Course Information
Course Overview
Practice Machine Learning using PYTHON
Welcome to practical Python-based machine learning course! This course is specifically designed to equip you with the skills needed for developing intrusion detection systems using machine learning technology. With a primary focus on the Python programming language and leveraging the scikit-learn (sklearn) library, this course provides a robust foundation for understanding machine learning concepts and their real-world applications.
You will gain expertise in implementing machine learning techniques using the scikit-learn library, delving into profound insights from the Intrusion Detection System dataset, which serves as the primary case study. Throughout the course, you'll develop a deep understanding of machine learning algorithms, data preprocessing, and model evaluation, learning how to apply these concepts effectively in the context of intrusion detection.
Combining structured theory and hands-on labs, this course not only enhances your knowledge of machine learning but also instills confidence to tackle professional challenges. The certificate earned upon completion adds significant value to your profile. Join now to seize better career opportunities in the field of machine learning and become an expert in intrusion detection using Python and scikit-learn.
Important Note: Every codes we will practice in this course can you get on Resources section, find the source code link for every video that contains code
Thank You. <3
Course Content
- 7 section(s)
- 70 lecture(s)
- Section 1 Introduction
- Section 2 Basic Knowledge & Practice
- Section 3 Introduction to Algorithms / Modeling
- Section 4 [Study Case] Intrusion Detection System (practicing with different datasets)
- Section 5 Nice to know (short brief only, no practice)
- Section 6 [BONUS] Case Study Missing data Imputation
- Section 7 Practice makes perfect
What You’ll Learn
- Basic Data Science, Python for Machine Learning, Machine Learning, Hands-on ML, Case Study (Intrusion Detection System), Case Study (Missing Data Imputation)