Course number : |
SI-2023-207 |
Eligible students to participate : |
1st Year B. Tech, MCA, MSc-DS |
Prerequisites : |
• Sound knowledge of engineering mathematics |
Resource person(s) : |
Industry Expert.
- Prof. Biranchi Narayan Rath
- Prof. Amulya Roul
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Duration of the Course : |
• Sound knowledge of engineering mathematics |
Course Outcome : |
- Proficient Python Programming: Develop proficiency in Python programming for implementing machine learning and deep learning models effectively.
- Solid Foundation: Gain a strong understanding of the fundamental concepts, techniques, and algorithms of machine learning and deep learning.
- Practical Application: Apply supervised and unsupervised machine learning algorithms as well as design, train, and fine-tune neural networks for real-world applications.
- Data Handling and Evaluation: Learn to handle and preprocess real-world datasets, evaluate model performance using appropriate metrics, and make informed decisions based on the results.
- Ethical Considerations and Industry Readiness: Understand the ethical implications of machine learning and deep learning, and be prepared to apply the acquired skills in various industry contexts.
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Course Content : |
- Introduction to Python programming for data science, machine learning and deep learning
- Data handling, manipulation, and visualization using Python libraries
- Supervised learning algorithms and model training
- Unsupervised learning techniques for clustering and dimensionality reduction
- Deep learning fundamentals, neural networks, and architecture design
- Practical implementation of deep learning models using Python frameworks (e.g., TensorFlow)
- Real-time data processing and integration into machine learning pipelines
- Capstone project
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Methodology of Course Delivery : |
- Online Live Class
- Interested students can attend the classes in Hybrid mode
- Doubt Clearing Session.
- Hands-on lab practices
- Sharing of recording of each class
- Sharing of print/on-line materials (slide/videos/text) for referral study.
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Batch Size : |
50-100 |
Course Fees : |
5,000.00/- |
Residence Fee (optional) : |
2,750.00/- |