Overview

This course is a curated collection of both Supervised and Unsupervised Machine Learning content.IMPORTANT: This course reuses material from two previous courses available on my Udemy profile. If you have already taken them, note that this is a combined and reorganized version, designed to offer a unified learning experience without switching between separate courses.5. Practical Projects and Real ApplicationsProject 1: A/B Testing on Web TrafficIn this project, you'll work with real web traffic data from a company to evaluate the impact of two page versions on user conversion rates. You will learn to:Clean and explore traffic data using Pandas.Apply statistical tests like the t-test and hypothesis testing to compare conversion rates.Visualize results and make data-driven decisions.Create a report with actionable insights to optimize website performance.Project 2: Bank Data Exploration for Machine LearningIn this practical case, you'll analyze a financial dataset containing bank client information to detect patterns and prepare data for future Machine Learning models. You'll focus on:Data cleaning and transformation using Pandas and NumPy.Visual exploration with Seaborn to identify trends and correlations.Using descriptive statistics to understand client behavior.Preparing the dataset for predictive modeling.Project 3: Insurance Data AnalysisYou will work with insurance company data to identify key factors affecting policy costs and client risk. You will learn to:Clean and transform customer and claim data.Apply EDA techniques to find trends and anomalies.Use descriptive stats and visualizations to support decisions.Prepare data for predictive risk models.Project 4: Malware Data AnalysisIn this project, you will analyze a dataset containing malware information to detect cyberattack patterns and help prevent vulnerabilities. You will focus on:Cleaning and structuring malware data.Visual exploration to identify relevant features.Applying preprocessing techniques to improve data quality.

Skills

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