×

Create CV Books Summaries Online Courses Blog Communities

Login

Sign up

Join Our Telegram Channel Now to Get Any New Free Courses : Click Here

ML and Data Science

Track :

Computer Science

Lessons no : 38

For Free Certificate After Complete The Course

To Register in Course you have to watch at least 30 Second of any lesson

Join The Course Go To Community Download Course Content

Mind Luster Certificate Template

How to Get The Certificate

You must have an account Register
Watch All Lessons
Watch at least 50% of Lesson Duration
you can follow your course progress From Your Profile
You can Register With Any Course For Free
The Certificate is free !

Lessons | 38

Lec 1 Introduction to Data Science ML Roadmap to Learn Data Science ML

Lec 1 Introduction to Data Science ML Roadmap to Learn Data Science ML

Lec 2 Supervised Learning Algorithms Machine Learning

Lec 2 Supervised Learning Algorithms Machine Learning

Lec 3 Introduction to Regression with Real Life Examples

Lec 3 Introduction to Regression with Real Life Examples

Lec 4 Linear Regression with Real life examples Calculations Easiest Explanation

Lec 4 Linear Regression with Real life examples Calculations Easiest Explanation

Lec 5 Logistic Regression with Simplest Easiest Example Machine Learning

Lec 5 Logistic Regression with Simplest Easiest Example Machine Learning

Lec 6 Linear Regression Vs Logistic Regression Supervised Learning Machine Learning

Lec 6 Linear Regression Vs Logistic Regression Supervised Learning Machine Learning

Lec 7 kNN Classification with Real Life Example Movie Imdb Example Supervised Learning

Lec 7 kNN Classification with Real Life Example Movie Imdb Example Supervised Learning

Lec 8 Naive Bayes Classification Full Explanation with examples Supervised Learning

Lec 8 Naive Bayes Classification Full Explanation with examples Supervised Learning

Lec 9 Introduction to Decision Tree with Real life examples

Lec 9 Introduction to Decision Tree with Real life examples

Lec 10 Decision Tree ID3 Algorithm with Example Calculations

Lec 10 Decision Tree ID3 Algorithm with Example Calculations

Conditional Probability with Easiest Explanation Example

Conditional Probability with Easiest Explanation Example

Introduction to Ensemble Learning with Real Life Examples Machine Learning

Introduction to Ensemble Learning with Real Life Examples Machine Learning

K mean Clustering with Numerical Example Unsupervised Learning Machine Learning

K mean Clustering with Numerical Example Unsupervised Learning Machine Learning

Hierarchical Clustering Agglomerative vs Divisive with examples

Hierarchical Clustering Agglomerative vs Divisive with examples

Single Linkage Clustering Agglomerative Clustering Hierarchical Clustering

Single Linkage Clustering Agglomerative Clustering Hierarchical Clustering

Complete Linkage Clustering with Example Clustering in Unsupervised Learning ML

Complete Linkage Clustering with Example Clustering in Unsupervised Learning ML

K medoids Clustering with Numerical Example Machine Learning

K medoids Clustering with Numerical Example Machine Learning

Random Forest in Machine Learning

Random Forest in Machine Learning

kNN for Classified Regression with Easiest Explanation Machine Learning

kNN for Classified Regression with Easiest Explanation Machine Learning

Mean Median Mode with Real Life examples Machine Learning

Mean Median Mode with Real Life examples Machine Learning

Standard Deviation Variance with Examples

Standard Deviation Variance with Examples

Bagging Bootstrap Aggregating in Machine Learning with examples

Bagging Bootstrap Aggregating in Machine Learning with examples

Supervised vs Unsupervised learning with real life example

Supervised vs Unsupervised learning with real life example

Python code for Mean Median Mode SD Variance and Range

Python code for Mean Median Mode SD Variance and Range

How Weights are Increased in Boosting Ensemble Learning

How Weights are Increased in Boosting Ensemble Learning

BAGGING vs BOOSTING vs STACKING in Ensemble Learning Machine Learning

BAGGING vs BOOSTING vs STACKING in Ensemble Learning Machine Learning

Bayes Theorem Total Probability with Examples

Bayes Theorem Total Probability with Examples

Cross Validation in Machine Learning with Examples

Cross Validation in Machine Learning with Examples

Pearson s Correlation Coefficient Supervised Learning Data Science Machine Learning

Pearson s Correlation Coefficient Supervised Learning Data Science Machine Learning

kNN k Nearest Neighbour Numerical Example Supervised Learning Machine Learning

kNN k Nearest Neighbour Numerical Example Supervised Learning Machine Learning

Decision Tree Example Calculate Entropy Information Gain Supervised Learning

Decision Tree Example Calculate Entropy Information Gain Supervised Learning

Single Linkage Clustering Example Unsupervised Learning Machine Learning

Single Linkage Clustering Example Unsupervised Learning Machine Learning

Token Parameters in LLama3 META Models 8B 70B Parameters Model GPT model

Token Parameters in LLama3 META Models 8B 70B Parameters Model GPT model

What is Data Preprocessing Data Cleaning Various Techniques with Example

What is Data Preprocessing Data Cleaning Various Techniques with Example

How to Deal with Missing Values in DataSet Data Preprocessing Data Cleaning Imputation Methods

How to Deal with Missing Values in DataSet Data Preprocessing Data Cleaning Imputation Methods

kNN Imputation with Examples Data Preprocessing and Data Cleaning

kNN Imputation with Examples Data Preprocessing and Data Cleaning

Fit Transform Method Data Preprocessing Machine Learning

Fit Transform Method Data Preprocessing Machine Learning

Feature Extraction in Data preprocessing Machine Learning

Feature Extraction in Data preprocessing Machine Learning

We Appreciate Your Feedback

Be the First One Review This Course

Excellent		0 Reviews
Good		0 Reviews
medium		0 Reviews
Acceptable		0 Reviews
Not Good		0 Reviews

0

0 Reviews

Pratham Parab

The concept was great. 2025-07-25

Biplab Sutradhar

Best for Beginner. 2025-06-25

RAHMAN MUSTAFIZUR

NON 2025-05-13

Kateryna Vavryniuk

Really helpful 2025-04-10

Mrs P.Murugeswari

Good 2025-03-27

Rajkumar Devarajan

Excellent 2025-03-27

Samruddhi Nichal

Helpful course 2025-01-15

Animesh Paramanik

This a great course to learn ML and DS , thank you for this . 2025-01-03

Shaik Gohar Nasheen

Nice 2024-12-30

Azeez Aqeel

Good 2024-12-27

Sai lakshmi maheswari kanikicharla

It's very good. 2024-12-26

mahesh ambre

helpful 2024-12-19

Our New Certified Courses Will Reach You in Our Telegram Channel
Join Our Telegram Channels to Get Best Free Courses

Related Courses

Linear Algebra for Computer Scientists

Linear Algebra for Computer Scientists

Insertion Sort

Bubble Sort

GCSE Computer Science

GCSE Computer Science

Random Access Memory

Random Access Memory

Binary Trees

Bitwise Operators

Bitwise Operators

Introduction to Stacks Data Structures

Introduction to Stacks Data Structures

Organization Theory Structure and Design

Organization Theory Structure and Design

Handling Large Scale Unit Level Data

Handling Large Scale Unit Level Data

Introduction to Machine Learning

Introduction to Machine Learning

Applied Linear Algebra Vectors Matrices

Applied Linear Algebra Vectors Matrices

Artificial Intelligence beginners

Artificial Intelligence beginners

Machine Learning Autumn 2018

Machine Learning Autumn 2018

Robotics and Autonomous Systems

Robotics and Autonomous Systems

Natural Language Understanding

Natural Language Understanding

Supervised Learning algorithms , in this course provides a comprehensive introduction to supervised learning algorithms, a core area of machine learning. You’ll explore key concepts, including how models are trained on labeled data to predict outcomes for new inputs. The course covers popular algorithms such as Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Gradient Boosting methods like XGBoost. Learn how to solve classification and regression problems, evaluate model performance using metrics, and optimize hyperparameters. With practical examples and case studies, this course equips you with the skills to apply supervised learning techniques to real-world tasks such as fraud detection, healthcare analytics, and predictive modeling.