Course Outline
Module I: Regression Models
1. Basics of regression using the 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 standardizing variables
3. Identifying and eliminating outliers
4. Strategies for imputing missing values
5. Dimensionality reduction and feature selection methods
6. Encoding categorical variables (one-hot encoding, label encoding)
Module III: The Problem of Overfitting
1. The phenomenon of overfitting and its consequences
2. Techniques to prevent overfitting
3. Cross-validation as a tool for model evaluation
4. Regularization in 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. Evaluation metrics for classifiers
4. Decision tree algorithm
5. Naive Bayes classifier
6. Support Vector Machine (SVM)
7. k-Nearest Neighbors (KNN) method
8. Multiclass classification issues
9. Ensemble methods—Random Forest and Gradient Boosting
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
