Top 10 Artificial Intelligence Training Institute in Guddadahalli 2019

Artificial Intelligence Training Course, Best Artificial Intelligence Training Institute in Guddadahalli

Artificial-Intelligence-Training

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Anexas is one of the most recommended ARTIFICIAL INTELLIGENCE Training Institute in Guddadahalli that offers hands on practical knowledge / practical implementation on live projects and will ensure the job with the help of advance level ARTIFICIAL INTELLIGENCE Training Courses.

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About Artificial Intelligence Training

Artificial Intelligence (AI) has a long history but is still properly and actively growing and changing.

In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and Tensorflow with Python.

Artificial Intelligence Course Speaker

Experience of starting up Artificial Intelligence training and consulting businesses in more than 6 countries. Trained around 2,50,000 professionals in CPHQ, Lean, Six Sigma (Black belts and Master Black Belts), Process Excellence, AI, Project Management, Accounting, Finance, Blue Ocean Strategy, SPC, Balanced Score Card and Soft Skills.

Total 28 years of experience with 11 years in the manufacturing industry and 17 years in Service industry in various domains like Oil, Banking, manufacturing, IT, ITES, Healthcare, Finance, BPO, Insurance, Shipping, Supply Chain, Logistics, Petrochemicals, Steel, etc.

Read Complete Detail About The Course Trainer Here

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Awards and Recognition

Six Sigma Green Belt Training Awards Six Sigma Green Belt Training Awards
Six Sigma Green Belt Training Awards Six Sigma Green Belt Training Awards
Six Sigma Green Belt Training Awards Six Sigma Green Belt Training Awards

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Course Name: Top 10 Artificial Institute Training Institute in Guddadahalli 2019
Business hours: 10 AM – 7 PM
Opening Days: Monday to Sunday
Training Cost: 10,000 INR
Phone Number: +91 8792857954
Reviews: 5 Star Rating
Category: AI Training

Introduction to Data Science Deep Learning & Artificial Intelligence

Introduction to Deep Learning & AI

Deep Learning: A revolution in Artificial Intelligence

  • Limitations of Machine Learning

What is Deep Learning?

  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining

What is Machine Learning?
Analytics vs Data Science

  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle
  • Advantage of Deep Learning over Machine learning
  • Reasons for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning

Data

  • Basis of Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data & Sources
  • Data Quality & Changes
  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored?

Big Data

  • What is Big Data?
  • 5 Vs of Big Data
  • Big Data Architecture
  • Big Data Technologies
  • Big Data Challenge
  • Big Data Requirements
  • Big Data Distributed Computing & Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem

Data Science Deep Dive

  • What Data Science is
  • Why Data Scientists are in demand
  • What is a Data Product
  • The growing need for Data Science
  • Large Scale Analysis Cost vs Storage
  • Data Science Skills
  • Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Data Acuqisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats
  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File format Conversions
  • Annonymization

Python

  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc.
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Running a Python Script
  • Using Variables
  • Keywords
  • Built-in Functions
  • StringsDifferent Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control.
  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences

Operators and Keywords for Sequences

  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.

Numpy & Pandas

  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs

Statistics

  • What is Statistics
  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python

Introduction

  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques

Clustering

  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study

Implementing Association rule mining

  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

  • How to build Decision trees
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Decision Tree
  • Confusion Matrix
  • Case study

Random Forest Classifier

  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study

Naive Bayes Classifier.

  • Case study

Project Discussion

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Problem Statement and Analysis

  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.

Linear Regression

  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression

Logistic Regression

  • Case study
  • Logistic Regression Overview
  • Data Partitioning
  • Univariate Analysis
  • Bivariate Analysis
  • Multicollinearity Analysis
  • Model Building
  • Model Validation
  • Model Performance Assessment AUC & ROC curves
  • Scorecard

Support Vector Machines

  • Case Study
  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM

Time Series Analysis

  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios
  • Choose the model according to the Time series scenario
  • Implement the model for forecasting
  • Explain working and implementation of ARIMA model
  • Illustrate the working and implementation of different ETS models
  • Forecast the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective model for forecasting
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series forecasting
  • Forecasting for given Time period
  • Case Study

Machine Learning Project

Machine learning algorithms Python

  • Various machine learning algorithms in Python
  • Apply machine learning algorithms in Python

Feature Selection and Pre-processing

  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study
  • Preprocessing
  • Preprocessing Scaling Techniques
  • How to preprocess your data
  • How to scale your data
  • Feature Scaling Final Project

Which Algorithms perform best

  • Highly efficient machine learning algorithms
  • Bagging Decision Trees
  • The power of ensembles
  • Random Forest Ensemble technique
  • Boosting – Adaboost
  • Boosting ensemble stochastic gradient boosting
  • A final ensemble technique

Model selection cross validation score

  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • How to compare machine learning algorithms in practice

Text Mining& NLP

  • Sentimental Analysis
  • Case study

PySpark and MLLib

  • Introduction to Spark Core
  • Spark Architecture
  • Working with RDDs
  • Introduction to PySpark
  • Machine learning with PySpark – Mllib

Deep Learning & AI using Python

Deep Learning & AI

  • Case Study
  • Deep Learning Overview
  • The Brain vs Neuron
  • Introduction to Deep Learning

Introduction to Artificial Neural Networks

  • The Detailed ANN
  • The Activation Functions
  • How do ANNs work & learn
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropogation
  • Understand limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • Building a multi-layered perceptron for classification
  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent

Convolutional Neural Networks

  • Convolutional Operation
  • Relu Layers
  • What is Pooling vs Flattening
  • Full Connection
  • Softmax vs Cross Entropy
  • ” Building a real world convolutional neural network
  • for image classification”

What are RNNs – Introduction to RNNs

  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building a Autoencoder model

Tensorflow with Python

  • Introducing Tensorflow
  • Introducing Tensorflow
  • Why Tensorflow?
  • What is tensorflow?
  • Tensorflow as an Interface
  • Tensorflow as an environment
  • Tensors
  • Computation Graph
  • Installing Tensorflow
  • Tensorflow training
  • Prepare Data
  • Tensor types
  • Loss and Optimization
  • Running tensorflow programs

Building Neural Networks using

Tensorflow

  • Tensors
  • Tensorflow data types
  • CPU vs GPU vs TPU
  • Tensorflow methods
  • Introduction to Neural Networks
  • Neural Network Architecture
  • Linear Regression example revisited
  • The Neuron
  • Neural Network Layers
  • The MNIST Dataset
  • Coding MNIST NN

Deep Learning using

Tensorflow

  • Deepening the network
  • Images and Pixels
  • How humans recognise images
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Overfitting and Regularization
  • Max Pooling and ReLU activations
  • Dropout
  • Strides and Zero Padding
  • Coding Deep ConvNets demo
  • Debugging Neural Networks
  • Visualising NN using Tensorflow
  • Tensorboard

Transfer Learning using

Keras and TFLearn

  • Transfer Learning Introduction
  • Google Inception Model
  • Retraining Google Inception with our own data demo
  • Predicting new images
  • Transfer Learning Summary
  • Extending Tensorflow
  • Keras
  • TFLearn
  • Keras vs TFLearn Comparison

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Why you should join Anexas for ARTIFICIAL INTELLIGENCE Training in Guddadahalli

Top Reasons which makes us best among all others:

  • All our training programs are based on live industry projects.
  • All our training programs are based on current industry standards.
  • Our training curriculum is approved by our placement partners.
  • Training will be conducted on daily & weekly basis and also we can customize the training schedule as per the candidate requirements.
  • Live Project based training with trainers having 15+ years of Industry Experience.
  • Training will be conducted by certified professionals.
  • Our Labs are very well-equipped with latest version of hardware and software.
  • Our classrooms are fully geared up with projectors & Wi-Fi access.
  • 100 % free personality development classes which includes Spoken English, Group Discussions, Mock Job interviews & Presentation skills.
  • You will get study material in form of E-Book’s, Online Videos, Certification Handbooks, Certification Dumps and 500 Interview Questions along with Project Source material.
  • Worldwide Recognized Course Completion Certificate, once you’ve completed the course.
  • Flexible Payment options such as Cheques, EMI, Cash, Credit Card, Debit Card and Net Banking.
  • Schedule Interview with Companies till Placement.

What are the AI job opportunities in Guddadahalli?

There are a lot of companies in Guddadahalli which have now started to use Artificial Intelligence to stay ahead in this competitive market. Some of these companies are listed below.

  • Accenture
  • ZipGo Technologies
  • Hexaware Technologies
  • Crescendo Global Services
  • Decision Minds India Pvt. Ltd.
  • Tech Mahindra
  • Varite India Pvt. Ltd.
  • Amazon, just to name a few

Undergoing Artificial Intelligence training from our institute will open up career opportunities in these organizations.

Who Can Join Our Artificial Intelligence Training In Guddadahalli?

It is essentially demanded from the candidates to meet the following prerequisites and training requirements before applying for our Artificial Intelligence Training in Guddadahalli.

Prerequisites and Requirements

  • Basic knowledge of Python Programming Language
  • Basic Knowledge of Statistics, Probability, Calculus, Linear Algebra

The following professionals, undergraduate and postgraduate students are eligible to join our AI training.

Eligible Candidates

  • Web Developers
  • Software Engineers
  • Software Testers
  • Data Scientists
  • Game Developers
  • System Administrators
  • Network Administrators
  • B.Tech/ M.Tech students
  • B.C.A/ M.C.A students
  • Other aspiring students and IT professionals who want to explore Artificial Intelligence and want to become skilled AI professional.

Artificial Intelligence Training Course Guddadahalli – Reviews

Rama

The Overall Training was nice. The sad part is that I missed almost half of the sessions due to travels. But went through the recorded sessions and completed the project.

Vidya

Quality training and excellent customer service. I highly recommend Anexas trainings..

Ashish

Overall Training was excellent. Trainer was well versed with all the knowledge and delivered it perfectly.

 

Subhash

As per my experience in Anexas, I would like to say that they have very nice course curriculum designed as per market relevance and faculties are best in class…

Ramya

I wanted to build my career in IT. Hats-off to the trainers at the training centre. They provide complete assistance and always ready to guide…

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