Artificial intelligence Training

Best Artificial intelligence Training in Chennai


Troy Infotech is providing the best Artificial intelligence training in Chennai. Our certified Artificial intelligence experts are providing the practical sessions in a highly efficient manner.The Artificial intelligence training course in Chennai transforms you to the students’ level to the experts’ level in Artificial intelligence. The training is given by one of the best certified Artificial intelligence professionals.

 

Our Artificial intelligence training center in Chennai will transform you as the expertise in Artificial intelligence.The training classes are conducted according to the convenience of the students. It includes both weekdays and weekend classes.

 

Our trained students in Artificial intelligence are placed as professionals in TOP MNC’s.Join Artificial intelligence course at Troy Infotech and become AI professionals. Troy Infotech, the No.1 Artificial intelligence Training Institute providing the excellent Artificial Intelligence Training in Chennai with placement for students.

 

Course Description


Artificial Intelligence (AI) has a very long history but is still properly and actively growing and changing. In this course, given by our training center you will learn the basics of modern Artificial Intelligence (AI) as well as some of the representative applications of AI such as Data Science, Machine Learning, Deep Learning, Artificial Neural Networks, Statistics, Restricted Boltzmann Machine (RBM) and Tensorflow with Python. Along with that, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which actually continues to expand human capability beyond our imagination.

 

Troy Infotech’s Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.

 

Course Objective


Troy Infotech covers all the in-depth & advanced AI career oriented concepts in the training which mainly include:

Learning the working on the concepts of building an intelligent computer system
Students will get the leverage skills in understanding the potential areas where AI can be implementations can be made
Helps to build knowledge in the concepts of AI debugging
Helps in applying the regression, clustering, classification, recommender systems and else deep learning
Makes the students to get hands on experience in working with AI real-time case studies and industry projects
Students will get the Real-Time Industry-Based Projects

Artificial Intelligence Training In Chennai Course Syllabus:


Introduction to Data Science Deep Learning with 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 of TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study

Implementing Association rule mining

  • What is Association Rules with 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 & its Variable Importance
  • Case study

Naive Bayes Classifier.

  • Case study

Project Discussion

Problem Statement and Analysis

  • What are the 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

  • Describing Time Series data
  • Formatting your Time Series data
  • Listing the different components of Time Series data
  • Discussing different kind of Time Series scenarios
  • Choosing the model according to the Time series scenario
  • Implementing the model for forecasting
  • Explaining working and implementation of ARIMA model
  • Illustrating the working and implementation of different ETS models
  • Forecasting the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualizing the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identification of 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
  • Application of 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
  • A study on 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
  • Build a real world convolution neural network for image classification

What are RNNs – Introduction to RNNs

  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • Python’s Long short term memory neural networks lstm

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
  • How to retrain Google Inception with our own data demo
  • Predicting new images
  • Transfer Learning Summary
  • Extending Tensorflow
  • Keras
  • TFLearn
  • Keras vs TFLearn Comparison

 

Take A Look At Our Artificial Intelligence Training Course


Real-Time Practical Training

You will get the real-time project experience on our training. We provide more practical training classes preferred by the candidates. Our practical oriented training will inspire all the participants.

Assured Placement Assistance

We are the pioneer in providing the assured placement assistance for the participants after their course completion.

Certified Trainers

Learning made easy by our certified trainers who fulfills the needs of the candidates in that particular course. Your knowledge will be transformed into expertise level by our trainers.

Internship Training

We provide internship training effectively for our participants which are highly helpful for their professional career.

Enroll Now

Related Courses

Frequently Asked Questions


Who can attend this Artificial Intelligence course?

The following can attend this Artificial Intelligence course:

  • Well-suited for management and non-technical participants
  • Students who want to learn Artificial Intelligence
  • New comers who are not familiar with AI or its implications

Why this Artificial Intelligence Course ?

  • Artificial intelligence is a driving force for changing humanity by helping individuals and businesses create innovative and challenging products and services, make critical decisions and achieve key goals.
  • Rapid Code development is seen in AI. This increases its usage in top industries across various business Verticals.
  • 6 million jobs are going to be created in the AI field in the coming decade and so, more job opportunities are available.
  • You will get High salary Jobs in this domain.

What will you learn?

You will learn & become expert in the following concepts:

  • Developing, analyzing and integrated for artificial intelligence application and IOT systems.
  • Demonstrate skills to create and design natural language processing systems.
  • Demonstrate designing and creating for machine learning systems using best practices and patterns.
  • Demonstrate original and new data in deep learning by consuming big data with original algorithms.
  • Demonstrate software development skills using more than one programming language, platform, development environment, and source control system.
  • Describe for develop, databases, analyze, integrate data structures, and database management systems.

What are the Career Opportunities available in Artificial Intelligence?

The following are the job opportunities you will get:

  • Lead Artificial Intelligence
  • Senior Software Developer-Artificial Intelligence
  • Deep Learning Developer-Artificial Intelligence
  • Chatbot Developer-Artificial Intelligence
  • Software Tester-Artificial Intelligence
  • Researcher-Artificial Intelligence

What Are The Pre-Requisites For Learning the Artificial Intelligence Course?

The topics included in this topic will be related to linear algebra and probability theorem. So, a basic knowledge of mathematics and statistics is an added advantage to take up this Machine learning course.

TROY Course Duration For Artificial Intelligence Training In Chennai

  • Fast Track Training Program (6+ hours daily)
  • Regular Classes (Morning, Day time & Evening)
  • Weekend Training Classes (Saturday, Sunday & Holidays)

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