Machine Learning in Practice Training is a hands-on program designed to help participants move beyond machine learning theory and develop the practical skills needed to build, evaluate, and apply machine learning models to real-world problems. The course combines essential concepts with practical exercises using real datasets and industry-relevant scenarios.
Duration 3 days – 21 hrs
Overview
Participants will work with key supervised learning algorithms, including Decision Trees, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naive Bayes. Through guided exercises, learners will understand how these algorithms work, when to use them, and how their performance can vary depending on the characteristics of the dataset and the problem being solved.
A major focus of the training is machine learning model evaluation. Participants will learn how to interpret metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices to determine whether a model is performing effectively. They will also explore cross-validation techniques that help produce more reliable estimates of model performance and reduce the risk of making decisions based on a single train-test split.
The course also addresses two common challenges in machine learning: overfitting and underfitting. Participants will learn how to identify these issues, understand their causes, and apply practical techniques to improve model generalization. The training introduces hyperparameter optimization using tools such as GridSearchCV and RandomizedSearchCV, enabling learners to systematically improve model performance.
Using Python and scikit-learn, participants will develop practical machine learning pipelines that bring together data preparation, model training, evaluation, and optimization. Rather than focusing solely on individual algorithms, the course emphasizes a structured approach to solving machine learning problems from model selection through final evaluation.
The final mini-project provides an opportunity to apply the concepts covered throughout the training. Participants will select an appropriate algorithm, evaluate its performance, apply cross-validation and hyperparameter tuning, and present their results using a real-world dataset.
By the end of the Machine Learning in Practice Training, participants will have strengthened their practical machine learning skills and gained the confidence to compare algorithms, evaluate model performance, address overfitting and underfitting, optimize models, and develop structured machine learning workflows using Python and scikit-learn.
Objectives
- Implement and compare key supervised ML algorithms: Decision Trees, SVM, KNN, and Naive Bayes
- Evaluate model performance using accuracy, precision, recall, F1-score, and confusion matrices
- Apply cross-validation and tuning techniques for better generalization
- Identify and address overfitting and underfitting in machine learning models
- Use Python (scikit-learn) to build optimized ML pipelines on structured datasets
Audience
- Data analysts, junior data scientists, and AI enthusiasts with basic ML experience
- Professionals who completed an introductory ML course and want to advance their skills
- Developers and technical leads applying ML models in real-world projects
- Students and researchers seeking practical modeling and evaluation experience
Prerequisites
- Completion of an introductory machine learning course
- Proficiency in Python and foundational libraries (NumPy, Pandas, scikit-learn)
- Familiarity with basic ML concepts: regression, classification, and model training
Course Content
Day 1: Supervised ML Algorithms in Action
- Decision Trees: concept, splitting criteria, advantages & limitations
- SVM (Support Vector Machines): linear vs. nonlinear classification, kernels
- K-Nearest Neighbors (KNN): distance metrics, selecting K
- Naive Bayes: probability, assumptions, and text classification use case
- Hands-on: Implementing and comparing classifiers on a sample dataset
Day 2: Model Evaluation Techniques
- Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
- Confusion matrix interpretation and reporting
- Cross-validation: k-fold, stratified sampling, holdout method
- Hands-on: Evaluate and visualize model performance using scikit-learn
Day 3: Optimization, Overfitting & Practical ML Pipeline
- Underfitting vs. Overfitting: causes, detection, and solutions
- Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
- Building a modular ML pipeline in scikit-learn
- Final mini project: Select an algorithm, apply cross-validation, tune parameters, and report performance on a real dataset
