Advanced Python and Data Science

The course includes thorough coverage of Learning Algorithms, Terminology, Data Types, Data Visualization. The course takes a practical approach to creating and organizing Linear Regression ,Binary Classification ,Multiclass Classification.

Attendees will learn how to use Python to create scripts that do Linear Regression , Binary Classification, Multiclass Classification, Data Visualization - Linear, Log, Quadratic and More.

Comprehensive hands on exercises are integrated throughout to reinforce learning and develop real competency.

Students Will Learn

Linear Regression, Linear Model, Squared Error Loss Function, Stochastic Gradient Descent

Linear Regression for complex shapes, AWS - Linear Regression Models

How to evaluate regression model accuracy ,Evaluate predictive quality of the trained model, Review Default Recipe Settings Used To Train model ,Train Model With Custom Recipe and Review Performance

Adding Features To Improve Model

Normalization, Impact of Features With Different Magnitude, Normalization to smoothen magnitude differences, Train Model With Feature Normalizaton

Adding Complex Features, Logistic Regression, Binary Classification - Logistic Regression, Loss Function, Optimization, Binary Classification Approach, True Positive, True Negative, False Positive and False Negative

Data Transformation using Recipes, Text Transformation, Numeric Transformation - Quantile Binning, Numeric Transformation â?? Normalization, Cartesian Product Transformation - Categorical and Text

Hyper Parameters, Model Optimization and Lifecycle, Data Rearrangement, Maximum Model Size, Passes, Shuffle Type, Regularization, Learning Rate, Regularization Effect