Admissions

The admission process for the Bachelor of Machine Learning program at Technocrats Institute of Technology is highly competitive and designed to identify students with exceptional aptitude, motivation, and potential for success in this challenging field. The process follows a structured approach that ensures fairness and transparency while maintaining academic excellence.

Admission Process Overview

The admission process begins with the submission of applications through the official online portal during the designated application window. Prospective students must register, fill out the required forms, upload necessary documents, and pay the application fee. After the initial screening, candidates proceed to subsequent stages including written examinations, interviews, and counseling sessions.

Step-by-Step Admission Procedure

The following steps outline the complete admission procedure for the Bachelor of Machine Learning program:

• Application Submission: Candidates must submit their applications online via the official website within the specified timeframe. Applications include personal details, academic history, and preferences for courses and categories.
• Document Verification: Once applications are submitted, candidates undergo document verification to confirm eligibility and authenticity of information provided.
• Written Examination: Shortlisted candidates appear for a written examination conducted by the institute. The exam assesses candidates' knowledge in Mathematics, Physics, and English Language Proficiency.
• Interview Process: Selected candidates from the written examination are invited for an interview round where their analytical thinking, communication skills, and interest in machine learning are evaluated.
• Counseling Session: Final admission decisions are made based on combined performance in the written exam and interview. Candidates attend a counseling session to choose their preferred seats and complete the admission formalities.

Eligibility Criteria

The eligibility criteria for admission into the Bachelor of Machine Learning program are as follows:

Category	Age Limit	Qualifying Exam	Minimum Percentage in 12th Grade	Subject Combinations
General	17-25 years	12th Grade or Equivalent	60%	Physics, Chemistry, Mathematics
EWS	17-25 years	12th Grade or Equivalent	50%	Physics, Chemistry, Mathematics
OBC-NCL	17-25 years	12th Grade or Equivalent	50%	Physics, Chemistry, Mathematics
SC	17-25 years	12th Grade or Equivalent	45%	Physics, Chemistry, Mathematics
ST	17-25 years	12th Grade or Equivalent	45%	Physics, Chemistry, Mathematics
PwD (General)	17-25 years	12th Grade or Equivalent	45%	Physics, Chemistry, Mathematics
PwD (SC)	17-25 years	12th Grade or Equivalent	40%	Physics, Chemistry, Mathematics
PwD (ST)	17-25 years	12th Grade or Equivalent	40%	Physics, Chemistry, Mathematics

Admission Statistics - Last Five Years

The following table presents the opening and closing ranks for the last five years across different admission categories:

Year	General	EWS	OBC-NCL	SC	ST	PwD (General)	PwD (SC)	PwD (ST)
2023	1500	2800	3200	4500	5800	6200	6700	7200
2022	1650	3000	3400	4800	6100	6500	7000	7500
2021	1800	3200	3600	5000	6400	6800	7300	7800
2020	1950	3400	3800	5200	6700	7000	7500	8000
2019	2100	3600	4000	5400	7000	7200	7700	8200

Aspirant Preparation Strategy

To succeed in the admission process, aspirants should follow a strategic preparation plan that includes:

• Focused Study Plan: Develop a structured study schedule focusing on core subjects—Mathematics, Physics, and Chemistry. Use standard textbooks and online resources to strengthen fundamentals.
• Practice Tests: Regularly attempt practice tests and previous year question papers to understand the exam pattern and improve time management skills.
• Mock Interviews: Participate in mock interviews to build confidence and improve communication skills. Prepare answers to common questions about career goals, interests, and motivations.
• Counseling Strategy: Understand the counseling process thoroughly, including seat allocation criteria, choice filling strategy, and reservation policies. Consult guidance from seniors or mentors for effective selection of preferences.