In data science, underfitting occurs when a data model is unable to effectively represent the link between input and output variables, resulting in a high error rate on both the training set and unknown data.

Overfitting is a statistical modelling error that arises when a function is too tightly fitted to a small number of data points. As a result, the model is only usable in relation to the data set it was created with, and not in relation to any other data sets.

When our machine learning model is unable to capture the underlying trend of the data, we call this underfitting. To prevent the model from overfitting, the feeding of training data can be halted at an early stage, otherwise the model may not learn enough from the training data.

Overfitting is more likely than underfitting to be detrimental. The reason for this is that overfitting has no true upper limit on the reduction of generalisation performance it might cause, whereas underfitting does. Consider a neural network or polynomial regression model, which are non-linear regression models.

Dropout guards against overfitting caused by a layer's "over-reliance" on a few inputs. Because these inputs aren't always available during training (i.e., they're dropped at random), the layer learns to employ all of them, resulting in better generalisation.

Course Content

Getting Started with Machine Learning

Introduction to Machine Learning

Environment Setup Part 1

Environment Setup Part 2

Environment Setup Part 3

Quiz : Introduction to Machine Learning

Data Wrangling in Machine Learning

Importing Libraries and Dataset in Machine Learning

Handling Missing Data in Machine Learning

Handling Missing Data in Machine Learning - Practical

Encoding Categorical Data in Machine Learning

Encoding Categorical Data in Machine Learning - Practical

Splitting Dataset in Machine Learning

Splitting Dataset in Machine Learning - Practical

Normalizing the Data in Machine Learning - Part 1

Normalizing the Data in Machine Learning - Part 2

Finding Machine Learning Datasets

Exploratory Data Analysis in Machine Learning

Plotting Graphs in Machine Learning - Part 1

Plotting Graphs in Machine Learning - Part 2

Distribution Models in Machine Learning - Part 1

Distribution Models in Machine Learning - Part 2

Assignment : Data Preprocessing for Machine Learning

Quiz : Data Preprocessing for Machine Learning

Machine Learning Paradigms

Assignment : Machine Learning Paradigms

Quiz : Machine Learning Paradigms

Overview of Classification

Logistic Regression in Machine Learning

Logistic Regression in Machine Learning - Practical - 1

Logistic Regression in Machine Learning - Practical - 2

KNN in Machine Learning

KNN in Machine Learning Practical - 1

KNN in Machine Learning - Practical 2

Decision Trees for Classification in Machine Learning

Decision Trees for Classification in Machine Learning - Practical - 1

Decision Trees for Classification in Machine Learning - Practical - 2

Decision Tree Iterative Dichotomiser in Machine Learning - 3

Random Forest in Machine Learning

Support Vector Machine Classifier

Support Vector Machine Classifier - Practical - 1

Support Vector Machine Classifier - Practical - 2

Naive Bayes Classifier in Machine Learning

Naive Bayes Classifier in Machine Learning Practical - 1

Naive Bayes Classifier in Machine Learning Practical - 2

Evaluating Classification Models Performance in Machine Learning

Evaluating Classification Models Performance in Machine Learning Practical

Assignment : Supervised Learning Algorithms

Quiz : Supervised Learning Algorithms

Simple Linear Regression in Machine Learning

Simple Linear Regression in Machine Learning - Practical

Salary Prediction using Linear Regression in Machine Learning

Multi-Linear Regression in Machine Learning

Startup Prediction using Multiple Regression in Machine Learning

Support Vector Regressor in Machine Learning

Support Vector Regressor in Machine Learning - Practical - 1

Support Vector Regressor in Machine Learning Practical - 2

Decision Tree Regressor in Machine Learning

Decision Tree Regressor in Machine Learning Practical - 1

Decision Tree Regressor in Machine Learning Practical - 2

Regressor Model Selection in Machine Learning

Evaluating Regression Model Performance in Machine Learning

Evaluating Regression Model Performance in Machine Learning - Practical

Assignment : Regression Algorithms

Quiz : Regression Algorithms

Distance Metrics in Machine Learning

K-Means Clustering in Machine Learning

K-Means Clustering in Machine Learning - Practical

Mall Customers Prediction using K Means Clustering in Machine Learning

Hierarchical Clustering in Machine Learning - Agglomerative , Divisive

Agglomerative Clustering in Machine Learning - Practical

Divisive Clustering in Machine Learning - Practical

DBscan Spatial Clustering in Machine Learning

Mall Customers Prediction using Hierarchical Clustering in Machine Learning

Assignment : Unsupervised Learning Algorithms

Quiz : Unsupervised Learning Algorithms

Association Rule in Machine Learning - Apriori, FP Growth

Association Rule in Machine Learning - Apriori Practical

Market Basket Analysis using Apriori in Machine Learning