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Course Content

Introduction to Machine Learning

Environment Setup part 1

Environment Setup part 2

Environment Setup part 3

Quiz : Introduction to Machine Learning

Data Wrangling

Importing Libraries and Dataset

Handling Missing Data

Handling Missing Data - Practical

Encoding Categorical Data

Encoding Catergorical Data - Practical

Splitting Dataset

Splitting Dataset - Practical

Normalizing the Data - Part 1

Normalizing the Data - Part2

Finding Machine Learning Datasets

Exploratory Data Analysis

Plotting Graphs - Part 1

Plotting Graphs - Part 2

Distribution Models - Part 1

Distribution Models - Part 2

Assignment of Data Preprocessing for Machine Learning

Quiz : Data Preprocessing for Machine Learning

Machine Learning Paradigms

Sampling Methods

Underfitting and Overfitting in Models

Variance and Bias

Assignment of Machine Learning Paradigms

Quiz : Machine Learning Paradigms

Overview of Classification

Logistic Regression

Logistic Regression - Practical

KNN

KNN - Practical

Decision Trees for Classification

Decision Trees Practical - 1

Decision Trees Practical - 2

Random Forest

Support Vector Machine Classifier

Support Vector Machine Classifier - Practical 1

Support Vector Machine Classifier - Practical 2

Naive Bayes Classifier

Naive Bayes Classifier - Practical 1

Naive Bayes Classifier - Practical 2

Evaluating Classification Models Performance

Evaluating Classification Models Performance - Practical

Assignment of Supervised Learning Algorithms

Quiz : Supervised Learning Algorithms

Simple Linear Regression

Simple Linear Regression - Practical

Multi-Linear Regression

Support Vector Regressor

Support Vector Regressor - Practical

Decision Tree Regressor

Decision Tree Regressor - Practical

Regressor Model Selection

Evaluating Regression Model Performance

Evaluating Regression Model Performance - Practical

Assignment of Regression Algoritms

Quiz : Regression Algorithms

Advance Trends in Machine Learning

Course Summary

Interview Questions Part 1

Interview Questions Part 2

Interview Questions Part 3

Career Guidelines

Machine Learning Paradigms

Supervised Learning in Machine Learning Machine Learning Paradigms

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Discussion

Course Content

Introduction to Machine Learning

Environment Setup part 1

Environment Setup part 2

Environment Setup part 3

Data Wrangling

Importing Libraries and Dataset

Handling Missing Data

Handling Missing Data - Practical

Encoding Categorical Data

Encoding Catergorical Data - Practical

Splitting Dataset

Splitting Dataset - Practical

Normalizing the Data - Part 1

Normalizing the Data - Part2

Finding Machine Learning Datasets

Exploratory Data Analysis

Plotting Graphs - Part 1

Plotting Graphs - Part 2

Distribution Models - Part 1

Distribution Models - Part 2

Assignment of Data Preprocessing for Machine Learning

Machine Learning Paradigms

Sampling Methods

Underfitting and Overfitting in Models

Variance and Bias

Assignment of Machine Learning Paradigms

Overview of Classification

Logistic Regression

Logistic Regression - Practical

KNN

KNN - Practical

Decision Trees for Classification

Decision Trees Practical - 1

Decision Trees Practical - 2

Random Forest

Support Vector Machine Classifier

Support Vector Machine Classifier - Practical 1

Support Vector Machine Classifier - Practical 2

Naive Bayes Classifier

Naive Bayes Classifier - Practical 1

Naive Bayes Classifier - Practical 2

Evaluating Classification Models Performance

Evaluating Classification Models Performance - Practical

Assignment of Supervised Learning Algorithms

Simple Linear Regression

Simple Linear Regression - Practical

Multi-Linear Regression

Support Vector Regressor

Support Vector Regressor - Practical

Decision Tree Regressor

Decision Tree Regressor - Practical

Regressor Model Selection

Evaluating Regression Model Performance

Evaluating Regression Model Performance - Practical

Assignment of Regression Algoritms

Advance Trends in Machine Learning

Course Summary

Interview Questions Part 1

Interview Questions Part 2

Interview Questions Part 3

Career Guidelines

FAQs

A machine learning paradigm is a framework for the design and development of algorithms that learn from data. The paradigm has been used in fields such as computer vision, natural language processing, and speech recognition.

Behaviorism, cognitivism, constructivism, connectivism, and humanism are the most widely acknowledged learning theories today. 1). We shall refer to the named learning paradigms and their associated learning and instructional design theories in this section.

Thus, in both supervised and unsupervised learning, the model (agent) learns based on the training dataset, whereas in RL, the agent learns by interacting with the environment directly. As a result, RL is fundamentally an interaction between the agent and its surroundings.

Supervised learning, unsupervised learning, and reinforcement learning are the three major learning paradigms. Typically, they can be used by any sort of artificial neural network architecture. There are numerous training algorithms for each learning paradigm.

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