Think Different

  Introduction Machine Learning concepts Module 1. The Predictive Modeling Pipeline Tabular data exploration Fitting a scikit-learn model on numerical data Handling categorical data Module 2. Selecting the best model Overfitting and Underfitting Validation and learning curves Bias versus variance trade-off Module 3. Hyperparameters tuning Manual tuning Automated tuning Module 4. Linear Models Intuitions on linear models Linear regression Modelling with a non-linear relationship data-target Regularization in linear model Linear model for classification Module 5. Decision tree models Intuitions on tree-based models Decisison tree in classification Decision tree in regression Hyperparameters of decision tree Module 6. Ensemble of models Ensemble method using bootstrapping Ensemble based on boosting Hyperparameters tuning with ensemble methods Module 7. Evaluating model performance Comparing a model with simple baselines Choice of cross-validation Nested cross-validation Classification...

                                                                             Photo by Max Fischer in pexel Here's a comprehensive guide to gaining exceptional knowledge of Python, including a deep understanding of the Python runtime and metaprogramming. Python Fundamentals Before diving into advanced topics, it's essential to have a solid grasp of Python fundamentals. This includes: Variables, Data Types, and Operators : Understand how to declare and use variables, as well as the various data types (e.g., strings, lists, dictionaries) and operators (e.g., arithmetic, comparison, logical) available in Python. Control Structures : Learn how to use if-else statements, for loops, while loops, and try-except blocks to control the flow of your programs. Functions : U...