Regularization is the process of shrinking or regularising the coefficients towards zero in machine learning. To put it another way, regularisation prevents overfitting by discouraging the learning of a more complicated or flexible model.

The goal of optimising an objective function is to identify a set of inputs that results in a maximum or minimal function evaluation. Many machine learning algorithms, from fitting logistic regression models to training artificial neural networks, are based on this difficult topic.

One of the most fundamental topics in machine learning is regularisation. It's a method of preventing the model from overfitting by providing additional data. When using training data, the machine learning model may perform well, but when using test data, it may not.

Regularization Techniques in Deep Learning = minimises or eliminates the problem of overfitting. Keras-Tuner optimises neural network structures by reducing the number of connections and neurons for best performance.

When fitting a machine learning algorithm, function optimization is the reason for minimising error, cost, or loss. In a predictive modelling project, optimization is also done during data preparation, hyperparameter tweaking, and model selection.

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