Machine Learning (ML) is a transformative field of Artificial Intelligence (AI) that empowers systems to learn from data, make predictions, and adapt to new information without explicit programming. Its integration into real-time applications has revolutionized industries by enabling dynamic, data-driven decision-making and enhanced user experiences.

The program focuses on real-time industry mentorship-drive learning and development with industry use cases to create full-fledged application, utilizing cutting-edge technologies and adhering to industry best practices.

About Open Innovation Squad Program (OISP)

The Open Innovation Squad Program (OISP) is a transformative mentorship-driven learning and development initiative designed to bridge the gap between academic knowledge and industry expectations. This program is meticulously crafted to empower students with cutting-edge technical expertise, hands-on experience solving real-world industry projects and challenges under the personalized mentorship of seasoned industry experts with guaranteed placement support.

Program Benefits

• Skill Development Aligned with Industry Trends
• Exposure to Emerging Technologies and Business Trends
• On-Demand Doubt Session with Mentors
• Live, Personalized Mentorship
• Real-Time Performance Assessment
• Industry-Driven Problem-Solving Hackathons
• Placement and Interview Preparation
• Resume and Personal Branding Mastery
• Guaranteed Job and Internship assistance who successfully complete the program
• Portfolio Building and Certifications
• Access to exclusive placement drives

Program Highlights: What you'll learn and implement with industry usecases

Module 1: Basics of statistics

• Introduction to statistics for ML
• Probability Basics
• Data distributions and Normal Distributions

Module 2: Introduction to ML and data handling

• Exploratory data analysis
• Introduction to ML & Data Pre-processing
• Feature Engineering
• Dimensionality Reduction (PCA, LDA, T-SNE)
• Model Evaluation and Selection

Module 3: Supervised Learning

• Introduction to Supervised Learning
• Linear Regression
• Decision Tree
• SVM
• Logistic regression
• Ensemble Technique
• k-NN

Module 4: Unsupervised Learning

• Introduction to Unsupervised Learning
• K-Means
• Hierarchical
• DBSCAN

Module 5: Model Deployment and Challenges in Real-Time ML Implementation