- Instructor: Tom Steven
- Students: 8633
- Duration: 5 weeks

• Introduction to data science

• Applications of data science

• Importance of data science

• Difference between Machine Learning, Deep Learning and Ai

• Introduction to Deep learning

• Introduction to Ai

• Data and Data types

• Measures of central tendency

• Variance, Standard Deviation and Range

• Skewness & Kurtosis

• Various distributions

• Basic plots

• Introduction to hypothesis testing

• Definition of Hypothesis testing

• Errors in Hypothesis testing

• One-way Anova

• Two-way Anova

• Linear regression

• Logistic regression

• Support Vector Machines

• Neural Networks

• Decision Tree

• Random forest

• K-Nearest Neighbor

• Clustering

• Principal Component Analysis

• Association Rules

• Recommender systems

• Singular value Decomposition

• Text Mining

• Processing the text

• Positive word clouds

• Negative word cloud

• Applications of positive & negative word cloud

• How to deal with outliers

• How to overcome overfitting and Underfitting

• Hands-on sessions

• Building a model

• How to improve accuracy of a model

• Installation of R & R-studio

• Introduction to R

• Usage of packages

• How to deal with outliers

• How to overcome overfitting and Underfitting

• Hands-on sessions

• Building a model

• How to improve accuracy of a model

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