Download Free Designs http://bigtheme.net/ Free Websites Templates

Artificial Intelligence Training in Chennai

Softlogic Systems is a leading provider of Artificial Intelligence Training in Chennai. India's Top Rated AI Training Institute offers realtime practical Artificial Intelligence Training with realtime project, job orientation and certification guidance. Master AI, Machine Learning, Deep Learning & Tensorflow. Get hands on exposure.

artificial-intelligence-training-institute-in-chennai

Artificial Intelligence Training in Chennai

Artificial Intelligence is the buzzword nowadays. Moreover, people are showing great enthusiasm to pursue a course in AI. The intelligence demonstrated by machines is something great and everyone finds it appealing. There are several institutes that provide Artificial Intelligence training in Chennai. You have to judge the institutes according to different factors. Softlogic is an established Artificial training center in Chennai that concentrates on quality training.

What is Artificial Intelligence?

The process wherein computers are able to carry out intelligent activities by applying specialized technologies is termed as Artificial Intelligence. Artificial intelligence knowledge is highly needed now and that’s the reason training in this happening concept is required. When machines perform the task just in the way humans do then it is indeed a great task. So instead of postponing, boost your CV manifold by learning AI from Softlogic and eventually get the job of your dreams.

Why Artificial intelligence?

AI’s application can be seen in most of the industries today. The self-driving cars are the result of AI. People are attracted towards AI very much and they take the Artificial Intelligence Course in Chennai seriously.

Artificial Intelligence is based on algorithms. It can also assist in extracting most out of the data. Astonishing accuracy can be achieved through AI.Not only bigger, but also deeper data can be evaluated through AI. Moreover, AI helps in profound discovery from existing data.There is automation of repetitive learning by means of AI.  The icing on the cake is that AI jobs are lucrative.

Course Objectives

This advanced AI training course endeavors to produce highly knowledgeable Artificial Intelligence professionals who can understand the industry demands and meet them. We assist the students to gain thorough understanding of AI’s algorithms and applications.

Career Opportunities for AI Experts

Those who learn AI today will be the individuals that most big companies want to recruit. They also have the chances to outshine other professionals in the field of IT. If you want to be one such individual, then enrolling in Softlogic for an Artificial Intelligence certification course would be fruitful. Some of the high-paying positions in this domain are:

  • Machine Learning Engineer,
  • Research Scientist,
  • Data Scientist,
  • R&D Engineer,
  • Computer Vision Engineer.
  • Business Intelligence Developer etc
How is the AI market scenario in Chennai?

Several professionals are not aware of the importance of AI and hence don’t have expertise on it. This is the reason it is an  in-demand skill nowadays. Moreover, Artificial Intelligence technologies are evolving and centers including Softlogic in Chennai will surely accommodate these trends.

The Artificial Intelligence Training in Chennai is an industry-relevant yet academically-strong program. This will help you to grab the amazing opportunities in AI in Chennai.

Softlogic’s Distinct Training Method for AI

AI is a vast field and there are various branches to it. In order to gain comprehensive knowledge of it, we have divided the course as machine learning and AI. Though Machine Learning is a part of AI, we have dedicated a separate course for it. Therefore, the student can select any one or both.

Who Can Attend?

Candidates should have a minimum of 2 years of experience in working in a technology role. This should comprise atleast 6 months experience in programming.

  • Robotics Engineer
  • Business Analysts
  • Hadoop Developers
  • Data Scientist
  • Candidates who knows Python for Data Science
  • Machine learning professionals wanting to go to the next level.
Prerequisites to Learn Artificial Intelligence Training..?

The AI course is an advanced course and hence the following knowledge would be useful:

  • Machine learning knowledge is compulsory
  • Fundamental Python programming skills are needed.

Don’t hesitate to contact us to know more about Artificial Intelligence training in Chennai. We will guide you in the right path as to how to take maximum advantage of the course.

What do you gain from studying Artificial Intelligence in Softlogic?

When you get trained on AI from Softlogic you can effortlessly set a great career. Our relentless efforts in understanding the possibilities of AI in the world of IT has made us a reliable center for assisting interested candidates learn this skillset. Our trainers are some of the most proficient ones in the industry with deep knowledge and great proficiency in theories. They maintain a student-centric approach of training. This in turn leads to excellent results in the participants.

Our popularity is established by the fact that we have highly relevant courses that meet the industry standards. Artificial Intelligence is one such course and we strive to keep you updated with all the latest applications in this field. We provide great placement support and assist you in getting your dream AI job.

Softlogic’s Artificial Intelligence course in Chennai has been framed to provide each student personal attention from trainers. The one to one attention will ascertain that the participant is given proper training both in theoretical and practical aspects. They will be trained well in the latest trends in the industry. Once the training is complete, we also assist our students to attend interviews and find their dream job.

Develop success in your Artificial Intelligence Career by enrolling in our career-oriented program. Call us for a free demo session now.

Artificial Intelligence Course Fee and Duration

The course fees is moderate and the students have the option to pay it in two installments. For concerns regarding course duration, you can call our educational counsellors.

Track Regular Track Weekend Track Fast Track
Course Duration 45 - 60 Days 8 Weekends 5 Days
Hours 2 hours a day 3 hours a day 6+ hours a day
Training Mode Live Classroom Live Classroom Live Classroom

This is an approximate course fee and duration for RPA Training. Please contact our team for current RPA Training course fee and duration.


Artificial Intelligence Training Course Syllabus

Artificial Intelligence is a changing technology. Hence, the syllabus is framed in such a way that it is updated, fresh and best.

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

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