Announcement Bell Icon Interested in Personalized Training with Job Assistance? Know More

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

Sampling Methods

Supervised Learning in Machine Learning Machine Learning Paradigms

Enroll Now For Free

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

Statistical sampling is a broad topic of research, but in practical machine learning, you may employ one of three types of sampling: simple random sample, systematic sampling, or stratified sampling. Simple Random Sampling: Samples are selected from the domain with a uniform probability.

The most common probability-based method is the stratified random sample, which divides a population into strata or groups before randomly selecting a subset from each group. The most common non-probability-based method is convenience sampling, which selects a sample from the population at convenience or random intervals.

There are four major methods: 1) basic random, 2) stratified random, 3) cluster, and 4) systematic. Non-probability sampling entails selecting the items of the sample using nonrandom means.

There are two kinds of sampling techniques: Probability sampling involves random selection, which allows you to draw strong statistical conclusions about the entire group. Non-probability sampling entails non-random selection based on convenience or other criteria, making it possible to collect data quickly.

Sampling is an important topic to understand in machine learning. It is important because without sampling, the model will not be able to learn anything. The model will never be able to generalize beyond the sample and it can’t make predictions on new data.

Recommended Courses

Python Programming Course

Students Enrolled : 156495

4.3

(1945)

हिन्दी

Be A Promising Game, App Developer, Data Science Expert...

Enroll For Free

Complete Machine Learning Course

Students Enrolled : 5092

4.4

(34)

हिन्दी

New

Become a certified Machine Learning Expert with our fre...

Enroll For Free

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image

App QR Code Image

Code Scan or Download the app

Google Play Store

598K+ Downloads

App Download Section Circle 1

4.57 Avg. Ratings

App Download Section Circle 2

15K+ Reviews

App Download Section Circle 3

Learn anywhere on the go
Get regular updates about your enrolled or new courses
Share content with your friends
Evaluate your progress through practice tests
No internet connection needed
Enroll for the webinar and join at the time of the webinar from anywhere