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Course Content:

Module 1: Regression-

Techniques:

1.Random Forest Regression

2. Simple Linear Regression

3. Multiple Linear Regression

4. Polynomial Linear Regression

6. Support Vector Regression                   

7. Decision Tree Regression  

Module 2: Classification-

Techniques:

1. K-Nearest Neighbors (KNN)

2. Support Vector Machine (SVM)

3. Kernel SVM

4. Naïve Bayes Classification

5. Decision Tree Classification

6. Random Forest Classification

7. Evaluating Classification-

Model Performance

Module 3:Natural Language-

Processing (NLP):

1. Basic of NLP

2. Language preprocessing Techniques

3. Auto summarizing the given-

text document

4. K-Means Clustering

5. K-mini Batch Clustering  

6. Build Pipeline View Project

7. Hierarchical Clustering

8. Jenkins to run script remotely

9. Elbow Method

10.Curve Smoothening Techniques

Module 4:Dimension-

Reduction Techniques :

1. Docker Principal Component-

Analysis (PCA)

2. Linear Discriminant Analysis (LDA)

3. Basics of Numpy and panda

4. Reinforcement Learning

5. Association Rule Learning

6. Curve Smoothening Techniques

Module 5: Statistics Basics:

1. Standard Deviation

2. Variance

3. Co-Variance

4. T-distribution

5. Pearson Correlation Coefficient-

(PCC)/ Correlation Coefficient

Module 6:Model Selection:

1. To Learn Live Online Machine-

Learning Training-

in Globally and Design Machine Learning-

Training in Noida using Class Room, -

ENROLL TODAY at myTectra.

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