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
Certificate Program
02 Credits
Course Overview
This course equips learners with practical skills in supervised and unsupervised machine learning using Python. Students gain hands-on experience in the complete ML workflow, from data preparation and feature engineering to model training, evaluation, and deployment. Emphasis is placed on using scikit-learn for efficient prototyping, testing, and validation of models, enabling learners to apply machine learning techniques to real-world problems across various domains.
Key Learning Highlights
- Build strong proficiency in supervised and unsupervised machine learning techniques.
- Work with real-world ML workflows, from data cleaning and preprocessing to model deployment.
- Use scikit-learn for model prototyping, evaluation, and optimization.
- Gain hands-on experience with Python libraries for machine learning applications.
- Apply ML concepts to solve practical problems in various domains.
Tools & Platform Used
Learning Outcome
By the end of this course, learners will be able to:
Implement supervised and unsupervised machine learning algorithms in Python.
Prepare and preprocess data for machine learning workflows.
Train, evaluate, and deploy ML models effectively using scikit-learn.
Understand and apply best practices for model validation and performance tuning.
Solve real-world problems by integrating ML techniques into practical applications.
Master the course with just 4 Modules
This course begins with an introduction to machine learning concepts and the Python ML ecosystem, covering essential libraries and tools. Learners then progress to data preprocessing and feature engineering, preparing datasets for modeling. The course covers supervised learning techniques with model training and evaluation, followed by unsupervised learning methods and model selection strategies. Finally, students apply their knowledge in a capstone project, building an end-to-end machine learning pipeline from data preparation to deployment.
Introduction to Machine Learning and Python ML Ecosystem
Overview of machine learning concepts and types (supervised, unsupervised, reinforcement)
Introduction to Python ML libraries: NumPy, pandas, Matplotlib, scikit-learn
Understanding ML workflows and real-world applications
Data Preprocessing and Feature Engineering
Techniques for data cleaning, handling missing values, and normalization
Feature extraction, transformation, and selection methods
Preparing datasets for model training and evaluation
Supervised Learning with Model Evaluation
Implementing regression and classification algorithms using scikit-learn
Model training, validation, and hyperparameter tuning
Evaluating performance with metrics like accuracy, precision, recall, and F1-score
Unsupervised Learning and Model Selection
- Clustering algorithms: K-Means, Hierarchical, DBSCAN
- Dimensionality reduction techniques: PCA, t-SNE
- Model selection, validation, and assessment for unsupervised learning tasks
Capstone Project – End-to-End ML Pipeline
Applying full ML workflow from data preprocessing to model deployment
Implementing supervised and unsupervised techniques on real datasets
Presenting results, insights, and deployment strategies
Roles
Machine Learning Engineer
Data Scientist
AI/ML Developer
Data Analyst with ML focus
AI Solutions Specialist
Predictive Analytics Engineer
