Course Content

Getting Started with Machine Learning

4m 4s

Introduction to Machine Learning

13m 51s

Environment Setup Part 1

3m 32s

Environment Setup Part 2

4m 43s

Environment Setup Part 3

2m 34s

Quiz : Introduction to Machine Learning

8m

Data Wrangling in Machine Learning

12m 26s

Importing Libraries and Dataset in Machine Learning

11m 19s

Handling Missing Data in Machine Learning

11m 51s

Handling Missing Data in Machine Learning - Practical

17m 3s

Encoding Categorical Data in Machine Learning

6m 34s

Encoding Categorical Data in Machine Learning - Practical

8m 3s

Splitting Dataset in Machine Learning

3m 38s

Splitting Dataset in Machine Learning - Practical

9m 36s

Normalizing the Data in Machine Learning - Part 1

6m 19s

Normalizing the Data in Machine Learning - Part 2

6m 58s

Finding Machine Learning Datasets

9m 17s

Exploratory Data Analysis in Machine Learning

9m 1s

Plotting Graphs in Machine Learning - Part 1

19m 12s

Plotting Graphs in Machine Learning - Part 2

23m 45s

Distribution Models in Machine Learning - Part 1

33m 55s

Distribution Models in Machine Learning - Part 2

13m 56s

Assignment : Data Preprocessing for Machine Learning

1m 40s

Quiz : Data Preprocessing for Machine Learning

10m

Machine Learning Paradigms

9m 55s

Assignment : Machine Learning Paradigms

3m 25s

Quiz : Machine Learning Paradigms

10m

Overview of Classification

5m 22s

Logistic Regression in Machine Learning

4m 54s

Logistic Regression in Machine Learning - Practical - 1

32m 40s

Logistic Regression in Machine Learning - Practical - 2

12m 37s

KNN in Machine Learning

7m 22s

KNN in Machine Learning Practical - 1

35m 1s

KNN in Machine Learning - Practical 2

8m 46s

Decision Trees for Classification in Machine Learning

6m 17s

Decision Trees for Classification in Machine Learning - Practical - 1

22m 42s

Decision Trees for Classification in Machine Learning - Practical - 2

20m 42s

Decision Tree Iterative Dichotomiser in Machine Learning - 3

22m 52s

Random Forest in Machine Learning

8m 37s

Support Vector Machine Classifier

5m 44s

Support Vector Machine Classifier - Practical - 1

16m 59s

Support Vector Machine Classifier - Practical - 2

11m 19s

Naive Bayes Classifier in Machine Learning

5m 25s

Naive Bayes Classifier in Machine Learning Practical - 1

15m 21s

Naive Bayes Classifier in Machine Learning Practical - 2

11m 8s

Evaluating Classification Models Performance in Machine Learning

7m 31s

Evaluating Classification Models Performance in Machine Learning Practical

33m 2s

Assignment : Supervised Learning Algorithms

1m 12s

Quiz : Supervised Learning Algorithms

10m

Simple Linear Regression in Machine Learning

8m 57s

Simple Linear Regression in Machine Learning - Practical

12m 34s

Salary Prediction using Linear Regression in Machine Learning

22m 42s

Multi-Linear Regression in Machine Learning

2m 53s

Startup Prediction using Multiple Regression in Machine Learning

25m 13s

Support Vector Regressor in Machine Learning

10m 45s

Support Vector Regressor in Machine Learning - Practical - 1

6m 48s

Support Vector Regressor in Machine Learning Practical - 2

15m 46s

Decision Tree Regressor in Machine Learning

4m 53s

Decision Tree Regressor in Machine Learning Practical - 1

5m 2s

Decision Tree Regressor in Machine Learning Practical - 2

17m 10s

Regressor Model Selection in Machine Learning

9m 7s

Evaluating Regression Model Performance in Machine Learning

8m 46s

Evaluating Regression Model Performance in Machine Learning - Practical

11m 35s

Assignment : Regression Algorithms

1m 12s

Quiz : Regression Algorithms

10m

Association Rule in Machine Learning - Apriori, FP Growth

7m 19s

Association Rule in Machine Learning - Apriori Practical

7m 15s

Market Basket Analysis using Apriori in Machine Learning

17m 50s

FP Growth in Machine Learning

7m 43s

Market Basket Analysis using FP Growth in Machine Learning

11m 46s

Assignment : Association Rule Mining

46s

Quiz : Association Rule Mining

10m

Reinforcement Learning in Machine Learning - Multi Armed Bandits

21m 4s

Upper Confidence Bound in Machine Learning - Practical

8m 41s

Thompson Sampling in Machine Learning - Practical

4m 18s

Q Learning in Machine Learning

33m 47s

Assignment : Reinforcement Learning

1m 21s

Quiz : Reinforcement Learning

10m

Overview of Dimensionality Reduction in Machine Learning

4m 26s

Principal Component Analysis in Machine Learning

33m 44s

Principal Component Analysis in Machine Learning - Practical

6m 35s

Linear Discriminant Analysis in Machine Learning

18m 51s

Linear Discriminant Analysis in Machine Learning - Practical

6m 42s

Assignment : Dimensionality Reduction

55s

Quiz : Dimensionality Reduction

10m

Basics of Regularization and Optimization in Machine Learning

7m 40s

Cross Validation in Machine Learning

20m 27s

Hyperparameter Tuning in Machine Learning

19m 21s

Sampling Methods in Machine Learning

19m 43s

Underfitting and Overfitting in Models in Machine Learning

6m 8s

Variance and Bias in Machine Learning

9m 49s

Assignment : Regularization and Optimization

1m 4s

Quiz : Regularization and Optimization

10m

Advance Trends in Machine Learning

10m 19s

Introduction to Keras and Deep Learning

7m 43s

Practical Demonstration -Keras

13m 59s

Reinforcement Learning Project - Teach a Taxi Part 1

16m 21s

Reinforcement Learning Project - Teach a Taxi Part 2

14m 37s

Reinforcement Learning Project - Teach a Taxi Part 3

13m 2s

Reinforcement Learning Project - Teach a Taxi Part 4

15m 26s

Loan Prediction Project Part 1

35m 45s

Loan Prediction Project Part 2

26m 17s

Course Summary

3m 52s

Interview Questions Part 1

19m 22s

Interview Questions Part 2

12m 23s

Interview Questions Part 3

15m 21s

Career Guideline

6m

DBscan Spatial Clustering in Machine Learning

Complete Machine Learning Course Unsupervised Learning Algorithms

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Course Content

Getting Started with Machine Learning

4m 4s

Introduction to Machine Learning

13m 51s

Environment Setup Part 1

3m 32s

Environment Setup Part 2

4m 43s

Environment Setup Part 3

2m 34s

Data Wrangling in Machine Learning

12m 26s

Importing Libraries and Dataset in Machine Learning

11m 19s

Handling Missing Data in Machine Learning

11m 51s

Handling Missing Data in Machine Learning - Practical

17m 3s

Encoding Categorical Data in Machine Learning

6m 34s

Encoding Categorical Data in Machine Learning - Practical

8m 3s

Splitting Dataset in Machine Learning

3m 38s

Splitting Dataset in Machine Learning - Practical

9m 36s

Normalizing the Data in Machine Learning - Part 1

6m 19s

Normalizing the Data in Machine Learning - Part 2

6m 58s

Finding Machine Learning Datasets

9m 17s

Exploratory Data Analysis in Machine Learning

9m 1s

Plotting Graphs in Machine Learning - Part 1

19m 12s

Plotting Graphs in Machine Learning - Part 2

23m 45s

Distribution Models in Machine Learning - Part 1

33m 55s

Distribution Models in Machine Learning - Part 2

13m 56s

Assignment : Data Preprocessing for Machine Learning

1m 40s

Machine Learning Paradigms

9m 55s

Assignment : Machine Learning Paradigms

3m 25s

Overview of Classification

5m 22s

Logistic Regression in Machine Learning

4m 54s

Logistic Regression in Machine Learning - Practical - 1

32m 40s

Logistic Regression in Machine Learning - Practical - 2

12m 37s

KNN in Machine Learning

7m 22s

KNN in Machine Learning Practical - 1

35m 1s

KNN in Machine Learning - Practical 2

8m 46s

Decision Trees for Classification in Machine Learning

6m 17s

Decision Trees for Classification in Machine Learning - Practical - 1

22m 42s

Decision Trees for Classification in Machine Learning - Practical - 2

20m 42s

Decision Tree Iterative Dichotomiser in Machine Learning - 3

22m 52s

Random Forest in Machine Learning

8m 37s

Support Vector Machine Classifier

5m 44s

Support Vector Machine Classifier - Practical - 1

16m 59s

Support Vector Machine Classifier - Practical - 2

11m 19s

Naive Bayes Classifier in Machine Learning

5m 25s

Naive Bayes Classifier in Machine Learning Practical - 1

15m 21s

Naive Bayes Classifier in Machine Learning Practical - 2

11m 8s

Evaluating Classification Models Performance in Machine Learning

7m 31s

Evaluating Classification Models Performance in Machine Learning Practical

33m 2s

Assignment : Supervised Learning Algorithms

1m 12s

Simple Linear Regression in Machine Learning

8m 57s

Simple Linear Regression in Machine Learning - Practical

12m 34s

Salary Prediction using Linear Regression in Machine Learning

22m 42s

Multi-Linear Regression in Machine Learning

2m 53s

Startup Prediction using Multiple Regression in Machine Learning

25m 13s

Support Vector Regressor in Machine Learning

10m 45s

Support Vector Regressor in Machine Learning - Practical - 1

6m 48s

Support Vector Regressor in Machine Learning Practical - 2

15m 46s

Decision Tree Regressor in Machine Learning

4m 53s

Decision Tree Regressor in Machine Learning Practical - 1

5m 2s

Decision Tree Regressor in Machine Learning Practical - 2

17m 10s

Regressor Model Selection in Machine Learning

9m 7s

Evaluating Regression Model Performance in Machine Learning

8m 46s

Evaluating Regression Model Performance in Machine Learning - Practical

11m 35s

Assignment : Regression Algorithms

1m 12s

Association Rule in Machine Learning - Apriori, FP Growth

7m 19s

Association Rule in Machine Learning - Apriori Practical

7m 15s

Market Basket Analysis using Apriori in Machine Learning

17m 50s

FP Growth in Machine Learning

7m 43s

Market Basket Analysis using FP Growth in Machine Learning

11m 46s

Assignment : Association Rule Mining

46s

Reinforcement Learning in Machine Learning - Multi Armed Bandits

21m 4s

Upper Confidence Bound in Machine Learning - Practical

8m 41s

Thompson Sampling in Machine Learning - Practical

4m 18s

Q Learning in Machine Learning

33m 47s

Assignment : Reinforcement Learning

1m 21s

Overview of Dimensionality Reduction in Machine Learning

4m 26s

Principal Component Analysis in Machine Learning

33m 44s

Principal Component Analysis in Machine Learning - Practical

6m 35s

Linear Discriminant Analysis in Machine Learning

18m 51s

Linear Discriminant Analysis in Machine Learning - Practical

6m 42s

Assignment : Dimensionality Reduction

55s

Basics of Regularization and Optimization in Machine Learning

7m 40s

Cross Validation in Machine Learning

20m 27s

Hyperparameter Tuning in Machine Learning

19m 21s

Sampling Methods in Machine Learning

19m 43s

Underfitting and Overfitting in Models in Machine Learning

6m 8s

Variance and Bias in Machine Learning

9m 49s

Assignment : Regularization and Optimization

1m 4s

Advance Trends in Machine Learning

10m 19s

Introduction to Keras and Deep Learning

7m 43s

Practical Demonstration -Keras

13m 59s

Reinforcement Learning Project - Teach a Taxi Part 1

16m 21s

Reinforcement Learning Project - Teach a Taxi Part 2

14m 37s

Reinforcement Learning Project - Teach a Taxi Part 3

13m 2s

Reinforcement Learning Project - Teach a Taxi Part 4

15m 26s

Loan Prediction Project Part 1

35m 45s

Loan Prediction Project Part 2

26m 17s

Course Summary

3m 52s

Interview Questions Part 1

19m 22s

Interview Questions Part 2

12m 23s

Interview Questions Part 3

15m 21s

Career Guideline

6m

FAQs

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering base technique. It can find clusters of various forms and sizes in a big amount of data that includes noise and outliers.

The goal of DBSCAN is to locate data point neighbourhoods that exceed a specific density criterion. The radius of the neighbourhood (eps) and the minimum number of neighbors/data points (minPts) within the radius of the neighbourhood define the density threshold.

DBSCAN is a clustering method used in machine learning to distinguish high-density clusters from low-density clusters.

DBSCAN has two parameters: (eps) and the minimum number of points needed to build a dense zone (minPts). It begins with a random starting spot that has never been visited before. The -neighborhood of this point is retrieved, and if it contains enough points, a cluster is formed.

Advantages. DBSCAN does not require the number of clusters to be specified in advance. DBSCAN works effectively with clusters of any shape. DBSCAN understands noise and is resistant to outliers.

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