.png)
.png)
This course on supervised learning with Python covers the basics of machine learning, data preparation, and encoding categorical variables.
Difficulty
Hours per week
Live Sessions
Next Date
Objective
The objective of this course is to provide participants with a comprehensive understanding of supervised learning algorithms and equip them with the necessary knowledge and skills to effectively apply these techniques using Python. Participants will learn how to preprocess and engineer features, train and evaluate models, and make accurate predictions for classification and regression tasks. By the end of the course, participants will have a solid foundation in supervised learning methodologies, enabling them to apply these techniques to various real-world problems, make data-driven decisions, and gain valuable insights from their data.
To succeed in this course, students should have:
• An intermediate understanding of Python programming.
• Familiarity with basic statistics concepts, such as mean, median, and standard deviation
• Familiarity with data manipulation in Python, including loading, cleaning, and visualizing data
• A project idea to develop over the course of two weeks this project have to start in the second week,you have to developing while take your clases
• Willingness to learn and participate in class discussions and group projects
• Commitment to attend all sessions and complete all assignments on time
In this section, we will introduce the basic concepts of classification models. We will discuss why classification is important, and explore various types of classification problems. We will also introduce the common evaluation metrics used for classification models.
Logistic regression is a popular classification model that works well for binary classification problems. In this section, we will introduce the logistic regression algorithm, and explain how to build and evaluate logistic regression models in Python.
In this section, we will discuss how to preprocess categorical variables for use in classification models. We will introduce various encoding methods, including one-hot encoding and label encoding, and explain how to implement them in Python. We will also discuss the pros and cons of each encoding method, and provide some tips for choosing the appropriate encoding method for different types of categorical variables.
Decision trees are another popular classification model that work well for both binary and multi-class classification problems. In this section, we will introduce the decision tree algorithm, and explain how to build and evaluate decision tree models in Python.
Random forests are an ensemble learning method that combine multiple decision trees to improve performance. In this section, we will introduce the random forest algorithm, and explain how to build and evaluate random forest models in Python.
Support vector machines (SVMs) are a powerful classification algorithm that can be used for both linear and non-linear classification problems. In this section, we will introduce the SVM algorithm, and explain how to build and evaluate SVM models in Python.
Naive Bayes is a simple yet effective classification algorithm that is often used as a baseline model. In this section, we will introduce the Naive Bayes algorithm, and explain how to build and evaluate Naive Bayes models in Python.
In this final section, we will review the key concepts covered in the course, and discuss how to apply classification models to real-world datasets. We will also provide some tips for improving model performance and avoiding common pitfalls.
.png)
