中This hands-on course equips learners with the foundational knowledge and practical skills required to build and evaluate supervised machine learning models using Python. Designed around the real-world Titanic dataset, the course walks learners through the complete machine learning pipeline—from project setup and lifecycle understanding to model deployment readiness.
Python: Logistic Regression & Supervised ML
您将获得的技能
- Exploratory Data Analysis
- Feature Engineering
- NumPy
- Machine Learning Algorithms
- Pandas (Python Package)
- Data Cleansing
- Machine Learning
- Data Analysis
- Predictive Modeling
- Scikit Learn (Machine Learning Library)
- Applied Machine Learning
- Data Manipulation
- Decision Tree Learning
- Supervised Learning
- Statistical Modeling
- Classification And Regression Tree (CART)
要了解的详细信息
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最近已更新！
September 2025
作业
6 项作业
授课语言：英语（English）
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本课程是 Python for Data Science: Real Projects & Analytics 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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- 获得可共享的职业证书
该课程共有2个模块
This module introduces learners to the foundational concepts and workflows involved in building supervised machine learning models using Python. It covers the real-world context of a data science project using the Titanic dataset, including the project lifecycle, problem definition, essential Python libraries for data analysis, and an overview of key algorithms such as Decision Trees and Logistic Regression. Through hands-on exposure, learners gain the practical knowledge required to begin implementing classification models and understand how to prepare and structure their machine learning pipeline.
涵盖的内容
6个视频3个作业
This module focuses on the practical steps involved in preparing data for supervised machine learning models. Learners will explore the process of conducting Exploratory Data Analysis (EDA), managing datasets, performing feature engineering, and visualizing insights using Python libraries such as pandas and seaborn. It further guides learners through the model building process, including dataset splitting, performance evaluation using confusion matrices, and applying cross-validation techniques to enhance model reliability.
涵盖的内容
8个视频3个作业
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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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