Supervised Learning
Train and evaluate classification and regression models.
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After this skill you can…
- Train decision trees, random forests, and neural nets
- Evaluate with accuracy, F1, AUC
- Avoid overfitting with regularisation
Prerequisites
Watch (10 videos)
Introduction to Machine Learning: Supervised Learning
→ Build regression models for prediction→ Implement classification algorithms for decision making→ Evaluate model performance using resampling and regularization
Machine Learning Algorithms: Supervised Learning Tip to Tail
→ Implement supervised learning algorithms→ Analyze business case scenarios with machine learning
Machine Learning Tutorial Python - 8 Logistic Regression (Multiclass Classification)
→ Build logistic regression models for classification→ Use scikit-learn for machine learning tasks
2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
→ Implement k-nearest neighbors in Python→ Use scikit-learn for machine learning tasks
Creating an SVM from scratch - Practical Machine Learning Tutorial with Python p.25
→ Implement SVM from scratch in Python→ Solve constraint optimization problems
L8.5 Logistic Regression in PyTorch -- Code Example
→ Implement logistic regression in PyTorch→ Train a logistic regression model
Waste Classification Machine Learning Classification Project-Waste Recycling
→ Build a waste classification model→ Train a machine learning model
Deep Learning for Real Estate Price Prediction
→ Build a deep learning model for regression tasks→ Predict continuous outcomes with deep learning
TensorFlow Tutorial 03 - First Neural Network (Training, Evaluation & Prediction)
→ Build a deep neural network→ Train and evaluate a model→ Make predictions with a model
Introduction to Machine Learning | | Learning ML with Scikit | Iris Dataset
→ Implement supervised learning using Scikit Learn
DeepCamp AI