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Course Content:
Module 1: Regression-
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-
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, -
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