AI Workshop -

Experience the life of an AI Engineer

(From Basics to Advanced)

Understand the 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 projects to work. 

Co-Developed by Senior AI Engineers from INTEL


Trained learners

40+ Projects

Build your practical skill


Mode of Learning


Learning Duration + Internship

40+ Projects while learning

Internship on AI for 3.5 Months and get the internship certificate.

Connect with Trainer(One-on-One)

Build one POC(Proof of Concept) from specific industry with complete solution with deployment

We believe in connecting our trainers with students via direct call, whatsapp, linkedin and instagram.

2+ Live Projects Demonstration

Starting from Business Case, Project Charter, Agile, AI Architecture, Software Engineering to final Project release.






1 Hackathon on AI

Tools Covered

AI Engineer must be flexible with tools and coding. 

  • YouTube

Watch his journey as AI Engineer

Lead Instructor

Mr. Kanth

Data Scientist | Consultant | Podcaster | Youtuber | Mentor 


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. 

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 Workshop

  • What is AI & Various Branches of AI?
  • Which programming tools are used for AI? Various modes of releasing AI models and their Real time applications
  • Evolution of Artificial intelligence from Rule Based to Artificial Neural Network Based
  • AI Teaching methodologies like Reinforcement Learning, Supervised, Unsupervised & Semi-Supervised Learning
  • Various Artificial Intelligence Algorithms basic intro based on Teaching methodologies
  • Lifecycle of AI Automation & Architecture(ML, DL, Q-Learning etc)


  • Introduction to Python ,IDE, Sypder and Jupyter
  • Installation - Link
  • Different Libraries and their usage

Introduction to List, Tuple, Set, Dict, Scalar, Vector, Matrix, Array, Tensor, DataFrame, Series

  • Introduction to basic data structures like list, tuple, set, dict
  • Assignment on list, tuple, dict and set
  • Working on User-defined functions, conditional statements, Loops
  • Escape Sequences, modules, lambda functions

Introduction to Pandas

  • Entire Pandas Commands
  • 10 Minutes to Pandas
  • Data Preparation and cleaning using Pandas
  • Linear Algebra using Numpy
  • Project work using Pandas and Numpy(Image Applications)

AI Lifecycle: Model Building using Machine Learning & Deployment

  • Linear Regression, Logistic Regression SLR, MLR
  • Correlation, Assumptions of Correlation
  • Linear Regression - Based on two points, OLS
  • Gradient Descent
  • Assumptions of Linear Regression
  • Coding on Linear Regression
  • Deep Math on Logistic Regression & Coding
  • Hypothesis, Confusion Matrix, Classification Report, ROC and AUC
  • Decision Trees, Random Forest, XGBoosting, AdaBoosting and deployment of model to production and understanding their metrics in deployed environment.

Introduction to Tensorflow 2.0

  • Broadcasting in tensorflow
  • Placeholders, Variables, Constants
  • Different API's in Tensorflow
  • Deep Dive in Tensorflow 2.0 & Various components of Tensorflow

Introduction to Matplotlib & Seaborn

  • Deep Dive into Matplotlib & Seaborn
  • Data Understanding using Matplotlib & Seaborn

Introduction to Scikit - Learn

  • Feature Scaling, Feature Selection, Dimensionality Reduction, Imputation, One-Hot encoding
  • Deep Dive into Scikit Learn

AI Lifecycle: NLP, NLU, NLG - Entire Natural Language Handling

  • Natural Language Processing, Natural Langauge Understanding, Natural Language Generator
  • Deep Dive into NLP & Text Mining
  • Tokenization, Lexical Analysis, Semantic Network, DTM, TFIDF, Coreference Matrix etc
  • Word Cloud, Sentiment Analysis
  • Word2Vectors, POS Tagging, Phrase Extraction, Topic Modelling, Audio Translation.

AI Lifecycle: Model Building using Deep Learning(Computer Vision)

  • Neural Networks
a. Introduction to Neural Networks b. Linking Logistic Regression and Neural Network c. Activation Functions d. Multilayer Perceptron e. Matrix View of Neural Network f. Understanding Backpropagation g. Regularization using Dropout, L1 and L2 h. Relu, Leaky Relu and other Activation Layers i. Advanced Loss Functions
  • Convolutional Neural Network Part-I
a. Introduction to CNN b. Understanding 1D and 2D Convolution c. Conv and Pooling Layers in Keras d. Understanding padding and strides e. Softmax and Dropout Layer f. Autoencoders g. Exercise 5.1: 1D Convolution on relevant data set h. Optimizing with batch normalization i. Understanding padding and strides j. Exercise 5.2: Experimenting with different Optimizers on relevant dataset
  • Convolutional Neural Networks – II
a. Training, Fine-tuning and Prediction in case of Pretrained Models b. VGGNet c. GoogleNet d. Residual Networks e. MobileNet Architecture f. Exercise 6.1: Visualizing ResNet-50 g. Exercise 6.2: Using YOLO for real time object detection ​​​​​​​​​​​​​ Introduction to Docker Creating Docker Image and Deployment of Deep Learning Model. a. Introduction to AWS b. Exercise 9.1: Configuring AWS instance, S3 Bucket and Ecosystem, Lamba c. Deploying using AWS Sagemaker

Statistics for AI Engineer

  • What is Data? Data properties? Data patterns? Data Types? Data Trends?
  • Sample & Population
  • Descriptive vs Predictive vs Prescriptive Analytics
  • Exploratory Data Analysis
  • Beta Binomial Distribution, Normal Distribution, Hyper -geometric distribution
  • Skewness & Kurtosis

AI Project Lifecycle: Business Understanding, Data Understanding & Cleaning

Project Demonstration

  • We identify the business problem
  • Construct the AI Architecture
  • Go with data collection plan
  • Data Understanding and report generation
  • Analytics and problem identification via visualizations
  • Cleaning of the data and final report
  • Project Work

Interview Preparation Plan(VIP Members Only)

  • 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

15 Days of one-on-one call consulting(For VIP Members only)

AI Lifecycle: Model Building using Markov Chains

  • What is meant by stochastic process?
  • Why Markov Chains? Why Hidden Markov Chains?
  • Markov Process and Transition Matrix
  • Why integrations with Markov Chains?
  • Advanced time series using Markov Chains
  • Project work using Advanced time series using Markov Chains

AI Lifecycle: Model Building using Reinforcement Learning

  • Reinforcement Learning using Q-Learning
  • Frozen Lake
  • Project on Reinforcement Learning
  • Heuristic Learning for Advanced Automation





AI WORKSHOP_ Experience the life of an A

Weekend Batch(Saturday & Sunday) 

Oct 17th, 2020 - Nov 21st, 2020 @7.30PM - 10 PM(IST) 

AI WORKSHOP - Experience the life of an AI Engineer 

  • Live Sessions

  • Get Life-Time Recording Access

  • Get Script Files

  • Project Files

  • Sample Resumes

AI WORKSHOP_ Experience the life of an A

Weekend Batch(Saturday & Sunday) 

AI WORKSHOP - Experience the life of an AI Engineer 

  • Live Sessions

  • Get Life-Time Recording Access

  • Get Script Files

  • Project Files

  • Sample Resumes

  • Interview Preparation Agenda for VIP members

Oct 17th, 2020 - Nov 28th, 2020 @7.30PM - 10 PM(IST) 

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"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."  

Avg. 10+ Career Transitions Every


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.


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.


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


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

Most frequently asked questions:

AI Workshop, Is it for beginners?

Yes, we designed it for beginners any background, any experience. We start from very basics by considering every learner as a layman.

Why choose this AI Workshop?

If you are struggling with how to start with AI Career, what to learn, what to focus more, what exactly the life of an AI Engineer compared to what you read on the internet, to see every step of AI job clearly, e.t.c.

Why low price?

Our goal with AI workshop is to create awareness for every learner on the right things to learn rather than wasting their time and money on wrong courses and wrong content. To make their AI Job more achievable inlines of what companies are expecting.​​​​​​​

Why internship?

Companies are looking for experienced professionals over learners with certificates on AI. So, to make every learner eligible for job-ready we attached with an internship.

Do I get support after AI Workshop?

Yes, VIP Members get the support after AI workshop.

Do I get the recordings?

Yes, you the recordings and project files after the classes for a lifetime

Do I get any resume building assistance?

Yes, VIP Pass can get this assistance.

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