• Instructor: Tom Steven
  • Students: 3633
  • 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

Curriculum is empty

Price

Free
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