Applied Machine Learning using Python and scikit-learn
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Cyber Physical Systems
5 Level NCrF
National Credit Framework
60 Hours
Flexible Learning
Beginner Level
No prior experience required
Diploma Program
02 Credits
Course Overview
This course provides a practical introduction to machine learning, focusing on both supervised and unsupervised techniques using Python. Learners will develop end-to-end ML workflows—from data preprocessing and feature engineering to model training, evaluation, and deployment. Through hands-on exercises with the scikit-learn library, students will gain experience in building and testing machine learning models efficiently, preparing them to solve real-world data-driven problems.
Key Learning Highlights
Hands-on experience with Python for implementing machine learning algorithms.
Building complete ML workflows including data preprocessing, model training, evaluation, and deployment.
Practical use of scikit-learn for rapid prototyping and performance comparison of models.
Understanding and applying both supervised and unsupervised learning techniques.
Gaining industry-relevant problem-solving and analytical skills through real-world datasets.
Tools & Platform Used
Learning Outcome
By the end of the course, learners will be able to:
Develop supervised and unsupervised machine learning models using Python.
Apply end-to-end ML workflows from data preparation to model deployment.
Utilize scikit-learn for efficient prototyping, training, and evaluation of ML models.
Interpret model results and improve performance through feature engineering and hyperparameter tuning.
Implement ML solutions to solve real-world problems using structured datasets.
Master the course with just 5 Modules
This course offers a practical journey through the machine learning lifecycle using Python and scikit-learn. Learners start by understanding core ML concepts, types, and setting up the Python ML ecosystem on Jupyter Notebooks and Google Colab. They then master data preprocessing techniques, build and evaluate supervised models, explore unsupervised methods like clustering, and apply model selection and tuning strategies. The course concludes with a capstone project, where students design an end-to-end ML pipeline—from data preparation to model deployment and reporting.
Introduction to Machine Learning and Python ML Ecosystem
- Understand types of ML: supervised, unsupervised, and reinforcement learning
- Set up Jupyter Notebooks and Google Colab environments
- Load and explore datasets using scikit-learn utilities
Data Preprocessing and Feature Engineering
- Handle missing data and encode categorical variables
- Apply feature scaling techniques like StandardScaler and MinMaxScaler
- Build preprocessing pipelines for streamlined workflows
Supervised Learning with Model Evaluation
Implement algorithms like Linear/Logistic Regression, Decision Trees, and SVM
Evaluate models using accuracy, precision, recall, F1-score, and confusion matrix
Use ROC-AUC metrics, train_test_split, and cross-validation
Unsupervised Learning and Model Selection
Apply clustering techniques such as K-Means, DBSCAN, and Hierarchical clustering
Perform model tuning with GridSearchCV and RandomizedSearchCV
Understand cross-validation strategies and manage overfitting
Capstone Project – End-to-End ML Pipeline
- Define a real-world ML problem and source relevant data
- Preprocess data, select and tune models, and evaluate performance
- Present findings through visuals, summary reports, and code submission
Roles
- Machine Learning Engineer
- Data Scientist
- Data Analyst
- AI/ML Developer
- Business Intelligence Analyst
- Research Associate (AI/ML)
