate of Completion
Course Curriculum
Module 1Python for Data Science
- NumPy arrays and vectorised operations
- Pandas DataFrames: loading, cleaning, EDA
- Matplotlib and Seaborn visualisations
- Jupyter Lab workflow
Module 2Supervised Learning
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines
- Gradient boosting (XGBoost, LightGBM)
Module 3Unsupervised Learning
- K-Means and DBSCAN clustering
- Principal component analysis (PCA)
- Anomaly detection
- Association rule mining
Module 4Model Evaluation & Tuning
- Train/test split and cross-validation
- Confusion matrix, ROC/AUC, RMSE
- Hyperparameter tuning with GridSearchCV
- Feature importance and selection
Module 5ML Pipelines & Deployment
- Scikit-Learn pipelines and custom transformers
- Model serialisation with Joblib/Pickle
- FastAPI REST API for model serving
- Basic MLOps concepts and model versioning
Prerequisites
- Python programming fundamentals (loops, functions, OOP)
- Basic statistics (mean, variance, distributions)
- No prior ML experience required
Who Should Attend
- Software developers transitioning to ML
- Data analysts wanting to build predictive models
- Research professionals applying ML to domain problems
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