Full Stack AI Program - 1 Year Internship

Be an AI Engineer in 10-12 Months with #1 Full Stack AI Program

Get Placed as AI Engineer with 10-15 LPA Avg. Salary

100% Job Guaranteed Placement Formula = Job Role Oriented Content + Real-Time Portfolio on Artificial Intelligence

Python, R, Tableau, Power BI, Excel, ML, MLOps, MLFlow, Pycaret, Gradio, FastAPI, NLP, NLTK, Computer Vision, Advanced Deep Learning, Neural Networks, Docker, etc and #BuildExperience on Those Tools with BEPEC Real-Time Projects and Make a Transition as Pro.

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Learners

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Artificial Intelligence Tools

Max Recorded Salary

Days of Internship

The Sky's The Limit

#Build Experience & Bridge the Gap between you & Companies

You are the Product and the Companies are the Consumers, They are in need of Buying the Right Product.

#BuildExperience and Meet the Requirements of Companies.

We Solve Real Problems

BEPEC Stands For BEPECuliar

Stand Out From Crowd

Key Program Highlights

Designed for Working Professionals & Freshers

400+ Hours of Holistic Learning Access for Lifetime

20+ Real-Time Projects to #BuildExperience or Portfolio

1-on-1 Mentorship until you get placed into Job

1-on-1 Mock Interview to #BuildConfidence before you attend interview

Interview Level Deep Statistics, ML Math with Mindmap for easy revision

60 + Industry Projects to #BuildConfidence on AI

Get Course Completion Certificate + Experience Certificate

Learn 30+ Tools related AI Job Profile

Smart Board Driven Classes to create Classroom kinda environment

Top-Notch Training from Working Principal Data Scientists & AI Engineers

AI Engineer Portfolio Crafting, To Shortlist your Resume & LinkedIn

Essential Soft Skills Training, To Master your interviews & Career

Pre & Post Reading Material with Quiz & Assessments

Lifetime Access for Recorded Classes, If you miss any live class

Live Doubt Resolution Classes on Weekdays by Working Data Scientists & AI Engineers

No Cost EMI & Multiple Pricing Options To KickStart your Journey

Live Class Schedule

Live Doubts Class Schedule

Live Class Schedule

Live Doubts Class Schedule

Full Stack Artificial Intelligence- #BuildExperience Program with Internship

Enroll Live Lectures and Get Customised Career Transition Roadmap Call

Course Curriculum

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

What is Data Analytics, DS, AI/ML

02:01:30
Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML

01:26:51

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

Statistics with Real-Time Project Demonstration on EDA

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

End-to-End Data Analysis Using Excel

End-to-End Data Analysis using Power BI & MySQL

End-to-End 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

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

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

Mastering NLP, NLU, NLG

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

AWS Sagemaker Studio

Deep Learning, Tensorflow & Keras

End-to-End Computer Vision

Advanced Deep Learning

Introduction to Deep Learning
Neural Network Fundamental Math Concepts
Different Types of Math Functions
Introduction to Tensorflow & Keras
Introduction to Feed Forward Neural Network
Regression Algorithm using Neural Networks
Classification Algorithm using Neural Network
Back Propagation
Image Recognition using CNN
Image Recognition using Transfer Learning
Math Behind RNN & LSTM
Project Implementation using RNN & LSTM
Audio Processing using LSTM
GANs, RBMs for Advanced Operations
AWS Sagemaker Studio Deployment

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

Interview Preparation

Course Files

Level

All Levels
Last Updated

March 6, 2023
Certificate

Certificate of completion

A course by

kanth@bepec.in

Meet our Mentor Kanth

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 had experience in delivering customizing trainings on Statistics, Cloud Computing, Machine Learning, Deep Learning, Reinforcement Learning
Delivered Trainings Across Dubai, Malaysia, Singapore, South Africa, Sudan etc.
Helped various organisations to implement AI using AI Software Stack & made them to achieve tremendous financial gains
Experience in Delivering 100+ trainings for working Data Scientists and AI Engineers to Upskill them for Future Projects.
Experience in delivering 50+ PMP Programs, ITIL Programs, Agile Programs, etc