Before the Interview

AI/ML Interview Preparation Program

80% of Interview Questions come from above Program

Understand the Real-Time life cycle of an AI Engineer, from how he receives the project and how he/she start with the project and up to the final release of AI Project. Experience every step with practical examples followed by Live Projects to place in your resume.

Co-Developed by Senior AI Engineers from INTEL

How we do Career Transition?

4000+

Trained learners

15+ Projects

Build your practical skill

Online

Mode of Learning

15+ Live Projects

We demonstrate 15+ Live Projects which we delivered to our clients across the globe. 

Previous AI/ML Live Interview Clips

We share previous interview audio clips of our recently attended learners to build better confidence before getting into AI/ML Interviews.

Do's & Don't in AI/ML Interview Calls

Call with our trainer before getting into interview. Our trainer guide you on various do's and don'ts before interview and he will review and correct you after the interview. 

3+ Mock Interviews

Real-Life Mock Interviews to evaluate your knowledge on AI/ML & to build confidence before going to your live Interviews.

Best In-Class Resume Constructing

We make our learners to construct best in class AI/ML Resume with professionals standards.(We provide "LIVE PROJECTS" to place in your Resume

GET 80% OF

AI/ML 

INTERVIEW

QUESTIONS

Handholding until you get placed

Our trainer going to help, guide and correct you until you get placed into the role as Data Scientist/AI/ML Engineer. 

Tools Covered

AI Engineer must be flexible with tools and coding. 

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KANTH

Kanth is an Orator & Mentor delivered AI, Machine Learning, Deep Learning, IOT, Industry 4.0 & Digital Twin Customised Training Programs across Dubai, Malaysia, Singapore, South Africa, Sudan etc. He delivered more than 100+ trainings on both sides like client side and vendor side. 

  • YouTube

Watch his journey as AI Engineer

Lead Instructor

Mr. Kanth

Data Scientist | Consultant | Podcaster | Youtuber | Mentor 

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Mr. Kanth is a Data Scientist and Six Sigma Certified. He is a Data Science Consultant for various Top-MNC's like Nokia, EY, Cognizant, BMW etc. He delivered end-to-end AI Solutions using Machine Learning and Deep Learning on Embedded Devices. Built various AI Solutions which impacted financial gains and human effort reduction. 

 

Mr. Kanth is an Orator & Mentor delivered AI, Machine Learning, Deep Learning, IOT, Industry 4.0 & Digital Twin Customised Training Programs across Dubai, Malaysia, Singapore, South Africa, Sudan etc. He delivered more than 100+ trainings on both sides like client side and vendor side. 

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Q & A Session from Recently Placed Participants:

We're trying all the possible ways to make your career transition more simplified with Q & A Session from recently placed participants, to build great exposure towards real-time interviews and how every learner need to prepare for their program as well as for their interviews. We host Q & A Sessions from different learners who got placed recently. 

Q & A Sessions helping every learner to crack their Data Science Interviews or AI Interviews with a more simplified approach. 

Program Syllabus

Best in industry career transition based syllabus built by Senior AI Engineers from Intel, NVIDIA, BOSCH etc which helps learners to build knowledge from zero level to real time working confidence on AI Projects.

Program Syllabus

Introduction to AI/ML Interview Preparation Program

  • Understanding & Evaluating the learner Knowledge on AI/ML/Data Science
  • What companies are expecting from Data Science/AI/ML Candidates?
  • We showcase multiple roles and responsibilities of Data Science/AI/ML job profiles
  • We design a roadmap to achieve those roles and responsibilities with various live projects to work and to place in your resume.



Real-Time Project Lifecycle of AI/ML Engineer

  • 9 Major Components of AI Project
  • Different Teams involved in AI Project Delivery Cycle
  • What is the roles & responsibilites of AI/ML Engineers based on different years of experience?
  • What companies are expecting from different experience level candidates?
  • What is AGILE Methodology?
  • What is AGILE CRISP Methodology and Why it is needed in companies for AI/ML Projects?
  • How to design AI Project Plan, Scope Document, Execution Plan, AI Solution Architecture, Work Breakdown Structure, e.t.c.?
  • In-Depth about AGILE CRISP Methodology and various deliverables.

First Stage of AI/ML Project: Business Problem & Business Understanding

  • Understanding Business Objective of AI Project
  • Background of the business
  • Business Success Criteria of AI/ML Project
  • Resources allocation for AI/ML Project
  • Requirements, Assumptions & Constraints of AI Project
  • Risk & Contingencies of AI/ML Project
  • Cost & Benefit of implementing the Live AI Project
  • Costing for constructing POC on AI Project
  • AI/ML Project Goals
  • AI/ML Project Success Criteria
  • AI/ML Live Project Plan
  • AI/ML Continuous Intergration Architecture.
  • PRACTICAL: Developing Project Charter, Project Plan, Work Break Down Structure, Building Scope & Execution Plan



Second Stage of AI/ML Project: Data Collection & Understanding

  • Understanding different Data Collection Techniques
  • Exploring different situations & challenges while doing data collection in real-time
  • Teams involved in Data Collection Phase in Real-Time
  • KT from the Client after Data Collection
  • Domain Explaination from SME
  • Data Engineer involvement in Data Collection Phase
  • Need to SQL in Data Collection Phase
  • Delivering Initial Data Collection Report
  • Delivering Data Description Report
  • Delivering Data Exploration Report
  • Delivering Data Quality Report
  • How real time clients feedbacks going to look like in Data Collection & Data Understanding Phase?
  • Hands-On Experience in Class
  • Best Practices to follow while doing Data Collection & Data Understanding



Third Stage of AI/ML Project: Data Preparation

  • Different types of Data cleaning & Data reshaping need to performed based on different algorithms we are choosing
  • Rationale for inclusion/Exclusion of Data
  • Dervied Features & Generated Features
  • Merging Data based on Project Requirement
  • Reformatting the data based on the project
  • Dataset Description about features
  • Importance of features
  • Data Cleaning Report
  • Hands-On in class Activity

Fourth Stage of AI/ML Project: Modeling

  • How to choose right technique or algorithm based on the AI Solution?
  • Why math plays a crucial role in choosing the right algorithm for AI/ML Solution?
  • Math behind the Modeling Techniques
  • Modeling Assumptions
  • Model Description
  • Model Test Design
  • Model Assessment
  • Continuous revision of Model Parameters
  • Best Practices while choosing right Modeling technique

Fifth Stage of AI/ML Project: Evaluation

  • Assessment of AI/ML Results with respect to business success criteria
  • Best Approved Models
  • Explaining the Evaluation results with Client
  • Train, Test & Validation set Evaluation and purpose of them
  • List of Possible Action Decision
  • Best practices of Model Evaluation
  • How to choose right evaluation techniques?



Sixth Stage of AI/ML Project: Deployment

  • Different Modes of Deployment
  • Importance of Deployment, Monitoring the Model
  • Deployment vs Release
  • Deployment Plan
  • Mointoring & Maintenance Plan
  • Different Tools & Techniques while doing Deployment
  • AWS Sagemaker Deployment, Heroku Deployment, Mobile Deployment using Tensorflow Lite
  • Plan of Actions from Monitoring & Maintenance and revising the other stages of AI/ML Project
  • Final Report
  • Final Presentation on AI/ML Project
  • Experience Documentation of AI/ML Project
  • Hands-On Live Activity in Class



Interview Preparation Plan

  • How to prepare your resume?
  • Interview Recordings of placed participants
  • How to prepare for interviews?
  • How to speak in interviews?
  • Do's and Don'ts in AI Interviews
  • How to build your linkedin profile?
  • Do's and Don'ts in linkedin profile, naukri profile, dice profile
  • How to sell yourself in interviews?
  • What an interviewer expects from you?
  • Different ways to market your resume
  • Interview Demonstration based on above points
  • Mock interview



One-on-One call consulting until you get placed by our Trainer Mr. Kanth



ANY PROFESSIONAL FROM ANY INDUSTRY WITH ANY EXPERIENCE CAN ENROLL FOR THIS PROGRAM.

YOU RECEIVE INTERNSHIP CERTIFICATE BY COMPLETING EVERY PROJECT PROVIDED.

SYLLABUS & TRAINING FORMAT DESIGNED FOR WORKING PROFESSIONALS AS WELL AS FRESHERS.

WE ASSUME OUR LEARNERS ALREADY COMPLETED DATA SCIENCE/ML PROGA

Best in industry projects to work & to place in resume

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Dedicated team to Build your resume

​

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Mock interviews before attending live interviews

​

Feedback on interviews & support until getting placed as Data Scientist

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One-on-One Program

AI/ML Interview Preparation Program

  • Get AI Engineer Practitioner Certificate

  • Get Live Projects to Place in your Resume

  • Practical Session on Project Charter, Scope, Execution plan, Solution Architecture Design, Documentation, e.t.c.

  • Live Mock & Resume Construction Session

  • Support until you get placed as AI/ML Engineer

Apply Now
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Growth

"BEPEC AI Program was lit 🔥, I'm able to answer every interview question with more confidence only because of real time projects. We builded various AI Solutions as projects. But Mr. Kanth demands more time from every learner and he make every student as practitioner."  

Swapnil - 21LPA

Avg. 10+ Career Transitions Every

Month

Every month we are making our students and participants from different backgrounds and different learning formats we're making them to get placed. We fix every block and hurdle to make your career transition simplified.

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FRESHERS

Majority of freshers are confused with what to do? How to attend interviews and how to crack them? We support them end-to-end until they get placed.

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EXPERIENCED

Making a career transformation with experience is difficult. But we make it possible based on your current experience as reference.

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NON-PROGRAMMERS

Non-technical or non-programmers feels pretty confusing to move into data science career. But we take care of your non-programmer journey to Data Scientist.

Our Alumni Working At

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Most frequently asked questions:

Introduction to AI/ML Interview Preparation Program

  • Understanding & Evaluating the learner Knowledge on AI/ML/Data Science
  • What companies are expecting from Data Science/AI/ML Candidates?
  • We showcase multiple roles and responsibilities of Data Science/AI/ML job profiles
  • We design a roadmap to achieve those roles and responsibilities with various live projects to work and to place in your resume.



Real-Time Project Lifecycle of AI/ML Engineer

  • 9 Major Components of AI Project
  • Different Teams involved in AI Project Delivery Cycle
  • What is the roles & responsibilites of AI/ML Engineers based on different years of experience?
  • What companies are expecting from different experience level candidates?
  • What is AGILE Methodology?
  • What is AGILE CRISP Methodology and Why it is needed in companies for AI/ML Projects?
  • How to design AI Project Plan, Scope Document, Execution Plan, AI Solution Architecture, Work Breakdown Structure, e.t.c.?
  • In-Depth about AGILE CRISP Methodology and various deliverables.

First Stage of AI/ML Project: Business Problem & Business Understanding

  • Understanding Business Objective of AI Project
  • Background of the business
  • Business Success Criteria of AI/ML Project
  • Resources allocation for AI/ML Project
  • Requirements, Assumptions & Constraints of AI Project
  • Risk & Contingencies of AI/ML Project
  • Cost & Benefit of implementing the Live AI Project
  • Costing for constructing POC on AI Project
  • AI/ML Project Goals
  • AI/ML Project Success Criteria
  • AI/ML Live Project Plan
  • AI/ML Continuous Intergration Architecture.
  • PRACTICAL: Developing Project Charter, Project Plan, Work Break Down Structure, Building Scope & Execution Plan



Second Stage of AI/ML Project: Data Collection & Understanding

  • Understanding different Data Collection Techniques
  • Exploring different situations & challenges while doing data collection in real-time
  • Teams involved in Data Collection Phase in Real-Time
  • KT from the Client after Data Collection
  • Domain Explaination from SME
  • Data Engineer involvement in Data Collection Phase
  • Need to SQL in Data Collection Phase
  • Delivering Initial Data Collection Report
  • Delivering Data Description Report
  • Delivering Data Exploration Report
  • Delivering Data Quality Report
  • How real time clients feedbacks going to look like in Data Collection & Data Understanding Phase?
  • Hands-On Experience in Class
  • Best Practices to follow while doing Data Collection & Data Understanding



Third Stage of AI/ML Project: Data Preparation

  • Different types of Data cleaning & Data reshaping need to performed based on different algorithms we are choosing
  • Rationale for inclusion/Exclusion of Data
  • Dervied Features & Generated Features
  • Merging Data based on Project Requirement
  • Reformatting the data based on the project
  • Dataset Description about features
  • Importance of features
  • Data Cleaning Report
  • Hands-On in class Activity

Fourth Stage of AI/ML Project: Modeling

  • How to choose right technique or algorithm based on the AI Solution?
  • Why math plays a crucial role in choosing the right algorithm for AI/ML Solution?
  • Math behind the Modeling Techniques
  • Modeling Assumptions
  • Model Description
  • Model Test Design
  • Model Assessment
  • Continuous revision of Model Parameters
  • Best Practices while choosing right Modeling technique

Fifth Stage of AI/ML Project: Evaluation

  • Assessment of AI/ML Results with respect to business success criteria
  • Best Approved Models
  • Explaining the Evaluation results with Client
  • Train, Test & Validation set Evaluation and purpose of them
  • List of Possible Action Decision
  • Best practices of Model Evaluation
  • How to choose right evaluation techniques?



Sixth Stage of AI/ML Project: Deployment

  • Different Modes of Deployment
  • Importance of Deployment, Monitoring the Model
  • Deployment vs Release
  • Deployment Plan
  • Mointoring & Maintenance Plan
  • Different Tools & Techniques while doing Deployment
  • AWS Sagemaker Deployment, Heroku Deployment, Mobile Deployment using Tensorflow Lite
  • Plan of Actions from Monitoring & Maintenance and revising the other stages of AI/ML Project
  • Final Report
  • Final Presentation on AI/ML Project
  • Experience Documentation of AI/ML Project
  • Hands-On Live Activity in Class



Interview Preparation Plan

  • How to prepare your resume?
  • Interview Recordings of placed participants
  • How to prepare for interviews?
  • How to speak in interviews?
  • Do's and Don'ts in AI Interviews
  • How to build your linkedin profile?
  • Do's and Don'ts in linkedin profile, naukri profile, dice profile
  • How to sell yourself in interviews?
  • What an interviewer expects from you?
  • Different ways to market your resume
  • Interview Demonstration based on above points
  • Mock interview



One-on-One call consulting until you get placed by our Trainer Mr. Kanth



 
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