Artificial Intelligence Course with Agentic AI
Job Transition with Internship

Learn From Basics To Expert-Level Concepts on AI, Gen AI, ML, Agents

#1 Hands-On Agenda on AI with Projects
Which gets You Hired!

AI, ML, Gen AI, Prompt Engineering & LLMs End-to-End Real-Time Projects & Portfolio Building

End-to-End Real-Time Project using Star Schema Design
End-to-End Data Modelling using Snowflake
Data Ingestion, Data Transformation, Data Storage and Data Analysis – DataOps Pipeline Demonstration
End-to-End Data Science Project with Amazon S3, Snow Flake, Spark & Snow Park
End-to-End Machine Learning Project Using Spark & Microsoft Fabric
AGILE CRISP: End-to-End Machine Learning Project Pipeline
Agile DataOps for Real-Time Credit Risk Scoring and Analysis
MLOps Pipeline: End-to-End Data Science Lifecycle using MLOps
Streaming Analytics: End-to-End NLP Project
End-to-End Computer Vision Project with Deployment
Image Based Generative AI Project using Diffusion Models
Prompt Engineering LLM Project using Zero Shot, Single Shot and Few Shot Learning
Building ChatBot Using LangChain, Amazon BedRock & StreamLit
Building Company Policy LLMs using RAG Architecture, Amazon BedRock & Streamlit
Fine-Tuning LLM using Hugging Face

Introduction to Data Analytics, Data Science, AI, ML Roadmap

Induction Classes

Mastering Python[Zero to Hero]

Introduction to Python

01:02:54
Why Python, Value, Variable, Function, Library[Roadmap on Python]

01:04:10
IDE in Python, Different Data Types

01:01:53
List, Tuple, Set & Dictionary Overview

57:16
Different List Methods

55:48
Different Tuple Methods

01:04:52
Set & Frozenset

01:00:50
Dictionary & String Manipulations

50:56
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda

01:03:15
Types of Operators, Conditional Statements

23:42
While Loop, List Comprehension, Break, Continue, Arguments

01:01:18
Functions, Escape Sequences, Lambda Functions

52:23
Hackathon-1

54:32
Introduction to OOPS

16:16
Instance Variable, Class Variable, Class Method

56:53
Association vs Composition & Aggregation

50:02
Oops Concept

36:29
Encapsulation, Inheritance

01:07:07
Polymorphism, Method OverLoading, Method Overriding

53:59
Introduction to Pandas

01:05:22
Data Analysis using Pandas

01:34:29
Introduction to Numpy

57:26
Different Numpy Commands

01:03:00
Introduction to Data Visualisation

51:41
Data Visualisation using Matplotlib

01:00:20
Data Visualization using Seaborn

59:34
Data Visualization using Plotly

41:44
Why Data Cleaning?

47:05
Data Cleaning with Sklearn & Pandas

49:57
Regular Expression Basics

54:01

Advanced Data Structures and Algorithms

Introduction to Power Bi
Non-Primitive Data Structures
Non-Linear Data Structures
What is an Algorithm
Theory & Code Implementation of Linked List
Stacks & Queues Assignment
Coding Stack Data Structure
Coding Queue Data Structure
Tree Data Structures
Types of Tree Data Structure
Tree Traversal
BFS Traversal
Bubble Sort Theory
Bubble Sort Code Implementation
Selection Sort Theory
Selection Sort Code Implementation
Insertion Sort Theory
Insertion Sort Code Implementation
Merge Sort Theory
Merge Sort Code Implementation
Quick, Merge Sort Performance
Quick Sort Theory
Linear Search & Bisection Search

Applied Statistics

What is Data? Various Types of Data
Levels of Measurement
Categorical Variables & Visualisation Techniques
Continuous Variables and Visualisations
Measures of Central Tendency
Measuring Skewness
Measures of Dispersion
Covariance & Correlation Coefficient
Box & Whisker Plot
Outlier Identification
Treating Outliers
Sampling Techniques
Statistical Analysis Consideration
Types of Analytics
Sample vs Population
Introduction to Inferential Statistics
Law of Large Numbers
Central Limit Theorem
Probability Distributions
Conditional Probability
Continuous Probability Distribution & Discrete Probability Distribution
PMF, CDF, PDF, PPF in Probability
Binomial Distribution
Poisson Distribution
Normal Distribution
Exponential Distribution
Laplace Distribution
Log-Normal Distribution
Standard Error
Confidence Interval Calculations
Z-Table for Confidence Interval
T-Table for Confidence Interval
Hypothesis Testing
Null & Alternate Hypothesis
Type-1 Error & Type-2 Error
2-Sample T Test
ANOVA Test
p-Value Calculations
Chi-Square Test
Project Demonstration using all the Statistical Techniques

Mastering Excel

Introduction to Excel
Basics of Excel Commands
Conditional Formatting in Excel
Different Calculations in Excel
Different Charts in Excel
Pivot Tables in Excel
Slicers in Excel
Filters in Excel
VlookUp, HLookup
Index & Match
Conditional Statements in Excel
SumIF & Count IF
Data Analysis Tool Kit
Forecasting in Excel
Correlation in Excel
Regression in Excel
What-If Analysis
Goal Seek
Data Tables
Scenario Manager
End-to-End Dashboard using Excel
Power Query in Excel

Statistics with Real-Time Project Demonstration on EDA

Real-Time Project Demonstration on Probability Distribution, Hypothesis Testing

Probability Distribution, Random Variable, Binomial, Poisson, Exponential
Normal Distribution
Real-Time Project Demonstration on Probability
Hypothesis Testing Part-1
Hypothesis Testing Part-2
How to Calculate P-Value? Coding Hypothesis Testing
How to solve PDF, CDF, PMF?
Chebyshevs, Log, Power Law, Q-Q, CLT

Real-Time Project Demonstration on Data Analysis Using Excel

Real-Time Project Demonstration on Data Analysis Using Power BI & MYSQL

Real-Time Project Demonstration on Data Analysis Using Tableau

Mastering MySQL

Introduction to MySQL

23:29
Creating Database in MySql

19:23
Deep Dive into MySQL

15:42
SELECT, UPDATE, DELETE Operations

29:03
Clauses in MySql Part-1

18:39
Clauses in MySql Part-2

23:31
Clauses in MySQL Part-3

31:31
MySQL Data Handling Part-1

41:21
MySQL Data Handling Part-2

41:22

Advanced MYSQL

Data Integrity & Referential Integrity
Data Normalisation
First & Second Normal Form
Functional Dependency, Transitive Dependency & 3rd Normal Form
Boyce-Codd Normal Form
Denormalization
Temporary Table, Common Table Expression, Recursive CTE
When to use Temporary Table, CTE & Recursive CTE
Subquery in MySQL
Views in MySQL
Stored Functions
Stored Procedures
Triggers in MySQL
Create Events in MySQL
Different Functions in MySQL

Mastering Tableau[Zero to Hero]

Introduction to Tableau

01:14:02
Basics of Tableau

32:02
Different Types of Charts

30:01
Table Calculations & Parameters

21:51
Waterfall, Pareto Chart, Filters in Tableau

25:12
LOD Expression & Pareto Chart

16:08
MySQL Connection with Tableau

12:10
End-to-End Project using Tableau

19:25
Connect Tableau Desktop with Server

11:35
Tableau Server, Scheduling & Alerts

04:54
Tableau Interview Preparation

07:34

Mastering Power BI[Zero to Hero]

Introduction to Power Bi

04:33
Creating a Bar Plot

09:58
Creating Pie Chart

03:32
Creating Ribbon Chart

02:37
Creating Scatter Plot

04:10
Creating WaterFall Charts

04:41
Creating Funnel Chart

01:59
Creating Line Plot & Area Plot

05:12
Creating Matrix & Conditional Formatting

06:09
Creating Decomposition Tree

02:26
Creating KPI Card

03:13
Creating Gauge Card

04:21
Creating Slicers

03:22
Creating Animated Bar Plot

02:37
Creating Sunburst Chart

02:34
Different Filers in Power BI

04:07
Include & Exclude Operations in Power BI

02:37
Introduction to Power Query

06:11
Groupby & Replace in Power Query

01:52
Merge and Append Operations

02:36
Prefix, Suffix, Length

02:43
Pivot in Power Query

03:51
Introduction to DAX Expressions

03:05
Creating DAX Measures

03:06
Creating DAX Columns

03:06
More DAX Expressions

02:52
Publishing Power BI Visualizations

06:39

Unsupervised Learning with Real-Time Projects

Supervised vs Unsupervised Learning

21:51
Fundamentals of Machine Learning Part-1

32:19
Fundamentals of Machine Learning Part-2

48:41
Insights from Train & Test Accuracy

21:23
Associate Rules Part-1

23:02
Associate Rules Part-2

02:27
Problem Identification & Approach Designing

01:02:13
Associate Rules Scripting

57:15
Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE

01:03:36

Feature engineering Techniques

Feature Scaling Techniques
Normalisation
Standardisation
Min Max Scaler
Robust Scaler
Label Encoding Technique
One-Hot Encoding Technique
Dummies Technique
Ordinal Encoding Technique
Imputation Technique
Feature Selection Techniques
Filter Method: Feature Importance
Filter Method: Fishers Score Technique
Filter Method: Chi-Square Technique
Filter Method: Variance Threshold
Filter Method: Correlation Coefficient
Filter Method: Mutual Information
Wrapper Method: Forward Selection Technique
Wrapper Method: Backward Selection Technique
Wrapper Method: Recursive Feature Elimination Technique
Wrapper Method: Exhaustive Feature Selection Technique
Wrapper Method: Sequential Feature Selection Technique
Embedded Methods: Regularisation
Embedded Method: Elastic Net Regression
Embedded Method: Decision Tree
Outlier Treatment
Over Sampling Technique
Under Sampling Techniques

Supervised Learning Algorithms

How to Prepare Machine Learning Math for Interviews

06:52
Why to use Linear Regression?

06:13
What is Linear Regression

04:42
Case Study on Linear Regression

06:21
Math Behind Linear Regression Part-1

07:03
Math Behind Linear Regression Part-2

06:25
Math behind OLS Technique

04:32
Assumptions of Linear Regression

23:08
Evaluation Metrics for Regression Model

15:50
Accuracy Improving Techniques

14:29
Coding Linear Regression Model
Regularisation Techniques

08:56
Why Logistic Regression

04:27
Math Behind Logistic Regression

17:44
Evaluation Metrics Behind Classification Algorithms

17:10
ROC & AUC Curve

10:20
Coding Logistic Regression Model
Introduction to Decision Tree

06:12
Intuition Behind Decision Tree

07:32
Math Behind Decision Tree

16:03
Math Behind Decision Tree using GINI

04:30
Drawbacks of Decision Tree

05:07
Random Forest and Gradient Boosting

13:08
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset

06:35
Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques

08:47
Coding Feature Selection Techniques
Math Behind KNN

32:21
Math Behind SVM

47:20
Plotting SVM
Introduction to PCA
Math behind PCA
Coding PCA
AutoML Using Pycaret

Probability Distributions, Hypothesis Testing

Probability Distribution, Random Variable, Binomial, Poisson, Exponential

13:07
Normal Distribution

23:00
Real-Time Project Demonstration on Probability

01:34:20
Hypothesis Testing Part-1

13:07
Hypothesis Testing Part-2

05:58
How to Calculate P-Value? Coding Hypothesis Testing

01:15:23
How to solve PDF, CDF, PMF?

10:03
Chebyshevs, Log, Power Law, Q-Q, CLT

22:14

Mastering NLP, NLU, NLG

Pyspark: BigData Processing

Introduction to Spark
Various divisions in Spark
Python with Spark
Pyspark for MLLib
Spark DataFrames
Spark Operations
Grouping, Aggregating and Joining with Spark
Introduction to Machine Learning with PySpark
Machine Learning Pipeline with Spark
Frequent Pattern Mining and Statistics
Feature Transformation with Spark
Data Cleaning & Pre-Processing with Spark
NLP with Pyspark
Spark Structured Streaming
Streaming ML Solutions

MongoDB Mastering

Introduction to Mongo DB
Installation of MongoDB
Basics Operations of MongoDB
Create Operations in MongoDB
Update Operations in MongoDB
Read Operations in MongoDB
Delete Operations in MongoDB
Query & Projection Operations
Python with MongoDB
Spark with MongoDB

Real-Time Projects to Work as Part of Job simulation

Data Analytics Real-Time Project-1
Data Analytics Real-Time Project-2
Data Analytics Real-Time Project-3
Data Analytics Real-Time Project-4
Data Science Real-Time Project-1
Data Science Real-Time Project-2
Data Science Real-Time Project-3
Data Science Real-Time Project-4
Real-Time NLP Project-1
Real-Time NLP Project-2
Real-Time NLP Project-3

ML Deployment & MLOps

End-to-End Deployment Using MLOps & Streamlit

03:11:38
Setup Virtual Environment

04:49
Docker Installation

03:47
How to Activate Virtual Environment

03:03
Docker Desktop

02:51
Regression Model using Pycaret

13:26
Regression Model using Pycaret Part-2

02:10
Interpretability of Model using SHAP

04:46
What is SHAP

01:16
Application Development using Gradio

03:31
Creating API using FASTAPI

03:54
Creating Docker Image

05:29
Model Versioning using Pycaret & MLFlow

06:51
End-to-End ML Proof of Concept

00:00

Advanced Time Series with Real-Time Projects

Azure Machine Learning Engineering

Introduction to Azure Environment
Training & Tuning ML Models using Azure
Working with Data in Azure
Training ML Models in Azure ML
Tuning ML Models in Azure
Azure Automated ML
Deploying & Explaining ML Models
Deploying ML Model for Real-Time Inferencing
Deploying ML Models for Batch Scoring
Responsible AI

AWS Sagemaker Studio

Hadoop

Hadoop MapReduce

HIVE Database

Introduction to Deep Learning & Tensorflow

Building Blocks of Artificial Neural Networks
What are Optimisers and Types of Optimisers
Math Behind Feed Forward Neural Network
Math Behind BackPropagation
Why Activation Functions & Importance
Introduction to Tensorflow
FFNN-Regression Script using Tensorflow
FFNN – Classification Script Using Tensorflow

Feed Forward Neural Network Coding

Computer Vision Mastering Using Tensorflow

Introduction to Object Detection vs Object Classification
Introduction to CNN
Convolution Matrix
Max Pooling Layer
Point-Wise & Element-Wise Convolution
Batch Normalisation
Strides & Padding
Convolution Filters
End-to-End Computer Vision

Transfer Learning OpenCV & YOLO

RNN & LSTM Coding & Math

What is RNN & Why RNN?
Limitations of RNN & Why LSTM
Intuitive Understanding Behind RNN & LSTM
Math Behind RNN
Math Behind LSTMS
Math Behind GRUs
Coding RNN, LSTM, GRU using Tensorflow
Project Demonstration: Forecasting Project using LSTMS

Mastering NLP

Introduction to NLP & NLP Applications
NLP Challenges
NLP Pre-Processing Techniques
NLP Feature Extraction
Project Demonstration: NLP Classification Project
What is N-Gram & Why N-Gram Technique
Coding N-Gram Technique
What is Word2Vec?
Word2Vec Algorithms like CBOW & Skip-Gram
Case Study: Coding Word2Vec Model
What is latent dirichlet allocation? Why LDA
What is Topic Modelling?
Case Study: Coding Topic Modelling

Mastering NLU

What is NLU & Why NLU
What is POS Tagging
Case Study: Coding POS Tagging
What is Dependency Parsing?
What is Constituency Parsing?
Case Study: Coding Dependency & Constituency Parsing
What is NER? & Usage of NER
Case Study: Coding NER
What is Coreference Resolution?
History of NLU Models
Project Demonstration: NLU Classification Model using LSTM
What is Encoder & Decoder Model
Math Behind Encoder-Decoder Models
Case Study: Coding Encoder-Decoder Model
What is Attention Based Models
Math behind Attention Based Models
Case Study: Coding Attention Based Models

Generative AI: Transformer Models BERT, T5, ELMO

Introduction to Transformers
Scaled Dot Product Attention
Multi-Headed Attention
Introduction to Transfer Learning
Introduction to PyTorch
Fine-Tuning Transformers using PyTorch
Introduction to BERT
Wordpiece Tokenisation
Embeddings of BERT
Masked Language Modelling Task
Project Demonstration: Sentence Prediction Task
Types of BERT
BERT for Sequence Classification
BERT for Question & Answering

Generative AI: Natural Language Generation Using GPT

Generative AI: Mastering T5

Generative AI: Vision Transformers

Generative AI: Autoencoders

Generative AI: Image Based Models

What are Generative Models
Math Behind Generative Models
Project Demonstration: GANs
Types of GANs
What are Restricted Boltzmann Machines?
Math Behind Restricted Boltzmann Machines
Project Demonstration: Coding RBMs with Case Study
What is Neural Style Transfer?
Math Behind Neural Style Transfer
Project Demonstration: Coding of NST

Generative AI: Diffusion Models, Energy Based Models & NFMs

Introduction to Diffusion Models
Deep Dive into CLIP Research Paper
Deep Dive into GLIDE Research Paper
Understanding Diffusion Model Theory & Architecture
Project Demonstration on Diffusion Models
Understanding Energy Based Models Theory & Architecture
Project Demonstration on Energy Based Models
Understanding Normalising Flow Models
Project Demonstration on Normalising Flow Models

Generative AI – LLMS: Hugging Face

Prompt Engineering – LLMs: Lang Chains

Introduction to Lang Chains
Using LLMs using Lang Chains
What is Prompt Engineering
Types of Prompt Engineering
Best Way to Improve LLMs Accuracy
Doing Prompt Engineering using Lang Chains
Building RAG Architecture with LangChains
Fine-Tuning LLMs with LangChains
Pre-Training LLMs

MLOps for Transformer Models

Deep Reinforcement Learning

What is Reinforcement Learning
RL versus ML Framework
Why RL?
Examples of RL
Limitations of RL
Terminologies of RL
Q-Learning & Q-Table Theory
Implementation of Q-Learning
Reinforcement Learning Based Q-Learning
Introduction to Rules of Frozen Lake
Implementation of Frozen Lake
Hyper Parameter Tuning
Introduction to SARSA(State-Action-Reward-State-Action)
Implementation of SARSA
Introduction to Deep Reinforcement Learning DQN
Implementation of DQN
Project Demonstration: DQN Project

R Programming

Installation of R Studio

19:42
Basics of R Studio

39:40
Data Visualisation with R Studio

33:25
Data Modelling with R Studio

13:53
Data Joining with R Studio

09:45
Data Visualisation with GGPlot2

11:03
Data Preparation with R Studio

09:39
Data Cleaning with Regexp

34:30
Data Visualisation with Plotly

44:40
test

AWS Sagemaker Deployment & Project

Interview Questions: Frequently Asked Statistics

Introduction to Statistics
Properties of Data
Sample vs Population
Types of Sampling Techniques
Exploratory Data Analysis
Analysing Data with Applied EDA
Measures of Dispersion
Skewness & Normality
Correlation & Scatterplot
Barplot & Lineplot

MindMap for Interview Revision

Data Science Interview Revision Mindmap
Statistics Mindmap
Python Learning Mindmap
SQL Interview Mindmap
Machine Learning Accuracy Improving Techniques Mindmap
Machine Learning Math Mindmap
Real-Time Project Procedure Mindmap
ML Framework Mindmap
Power BI Interview Mindmap
NLP Interview Mindmap
Deep Learning Interview Mindmap

Interview Preparation

Course Files

Handwritten Notes

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Next AI, Gen AI, ML Live Class Starts from 5th May

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AI Career Job Levels	Experience	Pay-Scale(India)
AI Engineer Intern	0-1 Years	2Lakh – 5Lakh/Annum
AI Engineer	1-4 Years	6Lakh – 12Lakh/Annum
Senior AI Engineer	4-7 Years	15Lakh – 30Lakh/Annum
Lead AI Engineer/Architect	7-10 Years	19Lakh – 49Lakh/Annum
Principal AI Engineer	10+ Years	Above 30Lakh/Annum
Director/VP/Head of AI	12+ Years	Above 37Lakh/Annum

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170 million new jobs on AI

21% growth in AI-related careers

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How To Get My Resume Shortlisted?

After completing the AI & Gen AI Internship at BEPEC, Mentor Kanth(1-on-1 Mentorship) will help you with AI roles and responsibilities, technical skills, projects, crafting ATS-friendly job titles, and optimizing your LinkedIn profile and job portals. This guidance will ensure your resume get shortlisted in the job market.

How To Get AI/Gen AI/ML Mock Interviews?

Kanth will conduct 1-on-1 mock interviews covering Artificial Intelligence, Generative AI, MLOps, and Machine Learning to help you identify areas for improvement before your actual interviews. He will also teach you how to use the STAR method, enhancing your communication and persuasion skills for confidently explaining your experience and expertise.

How To Build Experience on AI? (As No Jobs for Freshers)

BEPEC’s job-focused learning program allows you to gain hands-on experience by working on real-time projects. Upon completion, you’ll receive an internship certificate, which can significantly enhance your chances of entering the AI job market.

Artificial Intelligence Course with Internship

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₹7999Excl. GST

400+ Hours of Holistic Learning Content
7+ Project Use-Cases
Life-Time Live Doubt-Solving Access
Life-Time Recording Access
Internship Certificate
9 Months Live Classes
3 Months Remote Internship

₹25999Excl. GST

400+ Hours of Holistic Learning Content
60 Hours of Trainer Led Live Classes
3 Months Remote Internship
12+ End-to-End Real-Time Project Pipelines using AWS, Azure, Snowflake
25+ Capstone Projects
25+ Industry Case-Studies
200+ Guided Hands-On Exercises
45+ Assignments and Mini Projects
1-on-1 Interview Mentorship with Kanth
Resume Building, Cover Letter & ATS Score Optimization
Mock Interviews with Kanth
Life-Time live Doubt Classes Access
Life-Time Recording Access
Internship Certificate

~~₹54499~~ ₹37999Excl. GST

400+ Hours of Holistic Learning Content
200+ Hours of Trainer Led Live Classes
3 Months Remote Internship
12+ End-to-End Real-Time Project Pipelines using AWS, Azure, Snowflake
25+ Capstone Projects
25+ Industry Case-Studies
200+ Guided Hands-On Exercises
45+ Assignments and Mini Projects
1-on-1 Interview Mentorship with Kanth
Resume Building, Cover Letter & ATS Score Optimization
Mock Interviews with Kanth
Life-Time live Doubt Classes Access
Life-Time Recording Access
Internship Certificate

Career Transition Live Classes Starts From

Course	Live Class Timing	Batch Start Date
Full Stack Data Science Career Transition Program with Internship	Live Weekday Classes with Doubt Classes & Life-Time Access For Live-Class Recordings	1746475200 5th May 2025 - 18th November 2025
Full Stack Data Analytics Career Transition Program with Internship	Live Weekday Classes with Doubt Classes & Life-Time Access For Live-Class Recordings	1746475200 5th May 2025 - 31st August 2025
Artificial Intelligence Course: Job Transition with Internship	Live Weekday Classes with Doubt Classes & Life-Time Access For Live-Class Recordings	1746648000 7th May 2025 - 5th January 2026
Data Engineer Career Transition Program with Internship	Live Weekday Classes with Doubt Classes & Life-Time Access For Live-Class Recordings	1747080000 12th May 2025 - 1st September 2025

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AI/Gen AI Syllabus which covers Python, DSA, SQL, Applied Statistics, Excel, Machine Learning, Forecasting, MLOps, Deployment, PySpark, Tensorflow, Computer Vision, LSTMs, Transformers, Diffusion Models, Energy Based Models, NLP, NLU & Deep Learning

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Mock Interviews: Before Getting into the Job Market, Get Corrections from our Mentor on How To Speak in Interviews related to AI/GenAI
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Artificial Intelligence Real-Time Projects to Enhance Your Resume

Participate in BEPEC's real-time projects to gain hands-on experience that employers value. These projects not only deepen your understanding but also strengthen your resume, increasing your chances of getting shortlisted. By showcasing your ability to apply knowledge to practical scenarios, you'll stand out in interviews.

3 Steps in 🚀 Artificial Intelligence Course for Successful Career Transition

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Get Detailed understanding about Job-Ready "T" Skillset

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Develop Confidence on Job-Ready Skillset by Implementing them on Real-Time Projects with BEPEC Internship

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Client Testimonials

I had a great experience with BEPEC Solutions during my career transition. Their team provided clear guidance and personalized support, making the process smooth and stress-free. The career coaches are knowledgeable and genuinely care about their clients' success. Thanks to their resume and interview prep, I landed a job that aligns with my goals. I highly recommend BEPEC Solutions to anyone looking for professional growth and a seamless career change.

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Hi, I feel grateful to be a student of BEPEC. This is genuine and I would say most budget friendly institution to go for any course! Talking about learnings, 5/5. KANT SIR's teaching and patience is just so good to deal with each student. You get the recordings as well. Thanks for the amazing coaching!!

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I have enrolled for the Data Science Career Transition Program through Bepec Kanth explains how to apply all the concepts in real world. Along with developing domain knowledge he is also helping to enhance our professional skills to face real world challenges as data scientist... What ? Why? Where ?.....3 words most important

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About Kanth

Kanth Worked as a Principal Data Scientist with proficiency in Machine learning, Deep Learning, Generative AI, and LLMs, with around 12+ years of involvement in conveying end-to-end project pipelines using AGILE CRISP, DataOps, Big Data Streaming Pipelines, and MLOps Pipelines
Kanth is an AI CoE and Six Sigma Certified. He is an AI Consultant for companies like Nokia, EY, Cognizant, BMW, DU Telecom etc. Kanth delivered AI Solutions using AI Software Stack.
Kanth has experience in delivering customized training on statistics, cloud computing, machine learning, deep learning, general AI, data engineering, data analytics, data science, and reinforcement learning.
Delivered Training Across Dubai, Malaysia, Singapore, South Africa, Sudan etc.
Helped various organisations to implement AI & Gen AI using AI Software Stack & made them achieve tremendous financial gains
Experience in Delivering 100+ pieces of training for working Data Scientists and AI Engineers to Upskill them for Future Projects.
Experience in delivering 50+ PMP Programs, ITIL Programs, Agile Programs, etc
India – EY, Infosys, TechMahindra, ExlSolutions, Suzlon, Wipro, TCS, CSC, HCL, HP, Deloitte, IIT-Bhubaneswar, KIIT Bhubaneswar, R&D Hyundai
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BEPEC Solutions Pvt.Ltd. majorly a consulting and talent upskilling firm. We apply the right technology to solve business problems with the help of Analytics, AI, Cloud Computing, Edge AI, Explainable AI, ML, DL, RPA, e.t.c. across multiple verticals like BFSI, CPG, Retail, Healthcare, Manufacturing, Telecom, Insurance, e.t.c.

Artificial Intelligence Course with Agentic AI Job Transition with Internship

Learn From Basics To Expert-Level Concepts on AI, Gen AI, ML, Agents

#1 Hands-On Agenda on AI with Projects Which gets You Hired!

AI, ML, Gen AI, Prompt Engineering & LLMs End-to-End Real-Time Projects & Portfolio Building

End-to-End Real-Time Project using Star Schema Design

End-to-End Data Modelling using Snowflake

Data Ingestion, Data Transformation, Data Storage and Data Analysis – DataOps Pipeline Demonstration

End-to-End Data Science Project with Amazon S3, Snow Flake, Spark & Snow Park

End-to-End Machine Learning Project Using Spark & Microsoft Fabric

AGILE CRISP: End-to-End Machine Learning Project Pipeline

Agile DataOps for Real-Time Credit Risk Scoring and Analysis

MLOps Pipeline: End-to-End Data Science Lifecycle using MLOps

Streaming Analytics: End-to-End NLP Project

End-to-End Computer Vision Project with Deployment

Image Based Generative AI Project using Diffusion Models

Prompt Engineering LLM Project using Zero Shot, Single Shot and Few Shot Learning

Building ChatBot Using LangChain, Amazon BedRock & StreamLit

Building Company Policy LLMs using RAG Architecture, Amazon BedRock & Streamlit

Fine-Tuning LLM using Hugging Face

Introduction to Data Analytics, Data Science, AI, ML Roadmap

What is Data Analytics, DS, AI/ML

Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML

Induction Classes

What is Data, Importance of Data

What are Mathematical Equations? Why ME?

Fourier Transformation & Fourier Series

What is Probability and Why Probability

What is Geometry, What is Linear Algebra, Eigen Values and Eigen Vectors

What is Calculus

Mastering Python[Zero to Hero]

Introduction to Python

Why Python, Value, Variable, Function, Library[Roadmap on Python]

IDE in Python, Different Data Types

List, Tuple, Set & Dictionary Overview

Different List Methods

Different Tuple Methods

Set & Frozenset

Dictionary & String Manipulations

Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda

Types of Operators, Conditional Statements

While Loop, List Comprehension, Break, Continue, Arguments

Functions, Escape Sequences, Lambda Functions

Hackathon-1

Introduction to OOPS

Instance Variable, Class Variable, Class Method

Association vs Composition & Aggregation

Oops Concept

Encapsulation, Inheritance

Polymorphism, Method OverLoading, Method Overriding

Introduction to Pandas

Data Analysis using Pandas

Introduction to Numpy

Different Numpy Commands

Introduction to Data Visualisation

Data Visualisation using Matplotlib

Data Visualization using Seaborn

Data Visualization using Plotly

Why Data Cleaning?

Data Cleaning with Sklearn & Pandas

Regular Expression Basics

Advanced Data Structures and Algorithms

Introduction to Power Bi

Non-Primitive Data Structures

Non-Linear Data Structures

What is an Algorithm

Theory & Code Implementation of Linked List

Stacks & Queues Assignment

Coding Stack Data Structure

Coding Queue Data Structure

Tree Data Structures

Types of Tree Data Structure

Tree Traversal

BFS Traversal

Bubble Sort Theory

Bubble Sort Code Implementation

Selection Sort Theory

Selection Sort Code Implementation

Insertion Sort Theory

Insertion Sort Code Implementation

Merge Sort Theory

Artificial Intelligence Course with Agentic AI
Job Transition with Internship

#1 Hands-On Agenda on AI with Projects
Which gets You Hired!