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Master Machine Learning: Supervised & Unsupervised Techniques with Real-World Projects

This course provides a comprehensive and organized learning experience in both supervised and unsupervised machine learning. Important: This course incorporates material from two previously successful Udemy courses. If you've taken those courses, this updated version consolidates and reorganizes the content for a streamlined learning path – no need to jump between multiple courses!

Gain Practical Skills with Real-World Projects

This course focuses on hands-on application. You'll work with authentic datasets to develop valuable machine learning skills.

• Project 1: A/B Testing for Web Traffic Analysis

  Dive into real-world web traffic data from a company to evaluate the impact of different page versions on user conversion rates. You will learn to:

  • Clean and explore web traffic data using the powerful Pandas library.
  • 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 improve website performance.

• Project 2: Banking Data Exploration for Machine Learning

  Analyze a financial dataset containing customer banking information to uncover patterns and prepare data for machine learning models. This project emphasizes:

  • Data cleaning and transformation using Pandas and NumPy.
  • Visual exploration with Seaborn to identify trends and correlations.
  • Employing descriptive statistics to understand customer behavior.
  • Preparing data for predictive modeling.

• Project 3: Insurance Data Analysis

  Leverage data from an insurance company to identify key factors influencing policy costs and customer risk. You'll learn to:

  • Clean and transform customer and claims data.
  • Apply exploratory data analysis (EDA) techniques to discover trends and anomalies.
  • Utilize descriptive statistics and visualizations to support decision-making.
  • Prepare data for risk prediction models.

• Project 4: Malware Data Analysis

  Analyze a malware dataset to identify patterns in cyberattacks and contribute to threat prevention. Key skills include:

  • Cleaning and structuring malware data.
  • Visual exploration to identify relevant features.
  • Applying preprocessing techniques to enhance data quality.