AI COURSE - VIJAYAWADA

10000+ SATISFIED

PARTICIPANTS

10000+ BEPEC - GLOBAL

CERTIFIED

ARTIFICIAL INTELLIGENCE

PARTICIPANTS

8000+ PLACED ON

AI COURSE IN 

LAST 12MONTHS

35+ LIVE PROJECTS

TO WORK ON

AI, MACHINE LEARNING

100%

JOB ASSURANCE ON

AI COURSE

BEPEC'S ACHIEVEMENTS

AI 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 6 Months. We are able to place nearly 8000+ people on AI from last few years. 

CONGRATULATIONS

“D Manish- Learning AI is one my dream role. But with zero knowledge enrolled for AI and within 7-8 Months got placed as AI Engineer. Trainer made me more confident on each and every concept” 

— BEPEC TEAM

ONLINE

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

E-LEARNING

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

 

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

 

Linear Algebra for AI Course


  • Matrix, Array, Data Frame, Tuple, List e.t.c.
  • Matrix Multiplication
  • Matrix Addition
  • Matrix Subtraction
  • Transpose of the Matrix
  • Identity Matrix
  • Inverse Matrix
  • Tensor or multi dimensional array
  • Scalar Multiplication
  • Vector Operations
  • Definition of Vector and Scalar




Introduction to Python for AI


  • Introduction to python
  • Value, Variable, Functions and libraries
  • Installation of Anaconda
  • Setting up python environment using Spyder
  • Introduction to basic data types and basic python commands
  • Deep into List, Tuple, Set and Dict
  • Working on Pandas
  • Working on Numpy
  • Working on Scipy
  • Working on Matplotlib
  • Working on Seaborn
  • Working on Scikit - Learn
  • Working on Logical Functions, User Defined Functions e.t.c.




Calculus for AI, Probability for AI


  • What is Calculus?
  • Why is it important in AI?
  • Derivatives
  • Partial Derivatives
  • Chain Rule
  • Importance Partial Derivatives
  • Difference between normal derivatives vs partial derivatives.
  • Various derivatives
  • Rules in Partial derivatives
  • Purpose of Probability
  • Types of Probability.
  • What is conditional probability?




Machine learning Algorithms for AI


  • Introduction to Machine learning
  • Importance of Machine learning
  • How computer are programmed to make decisions using Machine learning
  • What is Machine learning?
  • What is Supervised and Unsupervised Learning?
  • What is the Data mining and Machine learning?
  • Applications of Machine learning
  • Introduction to Regression, Classification and Forecasting
  • Linear Regression: In this chapter we learn deep about what is linear Regression, What is correlation? What is meant by residual? How to use scatter plot? Deep into Simple linear Regression and Multiple Linear Regression e.t.c.
  • Machine learning Project -1: Real -time Project on Linear Regression
  • Logistic Regression: In this chapter we learn deep about what is logistic Regression, How we use logistic Regression for classification? Mechanism of Logistic Regression, What is real number constant? What is sigmoid curve? Confusion matrix and classification report e.t.c.
  • Various Application of Logistic Regression
  • Machine learning Project -2:Real time project work on Logistic Regression.
  • Decision Tree: In this chapter you learn deep about tree based model, how decision tree going to work. What is the mechanism of Decision tree? What is information gain? What is gini index? What are various validation techniques of Decision Tree? How to choose right function? Disadvantages of Decision tree e.t.c.
  • Various Applications of Decision Tree
  • Machine learning Project -3: Real Time Project on Decision Tree
  • Bagging and Boosting: In this chapter we learn deep about what is Bagging and Boosting, How we use bagging and boosting techniques for classification? Mechanism of bagging and boosting, What are the advantages of Bagging and Boosting? What is meant by parallel classifier and sequential classifier? Purpose of learning rate and iterations e.t.c.
  • Machine learning Project -4: Real time project work on Bagging and Boosting
  • Naive Bayes: In this chapter you learn deep about bayes classifiers what is meant by Naive? Why naive bayes is important? When to choose naive bayes? Where to use naive bayes? Importance of Conditional probability, when to use pdf?
  • Applications of Naive Bayes
  • Machine learning Project -5: Real Time Project on Naive Bayes
  • K-NN: In this chapter we learn deep about what is KNN Algorithm? Mechanism of KNN, Based on which function KNN is working? What is elbow curve? Purpose of Elbow curve? How to choose right K Value? When to use KNN? Does KNN going to learn?
  • Applications of KNN Algorithm
  • Machine learning Project -6: Real time project work on KNN Algorithm
  • Neural Network: In this chapter you learn deep about biological neuron vs artificial neuron. How artificial neuron is created? What is cost function? What is meant by hyperparameters? Purpose of learning rate. How to choose right activation function? What is meant by forward and backward?
  • Real time applications of Neural network
  • Machine learning Project -7: Real Time Project on FNN
  • Support Vector Machines: In this chapter we learn deep about what is SVM algorithm? How to build linear SVM and non-linear SVM. How to choose right hyper plane? Advantages of SVM over other algorithms.
  • Applications of SVM Algorithm
  • Machine learning Project -8: Real time project work on SVM Algorithm
  • Text mining with NLP: In this chapter you learn deep about text mining and NLP. What is meant by token? What is meant by corpus? What is meant by DTM? When to choose NLP Mechanism? Building Word clouds, Sentiment analysis e.t.c.
  • Real time applications of NLP
  • Machine learning Project -9: Real Time Project on Text mining and Natural language processing
  • Working on 3+ Unsupervised learning Algorithms like K - Means, Anomaly Detection, Associates Rules, Market Basket Analysis e.t.c.




Deep learning Introduction for AI


  • Introduction to Neural Networks
  • Different Types of neural networks
  • Various parameters before building neural network
  • Biological neuron vs artificial neuron
  • Architecture of neural network
  • Different layers in Neural Network
  • Deep dive into Optimizers
  • Deep Dive into Activation Functions
  • Deep Dive into Loss Functions
  • Deep Dive into Batch Size, Iterations, Learning Rate e.t.c.
  • Feed Forward and Back Propagation.
  • Building First Neural Network on Tensorflow
  • Various basic commands in Tensorflow
  • Building Feed forward using Tensorflow
  • Project on Feed Forward Neural Network
  • Understanding Architecture of CNN
  • Building a CNN Algorithm on Tensorflow and Keras
  • Real Time Project on CNN
  • Introduction to OpenCV
  • Various advantages of OpenCV for CNN
  • Understanding Architecture of RNN, LSTM and GRU's
  • Build RNN Algorithm on Tensorflow and Keras
  • Real Time Project on RNN's
  • Working on Speech recognition and text translation using RNN's
  • Understanding Architecture on Autoencoders
  • Different Types of Autoencoders
  • Building Autoencoders on Tensorflow and Keras
  • Real-Time Project on Autoencoders
  • Understanding Markov Chains and Hidden Markov Chains
  • Building Markov Chains and Hidden Markov Chains using Tensorflow
  • Real Time project on Markov chains and hidden Markov chains
  • Understanding Genetic Algorithms
  • Build a Genetic Algorithm using tensorflow and Keras
  • Understanding Search Algorithms
  • Building different types of Search Algorithms using python
  • Understanding Reinforcement Learning
  • Making CNN's or any models into Reinforcement Learning
  • Understanding the architecture of Reinforcement Learning
  • Getting started with Tensorflow




Tools you Master in AI Course


  • Numpy
  • Pandas
  • Scipy
  • Scikit - Learn
  • NLTK
  • Spacy
  • Matplotlib
  • Seaborn
  • Tensorflow
  • Keras
  • Tensorboard
  • OpenCV e.t.c.





SCHEDULE:

 

AI COURSE ONLINE  - (17,21ST SEP-2019)

22 WEEKENDS - CLASSROOM TRAINING - 300 HOURS

NO.OF TRAINERS

LAPTOP

ASSIGNMNETS

PLACEMENT ASSURANCE

LIVE PROJECTS

2

REQUIRED

80+ ASSIGNMNETS

YES

35+ LIVE PROJECTS

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

22 WEEKENDS - CLASSROOM TRAINING - 300 HOURS

NO. OF TRAINERS

LAPTOP

ASSIGNMENTS

PLACEMENT ASSURANCE

LIVE PROJECTS

2

REQUIRED

80+ ASSIGNMENTS

YES

35+ LIVE PROJECTS

AI COURSE ONLINE -  (2,5TH SEP-2019)

300 HOURS OF TRAINING - NEARLY 5 MONTHS 1HR 30MINS/DAY

MORNING

ASSIGNMENTS

LAPTOP

INTERNET

LIVE TRAINER

8.00AM - 11:00AM

80+ ASSIGNMENTS

REQUIRED

REQUIRED

YES

AI COURSE ONLINE -  (7,9TH SEP-2019)

300 HOURS OF TRAINING - NEARLY 5 MONTHS 1HR 30MINS/DAY

MORNING

ASSIGNMENTS

LAPTOP

INTERNET

LIVE TRAINER

8.00AM - 11:00AM

80+ ASSIGNMENTS

REQUIRED

REQUIRED

YES

Reviews

Best for all those analytical courses at various locations....and different mode of trainings....Best Planned Strategies

Amarendra Pendyala

I can say that was my one of great experience. I have taken two courses from bepec and i'm extremely satisfied with the trainer support and management services..

Vijay Pradeep

I took ML courses in BEPEC, they crafted course structure which can easily understand to common person

Presence of Bepec help me lot while learning and the guidelines and materials provided by Bepec is much helpful to me.Thank You

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?






 
Podcasts
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).

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Excellent faculty, i got trained from two trainers with real time examples and virtual lab helped me out!

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