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Artificial Intelligence Course Training in Chennai

Learn AI concepts and practical applications in the Certificate Course in Artificial Intelligence and Deep Learning Training in Chennai.
  • 80 Hours of Intensive Classroom & Online Sessions
  • 100+ Hours of Practical Assignments
  • 2 Capstone Live Projects
  • Receive Certificate from Technology Leader - IBM
  • Receive Certificate from Top University - UTM, Malaysia
  • 100% Job Placement Assurance
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"Alexa, an AI personal assistant that holds 70% of the smart speaker market is expected to add $10 billion by 2021" - (Source). The major thrust of AI is to improve computer functions which are related to how humans try to solve their problems with their ability to think, learn, decide, and work. AI today is powering various sectors like banking, healthcare, robotics, engineering, space, military activities, and marketing in a big way. AI is going to bring a revolution in several industries and there’s a lot of potential in an AI career. With a shortage of skilled and qualified professionals in this field, many companies are coming up and clamouring for the best talent. It is pellucid that AI is rapidly transforming every sphere of our life and is taking technology to a whole new level. It is narrating the ode of new-age innovations in Robotics, Drone Technologies, Smart Homes, and Autonomous Vehicle.

AI & Deep Learning

artificial intelligence course duration in Chennai

Total Duration

3 months

artificial intelligence pre-requisite in Chennai

Prerequisites

  • Computer Skills
  • Basic Mathematical Knowledge
  • Basic Data Science Concepts

Artificial Intelligence Training in Chennai

The Artificial Intelligence training in Chennai introduces you to the concept of AI which is the process of teaching machines to mimic the way humans learn. Automate your key business processes with AI through the certification program on AI and Deep Learning in Chennai. Build Artificial Intelligence systems using Deep Learning and Machine Learning algorithms with the assistance of this Artificial Intelligence Course. Develop AI and Deep Learning solutions using Python libraries. Implement Deep Learning solutions with CNN's and enable Natural Language Processing (NLP) with RNN's. Apply GPUs and TPUs in Deep Learning algorithms. Master the concepts of Artificial Intelligence at 360DigiTMG - the best Artificial Intelligence Course in Chennai.

This Artificial Intelligence Course has been conceived and structured to groom consummate AI professionals. In the initial modules, training is imparted on building AI systems using Deep Learning algorithms. The student learns to run all variants of Neural Network Machine Learning Algorithms. This course enables the students to implement Deep Learning solutions with Convolution Neural Networks and perform Text Analytics and Natural Language Processing (NLP) using Recurrent Neural Networks. The usage of Python libraries, GPUs, and TPUs in solving Deep Learning problems are highlighted in the best Artificial Intelligence Course in Chennai.

What is Artificial Intelligence?

AI is making intelligent computer programs that make machines intelligent so they can act, plan, think, move, and manipulate objects like humans. Due to massive increases in data collection and new algorithms, AI has made rapid advancement in the last decade. It is going to create newer and better jobs and liberate people from repetitive mental and physical tasks. Companies are using image recognition, machine learning, and deep learning in the fields of advertising, security, and automobiles to better serve customers. Digital assistants like Alexa or Siri are giving smarter answers to questions and performing various tasks and services with just a voice command.

What is Deep Learning?

Deep Learning is often referred to as a subfield of machine learning, where computers are taught to learn by example just like humans, sometimes exceeding human-level performance. In deep learning, we train a computer model by feeding in large sets of labeled data and providing a neural network architecture with many layers. In the course of this program, you will also learn how deep learning has become so popular because of its supremacy in terms of accuracy when trained with massive amounts of data.

Course Details

AI Training Outcomes in Chennai

This AI Training in Chennai introduces you to the concept of Artificial Intelligence and Deep learning and helps you in understanding Neural Network Architectures, Supervised and Unsupervised Learning, Decision Tree Learning, and Structuring of Algorithms for new AI machines along with learning to minimize errors through advanced optimization techniques. Artificial intelligence is always compared to human intelligence when it comes to solving complex human-centered problems. It has been examining and studying how to incorporate intelligent human behaviors in a computer.It has been researching on topics related to reasoning, problem solving, machine learning, automatic planning and so on.This course provides a challenging avenue for exploring the basic principles, techniques, strengths, and limitations of the various applications of Artificial Intelligence. Students will also gain an understanding of the current scope, limitations, and societal implications of artificial intelligence globally. They will investigate the various AI structures and techniques used for problem-solving, inference, perception, knowledge representation, and learning. This training will also equip them to design AI functions and components for computer games and analyze the algorithms and techniques used for searching, reasoning, machine learning, and language processing. Also, gain expertise in applying the various scientific methods to models of machine learning.
You will also

Be able to build AI systems using Deep Learning Algorithms
Be able to run all the variants of Neural Network Machine Learning Algorithms
Be able to deal with unstructured data such as images, videos, text, etc.
Be able to implement Deep Learning solutions and Image Processing applications using Convolution Neural Networks
Be able to run practical applications of building AI driven games using Reinforcement Learning and Q-Learning
Be able to effectively use various Python libraries such as Keras, TensorFlow, OpenCV, etc., which are used in solving AI and Deep Learning problems
Learn about the applications of Graphical Processing Units (GPUs) & Tensor Processing Units (TPUs) in using Deep Learning Algorithms

Block Your Time

ai course in Chennai

80 hours

Classroom Sessions

artificial intelligence training in Chennai

100 hours

Assignments

ai course in Chennai

100 hours

Live Projects

Who Should Sign Up?

  • Those aspiring to be Data scientists, or Deep learning and AI experts
  • Analytics managers and professionals, Business analysts and developers
  • Graduates looking for a career in Machine learning, Deep learning or AI
  • Professionals looking for mid-career shift to AI
  • Students entering the IT industry

AI Course Modules in Chennai

This course will be the first stepping stone towards Artificial Intelligence and Deep Learning. In this module, you will be introduced to the analytics programming languages. R is a statistical programming language and Python is a general-purpose programming language. These are the most popular tools currently being employed to churn data for deriving meaningful insights. You will have a high-level understanding of the human brain, the importance of multiple layers in the Neural Network, extraction of features layers wise, composition of the data in Deep Learning using an image, speech, and text. Students will be exposed to GPUs and TPUs that will be used on cloud platforms such as Google Colab to run Google AI algorithms alongside running Neural Network algorithms on-premise GPU machines.

This course will be the first stepping stone towards Artificial Intelligence and Deep Learning. In this module, you will be introduced to the analytics programming languages. R is a statistical programming language and Python is a general-purpose programming language. These are the most popular tools currently being employed to churn data for deriving meaningful insights.

 
  • All About 360DigiTMG & Innodatatics Inc., USA
  • Dos and Don'ts as a Participant
  • Introduction to Artificial intelligence and Deep learning
  • Course Outline, Road Map and Takeaways from the Course
  • Cross-Industry Standard Process for Data Mining
  • Artificial Intelligence and Deep Learning Applications

Different packages can be used to build Deep Learning and Artificial Intelligence models, such as Tensorflow, Keras, OpenCV, and PyTorch. You will learn more about these packages and their applications in detail.

 

Tensorflow and Keras libraries can be used to build Machine Learning and Deep Learning models. OpenCV is used for image processing and PyTorch is highly useful when you have no idea how much memory will be required for creating a Neural Network Model.

 
  • Introduction to Deep Learning libraries – Torch, Theono, Caffe, Tensorflow, Keras, OpenCV and PyTorch
  • Deep dive into Tensorflow, Keras, OpenCV and PyTorch
  • Introduction to Anaconda, R for Windows, R studio and Spyder
  • Environment Setup and Installation Methods of Multiple Packages

Understand the types of Machine Learning Algorithms. Learn about the life cycle and the detailed understanding of each step involved in the project life cycle. The CRISP-DM process is applied in general for Data Analytics /AI projects. Learn about CRISP-DM and the stages of the project life cycle in-depth.

 

You will also learn different types of data, Data Collection, Data Preparation, Data Cleansing, Feature Engineering, EDA, Data Mining and various Error Functions. Understand about imbalanced data handling techniques and algorithms.

 
  • Introduction to Machine Learning
  • Machine Learning and its types - Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning, Transfer Learning, Structured Prediction
  • Understand Business Problem – Business Objective & Business Constraints
  • Data Collection - Surveys and Design of Experiments
  • Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
  • Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
  • Balanced versus Imbalanced datasets
  • Cross-Sectional versus Time Series versus Panel / Longitudinal Data
  • Batch Processing versus Real-Time Processing
  • Structured versus Unstructured vs Semi-Structured Data
  • Big versus Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling Techniques for Handling Balanced versus Imbalanced Datasets
  • Measures of Central Tendency & Dispersion
    • Population Parameters and Sample Statistics
    • Mean/Average, Median, Mode
    • Variance, Standard Deviation, Range
  • Various Graphical Techniques to Understand Data
    • Bar Plot
    • Histogram
    • Boxplot
    • Scatter Plot
  • Feature Engineering - Feature Extraction & Feature Selection
  • Error Functions - Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error, Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross-Entropy
  • High-Level Strategy in Handling Machine Learning Projects

Maximize or minimize the error rate using Calculus. Learn to find the best fit line using the linear least-squares method. Understand the gradient method to find the minimum value of a function where a closed-form of the solution is not available or not easily obtained.

 

Under Linear Algebra, you will learn sets, function, scalar, vector, matrix, tensor, basic operations and different matrix operations. Under Probability one will learn about Uniform Distribution, Normal Distribution, Binomial Distribution, Discrete Random Variable, Cumulative Distribution Function and Continuous Random Variables.

 
  • Optimizations - Applications
  • Foundations - Slope, Derivatives & Tangent
  • Derivatives in Optimization: Maxima & Minima - First Derivative Test, Second Derivative Test, Partial Derivatives, Cross Partial Derivatives, Saddle Point, Determinants, Minor and Cofactor
  • Gradient Descent Method / Optimization - Minima, Maxima & Learning Rate

You will have a high level understanding of the human brain, importance of multiple layers in the Neural Network, extraction of features layers wise, composition of the data in Deep Learning using an image, speech and text.

 

You will briefly understand feature extraction using SIFT/HOG for images, Speech recognition and feature extraction using MFCC and NLP feature extraction using parse tree syntactic.

 

Introduction to neurons, which are connected to weighted inputs, threshold values, and an output. You will understand the importance of weights, bias, summation and activation functions.

 
  • Human Brain – Introduction to neuron
  • Compositionality in Data – Images, Speech & text
  • Mathematical Notations
  • Introduction to ANN
  • Components of ANN - Neuron, Weights, Activation function, Integration function, Bias and Output

Learn about single-layered Perceptrons, Rosenblatt’s perceptron for weights and bias updation. You will understand the importance of learning rate and error. Walk through a toy example to understand the perceptron algorithm. Learn about the quadratic and spherical summation functions. Weights updating methods - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron.

 
  • Introduction to Perceptron
  • Introduction to Multi-Layered Perceptron
  • Activation functions – Identity Function, Step Function, Ramp Function, Sigmoid Function, Tanh Function, ReLU, ELU, Leaky ReLU & Maxout
  • Back Propagation Demo
  • Network Topology – Key characteristics and Number of layers
  • Weights Calculation in Back Propagation

Understand the difference between perception and MLP or ANN. Learn about error surface, challenges related to gradient descent and the practical issues related to deep learning. You will learn the implementation of MLP on MNIST dataset - multi class problem, IMDB dataset - binary classification problem, Reuters dataset - single labelled multi class classification problem and Boston Housing dataset - Regression Problem using Python and Keras.

 
  • Error Surface – Learning Rate & Random Weight Initialization
  • Local Minima issues in Gradient Descent Learning
  • Is DL a Holy Grail?
  • Practical Implementation of MLP/ANN in Python – MNIST, IMDB, Reuters & Boston Housing
  • Segregation of data set: Train, Test & Validation
  • Data Representation in Graphs using Matplotlib
  • Deep Learning Challenges – Gradient Primer, Activation Function, Error Function, Vanishing Gradient, Error Surface challenges, Learning Rate challenges, Decay Parameter, Gradient Descent Algorithmic Approaches, Momentum, Nestrov Momentum, Adam, Adagrad, Adadelta & RMSprop
  • Deep Learning Practical Issues – Avoid Overfitting, DropOut, DropConnect, Noise, Data Augmentation, Parameter Choices, Weights Initialization

Convolution Neural Networks are the class of Deep Learning networks which are mostly applied on images. You will learn about ImageNet challenge, overview on ImageNet winning architectures, applications of CNN, problems of MLP with huge dataset.

 

You will understand convolution of filter on images, basic structure on convent, details about Convolution layer, Pooling layer, Fully Connected layer, Case study of AlexNet and few of the practical issues of CNN.

 
  • ImageNet Challenge – Winning Architectures, Difficult Vision Problems & Hierarchical Approach
  • Parameter Explosion Problem with MLPs
  • Convolution Networks
  • Convolution Layers with Filters
  • Pooling Layer
  • Case Study: Alex Net
  • Practical Issues – Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization

You will learn image processing techniques, noise reduction using moving average methods, different types of filters - smoothing the image by averaging, Gaussian filter and the disadvantages of correlation filters. You will learn about different types of filters, boundary effects, template matching, rate of change in the intensity detection, different types of noise, image sampling and interpolation techniques.

 

You will also learn about colors and intensity, affine transformation, projective transformation, embossing, erosion & dilation, vignette, histogram equalization, HAAR cascade for object detection, SIFT, SURF, FAST, BRIEF and seam carving.

 
  • Introduction to Vision
  • Importance of Image Processing
  • Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories
  • Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations
  • Noise Reduction – Moving Average & 2D Moving Average
  • Image Filtering – Linear & Gaussian Filtering
  • Disadvantage of Correlation Filter
  • Introduction to Convolution
  • Boundary Effects – Zero, Wrap, Clamp & Mirror
  • Image Sharpening
  • Template Matching
  • Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
  • Effect of Noise
  • Laplacian Filter
  • Smoothing with Gaussian
  • LOG Filter – Blob Detection
  • Noise – Reduction using Salt & Pepper Noise using Gaussian Filter
  • Nonlinear Filters
  • Bilateral Filters
  • Canny Edge Detector
  • Non Maximum Suppression
  • Hysteresis Thresholding
  • Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing , Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
  • Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation

Understand the language models for next word prediction, spell check, mobile auto-correct, speech recognition, and machine translation. You will learn the disadvantages of traditional models and MLP. Deep understanding of the architecture of RNN, RNN language model, backpropagation through time, types of RNN - one to one, one to many, many to one and many to many along with different examples for each type.

 
  • Introduction to Adversaries
  • Language Models – Next Word Prediction, Spell Checkers, Mobile Auto Correction, Speech Recognition & Machine Translation
  • Traditional Language model
  • Disadvantages of MLP’s
  • Introduction to State & RNN cell
  • Introduction to RNN
  • RNN language Models
  • Back Propagation Through time
  • RNN Loss Computation
  • Types of RNN – One to One, One to Many, Many to One, Many to Many
  • Introduction to the CNN and RNN
  • Combining CNN and RNN for Image Captioning
  • Architecture of CNN and RNN for Image Captioning
  • Bidirectional RNN
  • Deep Bidirectional RNN
  • Disadvantages of RNN

You will learn to build an object detection model using Fast R-CNN by using bounding boxes, understand why fast RCNN is a better choice while dealing with object detection. You will also learn by instance segmentation problems which can be avoided using Mask RCNN.

 
  • R-CNN
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN

Understand and implement Long Short-Term Memory, which is used to keep the information intact, unless the input makes them forget. You will also learn the components of LSTM - cell state, forget gate, input gate and the output gate along with the steps to process the information. Learn the difference between RNN and LSTM, Deep RNN and Deep LSTM and different terminologies. You will apply LSTM to build models for prediction.

 
  • Introduction to LSTM – Architecture
  • Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh
  • Mathematical Calculations to Process Data in LSTM
  • RNN vs LSTM
  • Deep RNN vs Deep LSTM

Gated Recurrent Unit, a variant of LSTM solves this problem in RNN. You will learn the components of GRU and the steps to process the information.

 
  • Introduction to GRU
  • Architecture & Gates
  • Update Gate, Reset Gate, Current Memory Content
  • Final Memory at current timestep
  • Applications of GRUs

You will learn about the components of Autoencoders, steps used to train the autoencoders to generate spatial vectors, types of autoencoders and generation of data using variational autoencoders. Understanding the architecture of RBM and the process involved in it.

 
  • Autoencoders
    • Intuition
    • Comparison with other Encoders (MP3 and JPEG)
    • Implementation in Keras
  • Deep AutoEncoders
    • Intuition
    • Implementing DAE in Keras
  • Convolutional Auto encoders
    • Intuition
    • Implementation in Keras
  • Variational Autoencoders
    • Intuition
    • Implementation in Keras
  • Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow

You will learn the difference between CNN and DBN, architecture of deep belief networks, how greedy learning algorithms are used for training them and applications of DBN.

 
  • Introduction to DBN
  • Architecture of DBN
  • Applications of DBN
  • DBN in Real World

Understanding the generation of data using GAN, the architecture of the GAN - encoder and decoder, loss calculation and backpropagation, advantages and disadvantages of GAN.

 
  • Generative Adversarial Networks (GANS)
  • Data Analysis and Pre-Processing
  • Building Model
  • Model Inputs and Hyperparameters
  • Model losses
  • Implementation of GANS
  • Defining the Generator and Discriminator
  • Generator Samples from Training
  • Model Optimizer
  • Discriminator and Generator Losses
  • Sampling from the Generator

You will learn to use SRGAN which uses the GAN to produce the high-resolution images from the low-resolution images. Understand about generators and discriminators.

 
  • Introduction to SRGAN
  • Network Architecture - Generator, Discriminator
  • Loss Function - Discriminator Loss & Generator Loss
  • Implementation of SRGAN in Keras

You will learn Q-learning which is a type of reinforcement learning, exploiting using the creation of a Q table, randomly selecting an action using exploring and steps involved in learning a task by itself.

 
  • Reinforcement Learning
  • Deep Reinforcement Learning vs Atari Games
  • Maximizing Future Rewards
  • Policy versus Values Learning
  • Balancing Exploration With Exploitation
  • Experience Replay, or the Value of Experience
  • Q-Learning and Deep Q-Network as a Q-Function
  • Improving and Moving Beyond DQN
  • Keras Deep Q-Network

Learn to Build a speech to text and text to speech models. You will understand the steps to extract the structured speech data from a speech, convert that into text. Later use the unstructured text data to convert into speech.

 
  • Speech Recognition Pipeline
  • Phonemes
  • Pre-Processing
  • Acoustic Model
  • Deep Learning Models
  • Decoding

Learn to Build a chatbot using generative models and retrieval models. We will understand RASA open source and LSTM to build chatbots.

 
  • Introduction to Chatbot
  • NLP Implementation in Chatbot
  • Integrating and implementing Neural Networks Chatbot
  • Generative Chatbot Development
  • Building a Retrieval Based Chatbot
  • Deploying Chatbot in Various Platforms

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    Additional assignments of over 100 hours
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AI & Deep Learning Course Panel of Coaches

ai and deep learning trainer in Chennai

Bharani Kumar Depuru

  • Areas of expertise: Data analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 14+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Chennai and Indian School of Business
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ai and deep learning trainer in Chennai

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine Learning, Business Intelligence and Data Visualization
  • Trained over 1,500 professionals across 12 countries
  • Worked as a Data Scientist for 14+ years across several industry domains
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more
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ai and deep learning trainer in Chennai

Nitin Mishra

  • Areas of expertise: Data Sciences, Machine Learning, Business Intelligence and Data Visualization
  • Over 20+ years of industry experience in Data Science and Business Intelligence
  • Trained professionals from Fortune 500 companies and students at prestigious colleges
  • Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL
  • Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more
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ai with deep learning certification in Chennai
Artificial Intelligence UTM certificate course in Chennai - 360digitmg

Certificate

Win recognition for your AI skills with the Certification Programme in AI and Deep Learning. This Artificial Intelligence and Deep Learning Course empowers you with knowledge that will help you blaze trails in your career. Distinguish yourself among peers and superiors and win recognition with this certificate from the best AI training institute in Chennai-360DigiTMG!

FAQs for AI & Deep Learning Course in Chennai

This course follows a blended learning approach with an intelligent mix of classroom sessions, online sessions and live project experience. The student will spend 60 hours in classroom/online sessions, 100 hours in assignments and 100 hours in live project work.

Once the student completes all his classroom/online sessions and assignments he will receive a Course Completion Certificate. After this, he has to enroll for a compulsory internship with INNODATATICS. Here he will be involved in a live project. Once his project is completed he will receive an Internship Certificate.

The student is expected to attend 80 hours of classroom/online sessions and spend 100 more hours on assignments. On submission of all assignments, he will be awarded a course completion certificate by 360DigiTMG.

After joining, the student can download the course material from our online Learning Management System AISPRY.

The duration of this course is 3 months.

A very good Artificial Intelligence course will enable the student to

  • Use Deep Learning Algorithms to construct AI systems
  • Program and run all variants of Neural Network Machine Learning Algorithms
  • Employ Convolution Neural Networks to implement Deep Learning solutions and Image Processing applications
  • Use Recurrent Neural Networks to analyze sequence data and perform Text Analytics and Natural Language Processing (NLP)
  • Build AI-driven games using Reinforcement Learning and Q - Learning
  • Learn to employ Python libraries such as Keras, TensorFlow, OpenCV to solve AI and Deep Learning problems

The modules contained in this course are:

  • Mathematical Foundations
  • AI and Deep Learning Concepts
  • Machine Learning Primer
  • Deep Learning Primer
  • Python libraries
  • Perceptron Algorithm, Back Propagation Neural Network Algorithm
  • Artificial Neural Network (ANN), Multilayer Perceptron (MLP)
  • Convolution Neural Network (CNN)
  • Recurrent Neural Network (RNN)
  • CNN +RNN variant models
  • Long Short Term Memory (LSTM)
  • Gated Recurrent Unit (GRU)
  • Autoencoder, Restricted Boltzmann Machine (RBM)
  • Deep Belief Network (DBN)
  • Generative Adversarial Network (GANs)
  • Reinforcement Learning and Q learning

The eligibility for this course is a Bachelors's degree in Mathematics/ Statistics/Computer Science/ Data Science or a Bachelors's degree in Engineering ( any discipline). The candidate should also possess knowledge of basic computer technology.

You will receive a mentor as part of this course as soon as you join. If 360DigiTMG feels that you need additional help then you might be assigned more than one mentor.

Most companies in India are hiring for the following roles in AI

  • Data Scientist - AI
  • Artificial Intelligence- Researcher
  • Data Analyst - AI
  • Data Engineer - AI
  • Machine Learning Engineer

A fresh graduate with live project exposure from INNODATATICS can seek employment after this course. He can apply for Machine Learning Engineer, Data Analyst - AI, AI - Programmer roles after finishing this course.

We offer end to end placement assistance to our students. We assist them in resume preparation and conduct several rounds of mock interviews. We circulate their resumes to reputed placement consultants with whom we have a long-standing agreement. Once placed we offer technical assistance for the first project on the job.

We have a tradition of upload free webinars on Youtube. Students can access these webinars from the link given below

All classroom sessions are video recorded and lodged in our Learning Management System AISPRY. If you miss a classroom session you can access the recorded session from the Learning Management System.

Our Learning Management System is title AISPRY. It contains videos of classroom sessions, course material, quiz modules, assignments, program codes, practice data sets and other material needed for the certification examination.

The Neural Network tutorials are video recorded and are stored in our online Learning Management System. You will receive lifetime access to our LMS after joining.

Our participants hail from different industry domains. So our regular program covers applications in all industries to accommodate the learning needs of all participants. We also conduct industry-specific training in the space of Data Science, Machine Learning and AI.

This program structure includes all widely used popular Neural Networks. We also provide training on new Neural Network algorithms.

A minimum of 32GB RAM and 2080ti GPU from Nvidia is most preferred. We will run CNN"s on the cloud so laptop configuration is not a major concern.

To glean the theoretical concepts " The Deep Learning book" should suffice. However, for practical application of concepts studied and programming support our Deep Learning training is most effective. We provide Neural Network webinars to watch before attending regular classes

Jobs in the field of Data Science in Chennai
Jobs in the field of Artificial intelligence in Chennai

This course on Artificial intelligence will open doors to many opportunities and is a viable career option as a Machine Learning Researcher / Engineer, AI Engineer, Data Mining and Analyst, Data scientist, Business Intelligence Developer, and AI Research Scientist.

Salaries in Chennai for Data Scientist
Salaries in Chennai for Artificial Intelligence

In India at entry-level, a candidate with Artificial Intelligence certification can earn around Rs. 8,00,000 per annum and a senior-level expert with 6-7 years of experience can earn between Rs. 70,00,000 -90,00,000 per annum.

Data Science Projects in Chennai
Artificial Intelligence Projects in Chennai

Artificial intelligence is giving computers the sophistication to act intelligently and there are many projects in the field of AI that one can take up to tackle common and complex global challenges in various fields like agriculture, healthcare, robotics, marketing, banking, etc.

Role of Open Source Tools in Analytics in Chennai
Role of Open Source Tools in AI

The various open- source tools in AI technologies that can take your projects to the next level are the Python libraries such as Keras, TensorFlow, OpenCV along with tools like R and Rstudio. These are also used to solve AI and Deep Learning problems.

Modes of Training for Data Science with Python in Chennai
Modes of training in Artificial Intelligence course

The course in Chennai is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.

Industry Application of Data Science in Chennai
Industry Applications of Artificial Intelligence

Industries like healthcare used AI to diagnose and treat medical conditions. The various Chatbots today are powered by AI. Logistics and Transportation also make use of AI to find the quickest shipment route and self-driving vehicles are undoubtedly the next big thing.

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