What You’ll Learn: ML Fundamentals
You’ll go from basic Python knowledge to confidently building and evaluating supervised machine learning models using industry-standard tools.
Regression Models
Learn and apply linear regression, polynomial regression, and other techniques for predicting continuous values.
Classification Models
Build models to predict discrete categories using logistic regression, decision trees, and random forests.
Model Evaluation
Use metrics like accuracy, precision, recall, and cross-validation to assess model performance.
Model Deployment
Learn to serialize models using joblib and serve predictions via a simple API.
Who Is This Course For?
Perfect for beginners with basic Python skills who want to enter the field of machine learning.
- Students starting their ML journey
- Developers wanting to add ML capabilities
- Professionals seeking to understand ML concepts
Hands-On Projects
House Price Prediction
Build a regression model to predict house prices based on features like size, location, and amenities.
Customer Churn Classification
Create a classification model to predict whether a customer is likely to churn.
Capstone
End-to-End ML Pipeline
Integrate data preprocessing, model training, evaluation, and deployment into a single project.
8-Week Supervised ML Syllabus
~80 hours total • Lifetime LMS access • 1:1 mentor support
Weeks 1–2: Python & Data Prep
- Review Python basics and data structures
- Introduction to NumPy and Pandas
- Data exploration and visualization
- Data cleaning and preprocessing techniques
Weeks 3–4: Linear & Logistic Regression
- Concepts of supervised learning
- Linear regression (simple and multiple)
- Logistic regression for classification
- Model training and prediction
Weeks 5–6: Tree-Based Models
- Decision trees: concepts and implementation
- Random forests and ensemble methods
- Feature importance and selection
- Overfitting and regularization
Weeks 7–8: Model Evaluation & Deployment
- Evaluation metrics (accuracy, precision, recall, F1)
- Confusion matrix and ROC curves
- Cross-validation techniques
- Model serialization and deployment overview
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
Yes, a basic understanding of Python (variables, functions, loops, data structures like lists and dicts) is required. Familiarity with NumPy and Pandas is helpful but not mandatory.
Yes! You will complete 3 end-to-end projects: a regression model, a classification model, and a final capstone project integrating both concepts.
