Curriculum

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1.Installation and configuration

2.Data Preprocessing

3.Regression Techniques

• Simple Linear Regression

• Multiple Linear Regression

• Polynomial Linear Regression

• Support Vector Regression

• Decision Tree Regression

• Random Forest Regression

• Evaluating Regression Model Performance

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4.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

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5.Natural Language Processing (NLP)

• Basic of NLP

• Language preprocessing Techniques

• Auto summarizing the given text document

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5.Natural Language Processing (NLP)

• Basic of NLP

• Language preprocessing Techniques

• Auto summarizing the given text document

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7.Elbow Method
8.Curve Smoothening Techniques
9.Association Rule Learning
10.Reinforcement Learning
11.Basics of Numpy and panda
12.Deep Learning

• Basics/what is Deep Learning

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13.Artificial Neural Networks15
14.Dimension Reduction Techniques

• Principal Component Analysis (PCA)

• Linear Discriminant Analysis (LDA)

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15.Statistics Basics

• Standard Deviation

• Variance

• Co-Variance

• T-distribution

• Pearson Correlation Coefficient (PCC)/ Correlation Coefficient

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16.Model Selection

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