Artificial Intelligence with Python Prerequisites: Participants must have basic knowledge of any programming language

 AI & Machine Learning vs. Data Science

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• Real implementation  ML Algorithms  Cost Function  Optimizers
• Gradient Descent Algorithm  Use cases  Regression Table  Model Specification
•  Cost Function  Cost Optimizer Gradient Descent Algorithm  Regression R Squared
• Multivariate Regression Model  Apply Data Transformations  Identify Multicollinearity in Data Treatment on Data
•  Bifurcate Data into Training / Testing Dataset

 AI & Machine Learning vs. Data Science.  Discussion on Various ML Learnings  Regression vs. Classification  Features , Labels , Class  Supervised Learning | Unsupervised Learning 3  Real implementation  ML Algorithms  Cost Function  Optimizers Linear Regression  Regression Problem Analysis  Mathematical modelling of Regression Model  Gradient Descent Algorithm  Use cases  Regression Table  Model Specification  L1 & L Regularization  Data sources for Linear regression Math of Linear Regression  Linear Regression Math  Cost Function  Cost Optimizer Gradient Descent Algorithm  Regression R Squared Linear Regression – Case Study & Project  Programming Using Python  Building simple Univariate Linear Regression Model  Multivariate Regression Model  Apply Data Transformations  Identify Multicollinearity in Data Treatment on Data  Identify Heteroscedasticity  Modelling of Data  Variable Significance Identification  Model Significance Test  Bifurcate Data into Training / Testing Dataset Download 436.27 Kb. Share with your friends: