Course Overview
Data Science and Machine Learning are transforming industries through the power of data. This 4-month residential program offers a hands-on learning experience, starting with the basics of Python and statistics and advancing to machine learning and deep learning. You’ll work with real-world datasets to solve industry-specific problems, developing skills to create AI models that drive business solutions.
The program covers predictive modeling, data visualization, and algorithm optimization, with a focus on practical application. Through projects, case studies, and simulations, you’ll gain a comprehensive understanding of data science and machine learning. By the end, you’ll be ready to apply your knowledge and earn the AWS Machine Learning Certification, setting you up for success in AI and data science.
What You’ll Experience
- Hands-on Learning: Work on real-world datasets and industry-specific projects.
- Collaborative Environment: Participate in group projects, peer reviews, and hackathons.
- Industry Insights: Learn from case studies across healthcare, finance, e-commerce, and more.
- Continuous Monitoring: Track progress with real-time feedback via DOMO ERP.
- Training Tools: Use Jupyter Notebook, Google Colab, and AWS SageMaker for practice.
- Expert-Led Sessions: Receive guidance from industry experts with live feedback.
- Career Prep: Get 30 hours of business communication training and mock interviews.
Syllabus
Python & Statistics Fundamentals
- Python Basics
- Data Handling
- Statistics Essentials
Machine Learning Foundations
- Supervised Learning
- Unsupervised Learning
- Model Evaluation
- Tools for Machine Learning
- Hands-on Projects
Deep Learning & Advanced Machine Learning
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Advanced Machine Learning Models
- Projects
Learning Outcomes
- Ability to develop Generative AI models for text, images, and data analysis
- Proficiency in deep learning frameworks like TensorFlow and PyTorch
- Expertise in AI model deployment and integration into real-world applications
- Understanding of AI ethics, bias mitigation, and responsible AI development
- Strong project portfolio** showcasing AI-powered applications
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