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Course Content

Module 01: Regression Techniques

Random Forest Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Support Vector Regression
Decision Tree Regression

Module 2: Classification Techniques

K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Kernel SVM
Naïve Bayes Classification
Decision Tree Classification
Random Forest Classification
Evaluating Classification Model Performance

Module 3:Natural Language Processing (NLP)

Basic of NLP
Language preprocessing Techniques
Auto summarizing the given text document
K-Means Clustering
K-mini Batch Clustering
Build Pipeline View Project
Hierarchical Clustering
Jenkins to run script remotely
Elbow Method
Curve Smoothing Techniques

Module 4:Dimension Reduction Techniques

Docker Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Basics of Numpy and panda
Reinforcement Learning
Association Rule Learning
Curve Smoothing Techniques

Module 5: Statistics Basics

Standard Deviation
Variance
Co-Variance
T-distribution
Pearson Correlation Coefficient (PCC)/ Correlation Coefficient

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