Course Outline
Module I: Regression Models
1. Fundamentals of regression using a linear model example
2. Optimization using the least squares method
3. Practical implementation using scikit-learn
4. Measures of regression model quality
5. Overview of other regression methods
Module II: Data Preparation for Modeling
1. Feature engineering
2. Scaling and standardization of variables
3. Identification and elimination of outliers
4. Strategies for imputing missing values
5. Methods for dimensionality reduction and attribute selection
6. Encoding of categorical variables (one-hot encoding, label encoding)
Module III: The Problem of Model Overfitting
1. The phenomenon of overfitting and its consequences
2. Techniques to counter overfitting
3. Cross-validation as a tool for model evaluation
4. Regularization of machine learning models
Module IV: Optimization of the Learning Process
1. Hyperparameter tuning using grid search
2. Building data processing pipelines
Module V: Classification Algorithms
1. Introduction to classification using logistic regression
2. Comparison of linear and non-linear models
3. Metrics for evaluating classifier quality
4. Decision tree algorithm
5. Naive Bayes classifier
6. Support Vector Machine (SVM)
7. K-Nearest Neighbors method (KNN)
8. Multiclass classification challenges
9. Ensemble methods – Random Forest and Gradient Boosting
Requirements
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Interactivity of the training. We experimented a lot.
Lidia Opuchlik - Orange Szkolenia
Course - Deep Reinforcement Learning with Python
Machine Translated
