LightGBM (Light Gradient Boosting Machine) is an advanced, open-source gradient boosting framework developed by Microsoft. It is designed to be fast, efficient, and scalable, making it a top choice for structured/tabular machine learning problems. This hands-on course teaches you how to apply LightGBM for classification, regression, ranking, and time series forecasting tasks with ease.
This course is ideal for data scientists, ML engineers, and analysts seeking to harness the power of gradient boosting in real-world projects. You’ll learn how to prepare data, tune hyperparameters, train LightGBM models, evaluate performance, and deploy models for production use.
Whether you’re working on Kaggle competitions or developing business-critical models, LightGBM offers state-of-the-art performance and training speed that scales with large datasets and features.
What You Will Gain
By the end of this course, you will have completed machine learning projects such as:
A customer churn prediction model with high AUC
A house price regression model using boosting and feature importance
A time-series forecasting model using lag features and rolling stats
You’ll also learn to:
Understand the gradient boosting algorithm behind LightGBM
Train models for classification, regression, and ranking tasks
Handle categorical features natively with LightGBM
Use cross-validation and early stopping to avoid overfitting
Tune hyperparameters using grid/random/Bayesian search
Interpret model outputs using SHAP and feature importance
Deploy trained models using joblib or ONNX
Who This Course Is For
This course is perfect for:
Data scientists and ML engineers working with structured data
Analysts transitioning into machine learning roles
Kaggle competitors seeking top leaderboard performance
Developers building high-performance predictive systems
Anyone looking for an alternative to XGBoost or Random Forests
How to Use This Course Effectively
Master the Basics First – Understand decision trees and boosting fundamentals
Follow the Datasets Closely – Use real-world tabular datasets provided in each module
Experiment with Hyperparameters – Practice tuning for maximum model performance
Visualize and Interpret – Use SHAP, gain plots, and feature importance metrics
Track Your Results – Use MLFlow or notebooks to log experiments and insights
Push to Production – Learn model serialization and inference in scripts/APIs
Compare with Other Models – Benchmark LightGBM against XGBoost and CatBoost
Why Learn LightGBM?
LightGBM is one of the fastest and most accurate machine learning algorithms for structured data. It supports GPU acceleration, handles missing values automatically, and processes categorical features efficiently. Learning LightGBM equips you with production-ready tools to solve complex business and analytical problems with ease and speed.
Course/Topic 1 - Coming Soon
The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Upon completing the course, learners will receive a Certificate of Completion from Uplatz that verifies their ability to build high-performance machine learning models using LightGBM. The certificate is a valuable credential for professionals aiming for data science, ML engineering, or AI roles in analytics-driven industries.
LightGBM is widely adopted in industries like finance, e-commerce, healthtech, and telecommunications. Completing this course will prepare you for roles such as:
What is LightGBM and how is it different from XGBoost? Answer: LightGBM is a gradient boosting framework that uses histogram-based and leaf-wise tree growth for faster training and better accuracy. Compared to XGBoost, it is faster on large datasets, handles categorical features natively, and uses less memory.
What are the advantages of LightGBM? Answer:
High speed and efficiency
Native support for categorical variables
Built-in handling of missing values
GPU support for large-scale training
Regularization options to avoid overfitting
What is the leaf-wise growth strategy in LightGBM? Answer: Unlike level-wise trees, LightGBM grows trees by choosing the leaf with the highest loss reduction. This results in deeper, more accurate trees, but requires constraints to prevent overfitting.
How does LightGBM handle categorical features? Answer: LightGBM converts categorical features into integers and uses one-hot encoding internally when necessary. It also finds optimal splits by evaluating combinations efficiently during training.
What are common hyperparameters to tune in LightGBM? Answer:
num_leaves – Controls complexity
learning_rate – Affects convergence
max_depth – Limits tree depth
min_child_samples – Controls overfitting
subsample and colsample_bytree – For randomness and generalization
What metrics are supported in LightGBM? Answer: LightGBM supports evaluation metrics like AUC, Logloss, RMSE, MAE, and accuracy. You can also define custom metrics based on validation needs.
Can LightGBM be used for time-series forecasting? Answer: Yes. While not designed for sequence models, LightGBM works well for time series with proper feature engineering (lags, rolling windows) and careful validation using time-aware splits.
How do you avoid overfitting in LightGBM? Answer: Techniques include early stopping, limiting num_leaves, reducing learning rate, applying regularization (lambda_l1, lambda_l2), and controlling depth with max_depth.
What is early stopping in LightGBM? Answer: Early stopping halts training when performance on a validation set doesn’t improve after a set number of rounds. It helps prevent overfitting and saves training time.
How do you interpret LightGBM models? Answer: You can interpret models using feature importance (gain, split count) and SHAP values to understand how features influence predictions on an individual and global level.
Q1. What are the payment options?
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A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.
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A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience.
Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!
Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field.
However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
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c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.
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