AI COURSE BHUBANESWAR

10000+ SATISFIED

PARTICIPANTS

1000+ BEPEC - GLOBAL

CERTIFIED

ARTIFICIAL INTELLIGENCE

PARTICIPANTS

8000+ PLACED ON

AI COURSE IN 

LAST 12MONTHS

5+ LIVE PROJECTS

TO WORK ON

AI, MACHINE LEARNING

100% JOB GUARANTEE,

ELSE MONEY BACK

     ***T&C APPLY***

BEPEC'S ACHIEVEMENTS

AI course is one of the best course which is making a huge impact on current work culture and creating huge amount of opportunities on AI across the globe.

Now Artificial Intelligence been the buzz word across the industries irrespective of domains. Is learning AI course or getting in AI jobs roles is it a challenging task? Really no! but lot of things to learn to get into AI job roles: Skills like machine learning, deep learning, linear algebra, calculus, reinforcement learning, genetic algorithms, search algorithms, markov chains e.t.c. need to be mastered to get into AI job roles.

As a professionals learning AI course may consume more amount of time. So we designed the course in such a way that while they are working they can attend AI training via on weekdays or weekends with more assignments to work for better practice and you learn entire AI for 3 Months. We are able to place nearly 8000+ people on AI from last few years. 

CLASSROOM

120 HOURS OF LIVE MENTOR DRIVEN AI COURSE CLASSROOM TRAINING PROGRAM WITH REAL - TIME PROJECT WORK.

ONLINE

120 HOURS OF LIVE MENTOR DRIVEN AI COURSE ONLINE TRAINING PROGRAM WITH REAL - TIME PROJECT WORK.

E-LEARNING

120 HOURS OF RECORDED SESSIONS WITH 15 HOURS OF TRAINER SUPPORT. REAL TIME PROJECT WORK ON AI COURSE

 

CORPORATE

AI COURSE WILL BE PROVIDED AS PER YOUR REQUIRED TIME AND REQUIRED AGENDA WITH EXPERIENCED TRAINERS

 

ABOUT OUR COURSE:

Artificial intelligence is the main house to create automation using different techniques like Machine learning, Deep learning, Reinforcement learning, logic programming, genetic programming, Markov Chains e.t.c. 

All this different segments in AI are offering intelligence and building automation across different industries like banking, health care, IT, telecom, insurance, manufacturing, oil and gas e.t.c.

Artificial Intelligence Definition: Developing intelligence for computers using human intelligence to automate the process of face recognition, voice recognition, text translation, classification, decision making e.t.c. comes under Artificial Intelligence. In this AI course you would be learning about all the above techniques like Image recognition, speech recognition, text to speech and speech to text e.t.c. things using AI.

AI Applications: Implementation of Artificial intelligence is pretty fast across different domains like cyber security, retail, oil and gas, airlines e.t.c. the common examples of AI which we can see like Self-Driving cars, face unlock, gaming, heart predictions, chatbots e.t.c. are various examples of AI

How to start with AI? Anyone who is interested with AI Job roles can get started with AI Course like Artificial Intelligence classroom or Artificial intelligence online program. But doing self study going to more time and improper learning of concepts. In order to crack job on AI. It's better to get under mentorship. 

Key concepts of AI: People who are planning to get into AI they need to be good at following concepts:

  •  Machine learning

  •  Deep Learning

  •  Reinforcement Learning

  •  Markov Chains and Hidden Markov Chains

  • Genetic Algorithms

  • Search Algorithms

  • NLP

​​

Pre-Requisites of AI: Professionals who are planning to get into AI, below concepts are important for AI Program

  •  Linear Algebra

  •  Calculus

  •  Statistics and Probability

  •  Programming

  •  Partial Derivatives 

Importance of each and every concept: In real time to create intelligence based on two dimensional data like rows and columns and which are in numerical format and structured information we deal with Machine learning in AI. To deal with unstructured info like text we deal with text mining to create intelligence related to text in AI. In order to create intelligence related to recognition of images or faces we use Deep learning. To create intelligence related to sequential tasking we use RNN in deep learning. Building intelligence which are random we use Markov's. So in order to create different types of artificial intelligence we use different techniques to build different sorts of artificial intelligence. 

Agenda

 

Introduction To AI Course


  • 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)




Python


  • 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


  • List and Tuple

  • A Assignment on List, Tuple, Set

  • Set Recap and Dict

  • For loop, While, If, if else, Case, Switch, Escape Sequence




Introduction to Pandas


  • A Assignment on Data Cleaning - Big Data Set

  • Entire Pandas Commands

  • A 10 Mins to Pandas to work on dataset

  • Numpy Commands & Linear Algebra

  • A Assignment on Numpy using Image or 3D data




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




Machine Learning


  • 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 & Learning Assignment

  • Coding Practice and Break Day

  • Deep Math on Logistic Regression & Coding

  • Hypothesis, Confusion Matrix, Classification Report, ROC and AUC

  • Decision Trees, Random Forest Deep Dive

  • Real time project on Decision Tree, Random Forest




Natural Langauge Processing


  • Deep Dive into NLP & Text Mining

  • Tokenization, Lexical Analysis, Semantic Network, DTM, TFIDF, Coreference Matrix etc

  • Word Cloud, Sentiment Analysis




Deep Learning with Tensorflow


  • 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

  • Creating a Docker image and Cloud Deployment

a. Introduction to AWS

b. Exercise 9.1: Configuring AWS instance

c. AWS CLI (Command Line Interface)

d. Introduction to Docker

e. Exercise 9.2: Containerizing a custom Deep Learning Model

  • Reinforcement Learning

  • Q-Learning

  • Frozen Lake

  • Project on Reinforcement Learning

  • Game Building

  • Search Algorithms

  • Nega Max algorithms

  • Knowledge Representation

  • Real time project on Game Building





SCHEDULE:

 

AI COURSE ONLINE  - (3,5TH SEP-2019)

12 WEEKENDS - CLASSROOM TRAINING - 120 HOURS

NO.OF TRAINERS

LAPTOP

ASSIGNMNETS

PLACEMENT ASSURANCE

LIVE PROJECTS

1

REQUIRED

10+ ASSIGNMNETS

YES

5+ LIVE PROJECTS

AI COURSE ONLINE  - (9,13TH SEP-2019)

12 WEEKENDS - CLASSROOM TRAINING - 120 HOURS

NO. OF TRAINERS

LAPTOP

ASSIGNMENTS

PLACEMENT ASSURANCE

LIVE PROJECTS

1

REQUIRED

10+ ASSIGNMENTS

YES

5+ LIVE PROJECTS

AI COURSE ONLINE -  (16,21ST SEP-2019)

120 HOURS OF TRAINING - NEARLY 3 MONTHS 1HR 30MINS/DAY

MORNING

ASSIGNMENTS

LAPTOP

INTERNET

LIVE TRAINER

8.00AM - 11:00AM

10+ ASSIGNMENTS

REQUIRED

REQUIRED

YES

AI COURSE ONLINE -  (23,25TH SEP-2019)

120 HOURS OF TRAINING - NEARLY 3 MONTHS 1HR 30MINS/DAY

MORNING

ASSIGNMENTS

LAPTOP

INTERNET

LIVE TRAINER

8.00AM - 11:00AM

10+ ASSIGNMENTS

REQUIRED

REQUIRED

YES

Our Hiring Partners

 
 
Certification

How do i earn my Machine learning - Best Practices Certification?

 

Pre-Requisites for Certified Machine learning Consultant - Best Practices

 

1. Must complete one project on Machine learning with Project documentation and Source Code

 

2. Need to attend 16 Hours of Best Practices on Machine learning Training from BEPEC Global

 

3. Need to book exam with BEPEC Global

 

4. Must gain 60% to get awarded with CMLD - Best Practices Certification

 

5. Answer sheet going to be corrected by Artificial Neural Networks

6.Aggregated by Independent Global Certification (IGC).

 
Our Services

20+ TIED UP COMPANIES

MOCK INTERVIEWS

JOB SPECIFIC TRAINING

HIGH IN CLASS RESUME BUILDING

100% JOB SUPPORT

LIFE TIME E-LEARNING ACCESS

LIVE PROJECT EXPERIENCE

 

FAQ'S:

What is AI?


Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems




Which programming language is best Python or R?


There are bunch of programming languages like R, Python, Java, Matlab, SAS, Weka, C, C++ e.t.c. But due to huge Machine learning libraries building ML Models made easy with R and Python. If you plan for long run on Machine learning field, it's better to go with Python




What is Machine learning? What is Data Mining?






 
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