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Advanced Data Analysis and Predictive Modeling with Machine Learning Using Python
Original price was: USD $99.00.USD $59.00Current price is: USD $59.00.
A practical, recording-based learning product that teaches you how to go from raw data to reliable predictions using Python—covering advanced EDA, feature engineering, supervised ML (regression/classification/ensembles), tuning, evaluation, and explainability, using a real TOC (Total Organic Carbon) dataset as the working example.
Advanced Data Analysis & Predictive Modeling with Machine Learning Using Python
On-demand workshop recordings • Practical ML workflow • TOC (Total Organic Carbon) dataset example
Instant Access
Intermediate–Advanced
EDA → Features → Modeling → Tuning → Evaluation
Intermediate–Advanced
EDA → Features → Modeling → Tuning → Evaluation
If you’re looking to go beyond basic plots and build predictive models you can trust, this on-demand course is exactly what you need. With advanced data analysis, feature engineering, and supervised machine learning, you’ll learn how to take a raw dataset and turn it into meaningful predictions.
What You’ll Learn
- Advanced EDA and data preprocessing that improves model performance
- Feature engineering (transformation, scaling, and selection of meaningful predictors)
- Supervised ML models for regression, classification, and ensemble techniques
- Model evaluation metrics (e.g., RMSE, accuracy) and correct train-test strategy
- Hyperparameter tuning and cross-validation to improve model robustness
- Model explainability to interpret and trust the results of your predictions
Course Modules (Recordings)
Module 1 — TOC Dataset + Python Workflow Foundations
Learn the basics of importing data, handling missing values, and running baseline statistics to clean your dataset. This module sets up the foundation for the rest of the course.
Module 2 — Deep EDA, Visualization & Feature Engineering
Dive deeper into data exploration with visualization, identifying key patterns, and creating meaningful features for predictive modeling.
Module 3 — Predictive Modeling, Validation & Tuning
Master the art of building regression/classification models, tuning them for the best performance, and evaluating them using real-world metrics like RMSE and accuracy.
Tools Covered
Pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook, scikit-learn, GridSearchCV, RandomizedSearchCV, and optionally XGBoost/LightGBM.
Who It’s For
This course is perfect for doctoral researchers, postdocs, faculty, industry scientists, and postgraduate students who want to develop a practical, research-grade machine learning workflow in Python.
What You’ll Get
- Recorded session access for on-demand learning
- e-Certificate upon completion (if applicable)
- Post-workshop query support (subject to terms)
Tip: Keep this product open while working on your own dataset—pause, replay, and apply each module step-by-step.
Note: This is an on-demand (recording-based) learning product. Access details and included deliverables depend on the product package settings.
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What You’ll Gain
- Full access to e-LMS
- Publication opportunity
- Self-assessment & final exam
- e-Certificate
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