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.
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 SelfDriving 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
PreRequisites 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

KNN: 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 nonlinear 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

RealTime 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:
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?
What is Machine learning? What is Data Mining?
Podcasts
Certification
How do i earn my Machine learning  Best Practices Certification?
PreRequisites 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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Avinash
Daily assignments helped to grab more topics on tensorflow
Satya
Great guidance and support from the trainer
Ranvish
Best job support from BEPEC
Sundar
Excellent faculty, i got trained from two trainers with real time examples and virtual lab helped me out!
Pranav
Training was topnotch, highly dedicated trainers until you get placed.