Supervised Learning
Train and evaluate classification and regression models.
0%
Confidence · no data yet
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)
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
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
Pytorch Tutorial 5-Live- Kaggle Advance House Price Prediction Using Pytorch Deep Learning
→ Build a house price prediction model with PyTorch→ Implement deep learning techniques for predictive modeling
PyTorch Book Reading - 7. Training a Tumor classifier
→ Train a classifier→ Use PyTorch for deep learning
How to implement Logistic Regression from scratch with Python
→ Implement Logistic Regression from scratch→ Understand the math behind Logistic Regression
SVM Optimization - Practical Machine Learning Tutorial with Python p.27
→ Implement SVM in Python→ Optimize machine learning models
DeepCamp AI