Course Content

Getting Started with Machine Learning

4m 4s

How to use LearnVern

3m 8s

Introduction to Machine Learning

16m 19s

Environment Setup Part 1

3m 21s

Environment Setup Part 2

4m 52s

Environment Setup Part 3

2m 35s

Quiz : Introduction to Machine Learning

Data Wrangling

12m 36s

Importing Libraries and Dataset

11m 35s

Handling Missing Data

12m 13s

Handling Missing Data - Practical

19m 46s

Encoding Categorical Data

7m 3s

Encoding Catergorical Data - Practical

8m 6s

Splitting Dataset

3m 21s

Splitting Dataset - Practical

9m 43s

Normalizing the Data - Part 1

7m 29s

Normalizing the Data - Part 2

7m 30s

Finding Machine Learning Datasets

8m 56s

Exploratory Data Analysis

9m 49s

Plotting Graphs - Part 1

17m 51s

Plotting Graphs - Part 2

21m 44s

Distribution Models - Part 1

29m 48s

Distribution Models - Part 2

10m 23s

Assignment : Data Preprocessing for Machine Learning

1m 54s

Quiz : Data Preprocessing for Machine Learning

10m

Machine Learning Paradigms

10m 37s

Assignment : Machine Learning Paradigms

3m 22s

Quiz : Machine Learning Paradigms

10m

Decision Tree Iterative Dichotomiser 3

20m 2s

Random Forest

5m 25s

Support Vector Machine Classifier

6m 22s

Support Vector Machine Classifier - Practical 1

16m 16s

Support Vector Machine Classifier - Practical 2

10m 25s

Naive Bayes Classifier

6m 36s

Naive Bayes Classifier - Practical 1

14m 34s

Naive Bayes Classifier - Practical 2

9m 42s

Evaluating Classification Models Performance

9m 8s

Evaluating Classification Models Performance - Practical

29m 33s

Overview of Classification

6m 16s

Logistic Regression

5m 35s

Logistic Regression - Practical - 1

30m 22s

Logistic Regression - Practical - 2

KNN

7m 34s

KNN Practical - 1

29m 39s

KNN - Practical 2

9m 53s

Decision Trees for Classification

6m 1s

Decision Trees for Classification - Practical 1

23m 27s

Decision Trees for Classification - Practical 2

18m 39s

Assignment : Supervised Learning Algorithms

1m 34s

Quiz : Supervised Learning Algorithms

10m

Simple Linear Regression

10m 4s

Simple Linear Regression - Practical

11m 46s

Salary Prediction using Linear Regression

19m 25s

Multi-Linear Regression

2m 56s

Startup Prediction using Multiple Regression

20m 34s

Support Vector Regressor

11m 15s

Support Vector Regressor - Practical 1

7m 28s

Support Vector Regressor - Practical 2

14m 44s

Decision Tree Regressor

5m 31s

Decision Tree Regressor - Practical 1

5m 33s

Decision Tree Regressor - Practical 2

16m 31s

Regressor Model Selection

11m 8s

Evaluating Regression Model Performance

10m 45s

Evaluating Regression Model Performance - Practical

12m 11s

Assignment : Regression Algorithms

1m 5s

Quiz : Regression Algorithms

10m

Distance Metrics

6m 1s

K-Means Clustering

11m 40s

K-Means Clustering - Practical

9m 32s

Mall Customers Prediction using K Means Clustering

16m 42s

Hierarchical Clustering - Agglomerative , Divisive

5m 25s

Agglomerative Clustering - Practical

4m 37s

Divisive Clustering - Practical

13m 43s

DBscan Spatial Clustering

10m 53s

Mall Customers Prediction using Hierarchical Clustering

17m 48s

Assignment : Unsupervised Learning Algorithms

3m 11s

Quiz : Unsupervised Learning Algorithms

10m

Association Rule Learning - Apriori, FP Growth

8m 17s

Association Rule Learning - Apriori Practical

7m 41s

Market Basket Analysis using Apriori

19m 30s

FP Growth

Market Basket Analysis using FP Growth

12m 20s

Assignment : Association Rule Mining

56s

Quiz : Association Rule Mining

10m

Reinforcement Learning Theory - Multi Armed Bandits

27m 30s

Upper Confidence Bound - Practical

10m 53s

Thompson Sampling - Practical

5m 34s

Q Learning

32m 24s

Assignment : Reinforcement Learning

1m 43s

Quiz : Reinforcement Learning

10m

Overview of Dimensoionality Reduction

4m 40s

Princinpal Component Analysis

39m 4s

Principal Component Analysis - Practical

6m 37s

Linear Discriminant Analysis

19m 9s

Linear Discriminant Analysis - Practical

6m 29s

Assignment : Dimensionality Reduction

52s

Quiz : Dimensionality Reduction

10m

Basics of Regularization and Optimization

7m 58s

Cross Validation

20m 3s

Hyperparameter Tuning

20m 7s

Sampling Methods

19m 8s

Underfitting and Overfitting in Models

5m 33s

Variance and Bias

9m 33s

Assignment : Regularization and Optimization

1m 1s

Quiz : Regularization and Optimization

10m

Advance Trends in Machine Learning

12m 48s

Introduction to Keras and Deep Learning

9m 2s

Practical Demonstration -Keras

18m 35s

Reinforcement Learning Project - Teach a Taxi Part 1

18m 10s

Reinforcement Learning Project - Teach a Taxi Part 2

15m 11s

Reinforcement Learning Project - Teach a Taxi Part 3

14m 35s

Reinforcement Learning Project - Teach a Taxi Part 4

17m 23s

Loan Prediction Project Part 1

32m 16s

Loan Prediction Project Part 2

28m 42s

Course Summary

3m 53s

Interview Questions Part 1

18m 22s

Interview Questions Part 2

13m 23s

Interview Questions Part 3

16m 2s

Career Guidelines

6m 14s

Assignment : Dimensionality Reduction

Complete Machine Learning Course in English Dimensionality Reduction

Enroll Now For Free

Discussion

Course Content

Getting Started with Machine Learning

4m 4s

How to use LearnVern

3m 8s

Introduction to Machine Learning

16m 19s

Environment Setup Part 1

3m 21s

Environment Setup Part 2

4m 52s

Environment Setup Part 3

2m 35s

Data Wrangling

12m 36s

Importing Libraries and Dataset

11m 35s

Handling Missing Data

12m 13s

Handling Missing Data - Practical

19m 46s

Encoding Categorical Data

7m 3s

Encoding Catergorical Data - Practical

8m 6s

Splitting Dataset

3m 21s

Splitting Dataset - Practical

9m 43s

Normalizing the Data - Part 1

7m 29s

Normalizing the Data - Part 2

7m 30s

Finding Machine Learning Datasets

8m 56s

Exploratory Data Analysis

9m 49s

Plotting Graphs - Part 1

17m 51s

Plotting Graphs - Part 2

21m 44s

Distribution Models - Part 1

29m 48s

Distribution Models - Part 2

10m 23s

Assignment : Data Preprocessing for Machine Learning

1m 54s

Machine Learning Paradigms

10m 37s

Assignment : Machine Learning Paradigms

3m 22s

Decision Tree Iterative Dichotomiser 3

20m 2s

Random Forest

5m 25s

Support Vector Machine Classifier

6m 22s

Support Vector Machine Classifier - Practical 1

16m 16s

Support Vector Machine Classifier - Practical 2

10m 25s

Naive Bayes Classifier

6m 36s

Naive Bayes Classifier - Practical 1

14m 34s

Naive Bayes Classifier - Practical 2

9m 42s

Evaluating Classification Models Performance

9m 8s

Evaluating Classification Models Performance - Practical

29m 33s

Overview of Classification

6m 16s

Logistic Regression

5m 35s

Logistic Regression - Practical - 1

30m 22s

Logistic Regression - Practical - 2

KNN

7m 34s

KNN Practical - 1

29m 39s

KNN - Practical 2

9m 53s

Decision Trees for Classification

6m 1s

Decision Trees for Classification - Practical 1

23m 27s

Decision Trees for Classification - Practical 2

18m 39s

Assignment : Supervised Learning Algorithms

1m 34s

Simple Linear Regression

10m 4s

Simple Linear Regression - Practical

11m 46s

Salary Prediction using Linear Regression

19m 25s

Multi-Linear Regression

2m 56s

Startup Prediction using Multiple Regression

20m 34s

Support Vector Regressor

11m 15s

Support Vector Regressor - Practical 1

7m 28s

Support Vector Regressor - Practical 2

14m 44s

Decision Tree Regressor

5m 31s

Decision Tree Regressor - Practical 1

5m 33s

Decision Tree Regressor - Practical 2

16m 31s

Regressor Model Selection

11m 8s

Evaluating Regression Model Performance

10m 45s

Evaluating Regression Model Performance - Practical

12m 11s

Assignment : Regression Algorithms

1m 5s

Distance Metrics

6m 1s

K-Means Clustering

11m 40s

K-Means Clustering - Practical

9m 32s

Mall Customers Prediction using K Means Clustering

16m 42s

Hierarchical Clustering - Agglomerative , Divisive

5m 25s

Agglomerative Clustering - Practical

4m 37s

Divisive Clustering - Practical

13m 43s

DBscan Spatial Clustering

10m 53s

Mall Customers Prediction using Hierarchical Clustering

17m 48s

Assignment : Unsupervised Learning Algorithms

3m 11s

Association Rule Learning - Apriori, FP Growth

8m 17s

Association Rule Learning - Apriori Practical

7m 41s

Market Basket Analysis using Apriori

19m 30s

FP Growth

Market Basket Analysis using FP Growth

12m 20s

Assignment : Association Rule Mining

56s

Reinforcement Learning Theory - Multi Armed Bandits

27m 30s

Upper Confidence Bound - Practical

10m 53s

Thompson Sampling - Practical

5m 34s

Q Learning

32m 24s

Assignment : Reinforcement Learning

1m 43s

Overview of Dimensoionality Reduction

4m 40s

Princinpal Component Analysis

39m 4s

Principal Component Analysis - Practical

6m 37s

Linear Discriminant Analysis

19m 9s

Linear Discriminant Analysis - Practical

6m 29s

Assignment : Dimensionality Reduction

52s

Basics of Regularization and Optimization

7m 58s

Cross Validation

20m 3s

Hyperparameter Tuning

20m 7s

Sampling Methods

19m 8s

Underfitting and Overfitting in Models

5m 33s

Variance and Bias

9m 33s

Assignment : Regularization and Optimization

1m 1s

Advance Trends in Machine Learning

12m 48s

Introduction to Keras and Deep Learning

9m 2s

Practical Demonstration -Keras

18m 35s

Reinforcement Learning Project - Teach a Taxi Part 1

18m 10s

Reinforcement Learning Project - Teach a Taxi Part 2

15m 11s

Reinforcement Learning Project - Teach a Taxi Part 3

14m 35s

Reinforcement Learning Project - Teach a Taxi Part 4

17m 23s

Loan Prediction Project Part 1

32m 16s

Loan Prediction Project Part 2

28m 42s

Course Summary

3m 53s

Interview Questions Part 1

18m 22s

Interview Questions Part 2

13m 23s

Interview Questions Part 3

16m 2s

Career Guidelines

6m 14s

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