Description
Logistic Regression in Python Course. This course is a comprehensive and practical program for learning Classification modeling algorithms in Python programming that teaches learners how to solve real business problems using Logistic Regression, Linear Discriminant Analysis, and KNN. This course is designed for people who want to learn the complete process of building Classification models in Machine Learning algorithms and implement it on real business problems. The course topics start with the most basic concepts of statistics and data analysis; where how to work with data types, central indices such as Mean, Median, and Mode, and dispersion indices such as Range and Standard Deviation are examined. Then, participants are introduced to the Python and Jupyter environments and learn how to use important libraries such as Numpy, Pandas, and Seaborn. The instructors of this course, relying on their experience working in management consulting and analytics firms, fully cover data preprocessing concepts such as Data Exploration, Uni-variate and Bi-variate analyses, Outlier removal, and Missing Values cleaning. In the specialized sections of the course, three main and applied techniques including Logistic Regression, Linear Discriminant Analysis, and K-Nearest Neighbors are taught theoretically and practically. Learners learn how to measure the performance of the built models with the Confusion Matrix tool and analyze the output results to make smarter decisions in business environments. In addition to videos and practice files, a complete practical project has been included so that learners can consolidate their learning and acquire the necessary skills to enter the Data Science and Machine Learning job market.
What you will learn
- Understand how to interpret Logistic Regression model results in Python and transform them into practical business solutions
- Learn Linear Discriminant Analysis and K-Nearest Neighbors techniques in Python
- Performing initial data analysis using Univariate Analysis before running and implementing the Classification model
- Predicting future outcomes and results based on past data by implementing Machine Learning algorithms
- Gain in-depth knowledge of data collection and data preprocessing for Logistic Regression problems
- Learn how to solve real-world business problems using various classification techniques
- Implementing a comprehensive practical project to implement and consolidate all the lessons learned during the course.
- Learn basic and introductory statistics using the Numpy library in Python
- Data Representation and Visualization Using the Seaborn Library in Python
- Implementing Classification techniques in Machine Learning using Scikit-Learn and Statsmodels libraries
This course is suitable for people who:
- People who are looking for professional activity and career development in the field of Data Science
- Professionals and practitioners who want to start their Data Journey
- Statisticians who need to gain more practical and applied experience
- All those interested in learning Classification techniques in Machine Learning in a short time, from beginner to advanced level.
Logistic Regression in Python Course Specifications
- Publisher: Udemy
- Instructor: Start-Tech Academy , Pukhraj Parikh , Abhishek Bansal
- Training level: Beginner to advanced
- Training duration: 7 hours and 40 minutes
Course syllabus as of 2026/9
Prerequisites for the Logistic Regression in Python course
- Students will need to install Python and Anaconda software, but we have a separate lecture to help you install the same
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 1080p
Download link
Rapidgator link
File(s) password: www.downloadly.ir
File size
2.02 GB