Improving Machine Learning and Deep Learning Models

Overview

Learn how to improve the performance of machine learning and deep learning models through a structured, hands-on path. This series covers model evaluation, regularization, hyperparameter tuning, and neural network optimization using tools like scikit-learn, XGBoost, and PyTorch.

Syllabus

Course 1: Fixing Classical Models – Diagnosis & Regularization
Course 2: Hypertuning Classical Models
Course 3: Improving Neural Networks with PyTorch
Course 4: Advanced Neural Tuning

Courses

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In this course, learners will improve a poorly performing classical ML model using core diagnostic and regularization techniques. The model starts off weak, and learners fix it step by step through evaluation, regularization, capacity tuning, and early stopping. All models are built using scikit-learn or XGBoost.
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This course teaches learners how to systematically improve classical ML models using hyperparameter search and evaluation strategies, continuing from a weak baseline.
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This course walks learners through improving a weak neural network using techniques specific to deep learning, including dropout, early stopping, and batch normalization.
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This course builds on previous neural improvements by introducing learners to advanced optimization techniques like learning rate schedules, optimizer selection, and weight initialization.