This beginner-friendly batch is tailored for students and early-stage professionals who want to build a solid foundation in Machine Learning using Python. The course covers core ML concepts including data pre-processing, model selection, training, and performance evaluation, all through guided lessons and real-time practice.A major highlight of this batch is an industry-level project on Financial Transactional Fraud Detection, where participants will work on real datasets to develop, test, and deploy a machine learning model that can detect fraudulent financial transactions. This project offers exposure to practical applications of ML in the finance sector.Key Benefits:Resume Enhancement: Participants can showcase this project on their resume to stand out in Applicant Tracking Systems (ATS) during job and internship applications.Academic Value: The project is suitable for submission as a final-year engineering project or academic coursework in data science or AI-related programs.Internship & Placement Opportunity: High-performing participants may be offered internship or direct placement opportunities within our organization with attractive compensation packages.Practical Skill Building: Gain hands-on experience with Python, scikit-learn, Pandas, and other essential ML libraries and tools.Portfolio-Ready Output: The completed project can be added to personal portfolios, GitHub profiles, and LinkedIn to increase professional visibility.Mentorship & Support: Get continuous support from mentors throughout the course, with feedback and guidance at every step.This batch empowers learners to transition confidently into the world of machine learning and data science, opening doors to academic success and industry opportunities.We suggest joining the batch to get FREE access to the course below. You can also take the course separately by paying the fee, but you won’t be part of the batch where you'll get extra guidance, skill-building support, and performance evaluations.We do not charge for any open source contents provided in this project, rather the registration fees is solely applicable for thorough assessment and evaluation with industry standard guided and systematic establishments
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