TechShikhi
Understanding the Balance between Bias (Underfitting) and Variance (Overfitting)
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ai-naymul/TechShikhi
TechShikhi
ai-naymul/TechShikhi
TechShikhi
Machine Learning
Machine Learning
Introduction to Machine Learning
Introduction to Machine Learning
What is Machine Learning
Applications of Machine Learning
Difference between AI, Machine Learning, and Deep Learning
Types of Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Machine Learning Algorithms
Machine Learning Algorithms
Linear Regression
Logistic Regression
Decision Trees
Random Forest
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Neural Networks
Neural Networks
Neural Networks and Deep Learning
Structure of Neural Networks
How Neural Networks Mimic the Human Brain
Data in Machine Learning
Data in Machine Learning
Importance of Data in Machine Learning
How to Split Data into Training and Testing Sets
Model Evaluation
Model Evaluation
Techniques to Evaluate the Performance of a Machine Learning Model
Overfitting and Underfitting
Overfitting and Underfitting
Understanding Overfitting and Underfitting
How to Handle Overfitting and Underfitting
Regularization
Regularization
Techniques to Prevent Overfitting
Optimization Algorithms
Optimization Algorithms
Understanding Optimization Algorithms like Gradient Descent
Bias-Variance Tradeoff
Bias-Variance Tradeoff
Understanding the Balance between Bias (Underfitting) and Variance (Overfitting)
Ethical AI and Bias Mitigation
Understanding the Ethical Implications of Machine Learning
How to Mitigate Bias in Machine Learning
Contributing
Understanding the Balance between Bias (Underfitting) and Variance (Overfitting)