Course Outline
Module I: Regression Models
1. Fundamentals of regression using a linear model as an example
2. Optimization by least squares
3. Practical implementation using scikit-learn
4. Quality metrics for regression models
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 handling missing values
5. Dimensionality reduction and feature selection methods
6. Encoding 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 counteract overfitting
3. Cross-validation as a model assessment tool
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. Quality assessment metrics for classifiers
4. Decision tree algorithm
5. Naive Bayes classifier
6. Support Vector Machine (SVM)
7. k-Nearest Neighbors (KNN) method
8. The issue of multi-class classification
9. Ensemble methods – Random Forest and Gradient Boosting
Requirements
Testimonials (2)
Interactivity of the training. We experimented a lot.
Lidia Opuchlik - Orange Szkolenia
Course - Deep Reinforcement Learning with Python
Machine Translated
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
