Model Training Courses and Certifications

Training Your First Machine Learning Model from Scratch

Build and evaluate a linear regression model from scratch with scikit-learn, using synthetic and California housing data to predict prices.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Coursera

Model Training & Evaluation

Train and evaluate models in PyTorch: tune mini-batch training and learning-rate schedulers, read loss curves and logs, and compare class weighting with SMOTE.

• Coursera
• 2 hours 35 minutes
• Self-Paced
• Paid Course

Google DeepMind: Accelerate Your Model

21 ratings at DataCamp

Estimate compute and memory needs for training models on a single GPU, apply bfloat16 and gradient accumulation to fine-tune a 4-billion-parameter Gemma model, and weigh AI's environmental impacts.

• DataCamp
• 7 hours
• Self-Paced
• Free Trial Available

Krish Naik

End to End ML Project With Deployment - Project Problem Statement, EDA and Model Training

Build a machine learning project by defining the problem, exploring and engineering data, training a model, and organizing the code modularly.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Practical Methodology and Ethics in AI

11 ratings at Coursera

Train, troubleshoot, and deploy deep learning models while addressing bias and fairness, and integrate structured probabilistic models to handle uncertainty and incomplete data.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

CertNexus

Train Machine Learning Models

Train, tune, and evaluate classification, regression, and clustering models, testing model hypotheses with design of experiments and applying algorithms in a Jupyter notebook.

• Coursera
• 5 weeks, 6 hours a week
• Self-Paced
• Paid Course

Coursera

Debug Neural Networks: Analyze Training Dynamics

Diagnose neural network training failures: read training and validation curves in TensorBoard, spot overfitting and exploding or vanishing gradients, and apply gradient clipping and early stopping.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Choose Cost-Effective ML Algorithms Fast

Judge algorithms by resource use, not just accuracy: benchmark XGBoost against Random Forest on a large dataset, charting training time and memory to recommend the cheapest option.

• Coursera
• 1 hour 59 minutes
• Self-Paced
• Paid Course

Coursera Career Certificate

ML Model Training & Validation

Train, validate, and optimize ML models for production: compare algorithm families, apply cross-validation and SHAP, orchestrate pipelines, version experiments, and monitor deployed models for drift.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 12 courses

Amazon Web Services;  Amazon

AWS ML Engineer Associate 2.2 Train Models (Simplified Chinese)

使用 Amazon SageMaker 内置算法、脚本模式和预构建容器训练机器学习模型，并优化训练时间。

• AWS Skill Builder
• 1 hour 30 minutes
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

AWS ML Engineer Associate 2.4 Analyze Model Performance (Korean)

분류·회귀 지표와 SageMaker 도구를 활용해 ML 모델 성능, 수렴 문제, 해석 가능성 및 재현 가능한 실험을 분석합니다.

• AWS Skill Builder
• 1 hour 30 minutes
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

AWS ML Engineer Associate 2.2 Train Models

Train machine learning models in Amazon SageMaker by selecting compute environments, using built-in algorithms and script mode, and reducing training time.

• AWS Skill Builder
• 1 hour 30 minutes
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

AWS ML Engineer Associate 2.3 Refine Models

Refine machine learning models through regularization, ensemble methods, hyperparameter optimization, pruning, fine-tuning, and versioning with Amazon SageMaker.

• AWS Skill Builder
• 2 hours
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

AWS ML Engineer Associate 2.2 Train Models (한국어)

Amazon SageMaker에서 컴퓨팅 환경, 기본 제공 알고리즘, 스크립트 모드를 활용해 머신러닝 모델을 훈련하고 훈련 시간을 최적화합니다.

• AWS Skill Builder
• 1 hour 30 minutes
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

AWS ML Engineer Associate 2.4 Analyze Model Performance (한국어)

Amazon SageMaker Clarify와 Debugger로 분류·회귀 모델의 성능, 수렴 문제, 재현 가능한 실험을 분석하는 실무 과정입니다.

• AWS Skill Builder
• 1 hour 30 minutes
• Self-Paced
• Paid Course