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Keep shopping21 data science portfolio projects in 21 days
18 students
21 data science portfolio projects in 21 days
Master Machine Learning & AI: From Time Series Analysis to Reinforcement Learning with Real-World Applications
Created byVinay Karode
Last updated 9/2024
English
What you'll learn
- Build and implement various machine learning models for real-world business applications, from time series forecasting to natural language processing
- Master practical data science techniques including customer segmentation, sentiment analysis, and predictive modeling using industry-standard tools
- Develop end-to-end AI solutions for business problems such as fraud detection, product recommendations, and risk analysis
- Apply advanced analytics techniques to create actionable insights from complex datasets across multiple domains (finance, retail, healthcare, etc.)
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Course content
1 section • 21 lectures • 6h 41m total length
Requirements
- Basic understanding of Python programming language
- Familiarity with fundamental mathematical concepts (statistics, probability, and algebra)
- No prior machine learning or AI experience required
- A computer with internet access and ability to install required software packages
- Basic understanding of data structures and algorithms would be helpful but not mandatory
Description
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 learning
Each 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 NLTK
The course includes:
- On-demand video content
- Downloadable source code for all projects
- Real-world datasets for practical experience
- Interactive coding exercises
- Project-based assessments
- Certificate of completion
All materials reflect the latest industry practices and technological advances in the field of data science and machine learning.
Who this course is for:
- Data analysts and business analysts looking to advance their career with AI/ML skills
- Software developers wanting to transition into machine learning and AI
- Business professionals seeking to understand and implement AI solutions in their organizations
- Students and graduates interested in practical applications of AI in business contexts
- nyone interested in learning how to solve real-world problems using machine learning, regardless of their background