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

8m

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

8m

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

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 : Regularization and Optimization

Complete Machine Learning Course in English Regularization and Optimization

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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

8m

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

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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