| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 6 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python TensorFlow Keras Docker Kubernetes |
About the Advanced Data Analysis and Predictive Modeling with Machine Learning Using Python Course
Advanced Data Analysis and Predictive Modeling with Machine Learning Using Python dives deep into Data Analysis And Predictive Modeling With Machine Learning Using Python.
Gain comprehensive expertise through our structured curriculum and hands-on approach.
Program Highlights
• Comprehensive coverage of Advanced Data Analysis and Predictive Modeling with Machine Learning Using Python from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Data Science
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Python, TensorFlow, Keras, Docker
• Career-oriented training for academic and professional growth in Data Science
Course Curriculum
Module 1: AI Fundamentals, Mathematics, and Data Analysis
- Apply linear algebra and calculus concepts to machine learning problems
- Analyze datasets using statistical methods and data visualization techniques
- Develop mathematical models to describe complex data relationships
Module 2: Data Engineering, Preprocessing, and Feature Pipelines
- Design and implement data pipelines using Python and relevant libraries
- Configure data preprocessing techniques to handle missing values and outliers
- Evaluate the effectiveness of feature engineering methods on model performance
Module 3: Model Architecture, Algorithm Design, and Machine Learning Methods
- Implement deep learning architectures using TensorFlow and Keras
- Analyze the trade-offs between different machine learning algorithms and models
- Develop ensemble methods to improve model accuracy and robustness
Module 4: Training, Hyperparameter Optimization, and Evaluation
- Configure hyperparameter tuning using grid search and random search methods
- Evaluate model performance using metrics such as accuracy, precision, and recall
- Develop strategies to prevent overfitting and improve model generalizability
Module 5: Deployment, MLOps, and Production Workflows
- Deploy machine learning models using Docker and Kubernetes
- Design and implement monitoring and logging systems for model performance
- Develop workflows to automate model retraining and deployment
Module 6: Ethics, Bias Mitigation, and Responsible AI Practices
- Analyze the ethical implications of machine learning models on society
- Develop strategies to mitigate bias in machine learning models and datasets
- Evaluate the transparency and explainability of machine learning models
Module 7: Industry Integration, Business Applications, and Case Studies
- Apply machine learning concepts to real-world business problems and case studies
- Develop solutions to integrate machine learning models with existing business systems
- Evaluate the return on investment (ROI) of machine learning projects and initiatives
Tools, Techniques, or Platforms Covered
Python TensorFlow Keras Docker Kubernetes
Real-World Applications
- Apply Advanced Data Analysis and Predictive Modeling with Machine Learning Using Python skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Data Science competencies
- Solve industry-relevant problems using Advanced Data Analysis and Predictive Modeling with Machine Learning Using Python methodologies and tools
- Contribute to open-source projects and collaborative research in Data Science
- Prepare for competitive examinations, interviews, and professional certifications in Data Science
Who Should Attend & Prerequisites
- Designed for Professionals.
- Designed for Students.
- Working experience with artificial intelligence tools and prior coursework in related topics expected.
- Mentorship by industry experts and NSTC faculty.
Certification
