0. Projets de Préparation au Machine Learning

Machine Learning: Hands-on Projects & Real-World Applications

This course provides a comprehensive blend of supervised and unsupervised machine learning learning content.

Important: This course combines and reorganizes content from two previous Udemy courses. If you've taken those courses, this offers a unified experience, eliminating the need to navigate between multiple programs.

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5. Practical Projects & Real-World Applications

• Project 1: A/B Testing on Web Traffic

  Dive into real-world web traffic data to assess the impact of different page versions on conversion rates. You'll gain skills in:

  • Data Cleaning & Exploration: Master Pandas for efficient data manipulation.
  • Statistical Testing: Apply t-tests and hypothesis testing to draw meaningful conclusions.
  • Data Visualization: Transform data into insightful visuals for informed decision-making.
  • Actionable Reporting: Create reports with clear, data-driven recommendations to optimize website performance.

• Project 2: Banking Data Exploration for Machine Learning

  Analyze a financial dataset containing customer information to uncover trends and prepare data for machine learning models. You will learn to:

  • Data Transformation: Utilize Pandas and NumPy for data cleaning and manipulation.
  • Data Visualization with Seaborn: Visually explore data to identify correlations and patterns.
  • Descriptive Statistics: Understand customer behavior through key statistical measures.
  • Predictive Modeling Preparation: Prepare data for building effective predictive models.

• Project 3: Insurance Data Analysis

  Examine data from an insurance company to identify key factors influencing policy costs and customer risk profiles. You will:

  • Data Cleaning & Transformation: Clean and transform customer and claim data.
  • Exploratory Data Analysis (EDA): Apply EDA techniques to gain insights.
  • Descriptive Statistics & Visualization: Use statistical measures and visualizations to guide decision-making.
  • Risk Prediction Data Preparation: Prepare data for building predictive risk models.

• Project 4: Malware Data Analysis

  Analyze a malware dataset to identify attack patterns and prevent vulnerabilities. Focus areas include:

  • Malware Data Cleaning & Structuring: Clean and organize malware data effectively.
  • Visual Exploration: Use visual techniques to identify relevant malware characteristics.
  • Data Preprocessing: Apply preprocessing techniques to enhance data quality for analysis.