Applied Machine Learning using Python and scikit-learn
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
5 Level NCrF
National Credit Framework
60 Hours
Flexible Learning
Beginner Level
No prior experience required
Micro Credit Course
02 Credit
Course Overview
This course offers a practical introduction to Applied Machine Learning using Python and the scikit-learn library. Learners will work on real-world datasets and build complete end-to-end ML pipelines involving data preprocessing, feature engineering, model training, and evaluation. Through project-based learning, students will gain hands-on experience with popular algorithms such as linear regression, decision trees, SVMs, and ensemble methods. By the end of the course, participants will be able to implement, evaluate, and fine-tune machine learning models for real-world applications.
Key Learning Highlights
Understanding the machine learning workflow using real datasets
Hands-on implementation of supervised learning algorithms like regression, classification, and ensemble models
Practical exposure to data preprocessing and feature engineering techniques
Building and evaluating complete ML models using scikit-learn
Project-based tasks to apply ML techniques in real-world scenarios
Tools & Platforms Used
Python
Pandas
NumPy
scikit-learn
Matplotlib
Jupy
Learning Outcome
By the end of this course, students will be able to:
Understand the full machine learning lifecycle, including data preprocessing, feature engineering, and model selection.
Apply supervised learning algorithms such as regression, classification, and ensemble models using scikit-learn.
Work with real datasets and evaluate models with appropriate performance metrics.
Build end-to-end machine learning pipelines using Python and scikit-learn.
Develop project-based machine learning solutions for practical, real-world problems.
Master the course with just 5 Modules
This course takes learners from the fundamentals of Generative AI and prompt construction to advanced tuning techniques. Beginning with the principles behind AI-generated text and images, participants progress to hands-on experimentation with real-world use cases. The journey ends with a deep dive into emerging trends, ethical implications, and future opportunities in the rapidly evolving field of GenAI.
Introduction to Machine Learning and Python ML Ecosystem
- Types of machine learning: supervised, unsupervised, reinforcement
- Installing scikit-learn, Jupyter Notebooks, working in Colab
- Using datasets: load_iris, load_diabetes, fetch_openml
Data Preprocessing and Feature Engineering
- Handling missing data (imputation strategies)
- Encoding categorical variables (Label, One-hot)
- Feature scaling: StandardScaler, MinMaxScaler, RobustScaler
- Pipelines for preprocessing
Supervised Learning with Model Evaluation
- Algorithms: Linear Regression, Logistic Regression, Decision Trees, SVM
- Model evaluation: accuracy, precision, recall, F1-score, confusion matrix
- ROC curve, AUC metrics
- Use of train_test_split, cross_val_score
Unsupervised Learning and Model Selection
- Clustering: K-means, DBSCAN, Hierarchical clustering
- Model selection and tuning: GridSearchCV, RandomizedSearchCV
- Cross-validation strategies: K-Fold, StratifiedKFold
- Bias-variance trade-off and overfitting
Capstone Project – End-to-End ML Pipeline
- Define a real-world ML problem
- Data sourcing and preprocessing
- Model selection, tuning, evaluation
- Reporting with visuals, summary tables, and export of final model
- Submission of notebook/codebase and presentation
Roles
- Machine Learning Engineer
- Data Scientist (Entry-Level)
