21 data science portfolio projects in 21 days

Udemy MOOC / Non-credit USD 149.99
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21 data science portfolio projects in 21 days

About this course

This comprehensive data science course is structured as an intensive 21-day journey through the most relevant and in-demand areas of machine learning and artificial intelligence. Each day focuses on a complete project implementation, carefully designed to build both your technical skills and your professional portfolio.The curriculum progresses logically from foundational concepts to advanced applications:**Week 1 (Days 1-7):**- Begin with time series forecasting using ARIMA- Master customer analytics and segmentation- Develop credit risk models- Build social media sentiment analyzers- Create e-commerce recommendation systems- Design employee attrition predictors- Implement real estate pricing models**Week 2 (Days 8-14):**- Develop cybersecurity threat detection systems- Create fraud detection algorithms- Build energy consumption forecasting models- Design traffic flow prediction systems- Calculate customer lifetime value- Analyze stock market patterns- Implement NLP text classification**Week 3 (Days 15-21):**- Conduct market basket analysis- Create health risk prediction models- Build music genre classifiers- Forecast housing market trends- Develop automated trading systems- Master demand forecasting with Prophet- Build AI agents using reinforcement learningEach project utilizes industry-standard tools and frameworks including:- Python programming language- Popular libraries like Scikit-learn, TensorFlow, and PyTorch- Data manipulation tools like Pandas and NumPy- Visualization libraries including Matplotlib and Seaborn- Advanced ML frameworks such as Prophet and NLTKThe course includes:- On-demand video content- Downloadable source code for all projects- Real-world datasets for practical experience<

What you'll learn

  • time series forecasting using ARIMA
  • customer analytics and segmentation
  • credit risk modeling
  • development of sentiment analysis tools
  • creating recommendation systems
  • designing predictive models for various applications
  • using natural language processing for text classification

Skills you'll gain

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