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Course Features
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Language: English
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7 hours on-demand video
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11 Lessons
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Study Level: Intermediate
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Certificate of Completion

Installation and configuration
Data Preprocessing
Regression Techniques
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Evaluating Regression Model Performance
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
Natural Language Processing (NLP)
Basic of NLP
Language preprocessing Techniques
Auto summarizing the given text document
Clustering Techniques
K-Means Clustering
K-mini Batch Clustering
Hierarchical Clustering
Elbow Method
Curve Smoothening Techniques
Association Rule Learning
Reinforcement Learning
Basics of Numpy and panda
Deep Learning 
Basics/what is Deep Learning
Artificial Neural Networks
Dimension Reduction Techniques
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Statistics Basics
Standard Deviation
Variance
Co-Variance
T-distribution
Pearson Correlation Coefficient (PCC)/ Correlation Coefficient

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