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Artificial Intelligence Course Training in Bhilai

Learn AI concepts and practical applications in the Certificate Course in Artificial Intelligence and Deep Learning Training in Bhilai.
  • 80 Hours of Intensive Classroom & Online Sessions
  • 100+ Hours of Practical Assignments
  • 2 Capstone Live Projects
  • 100% Job Placement Assurance
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Academic Partners & International Accreditations
  • Artificial Intelligence course with Microsoft
  • AiSPRY
  • Artificial Intelligence course with NASSCOM
  • Artificial Intelligence course with SUNY
  • Artificial Intelligence course with NEF
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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.

Course Fee

Artificial Intelligence Training in Bhilai

The Artificial Intelligence training in Bhilai 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 Bhilai. 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 Bhilai.

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 Bhilai.

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.

AI Training Outcomes in Bhilai

This AI Training in Bhilai 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-centred problems. It has been examining and studying how to incorporate intelligent human behaviours 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 Bhilai

80 hours

Classroom Sessions

artificial intelligence training in Bhilai

100 hours

Assignments

ai course in Bhilai

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 Bhilai

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.

The Perceptron Algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.

  • Neurons of a Biological Brain
  • Artificial Neuron
  • Perceptron
  • Perceptron Algorithm
  • Use case to classify a linearly separable data
  • Multilayer Perceptron to handle non-linear data

Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a Artificial Neural Network.

  • Integration functions
  • Activation functions
  • Weights
  • Bias
  • Learning Rate (eta) - Shrinking Learning Rate, Decay Parameters
  • Error functions - Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.
  • Artificial Neural Networks
  • ANN Structure
  • Error Surface
  • Gradient Descent Algorithm
  • Backward Propagation
  • Network Topology
  • Principles of Gradient Descent (Manual Calculation)
  • Learning Rate (eta)
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Minibatch Stochastic Gradient Descent
  • Optimization Methods: Adagrad, Adadelta, RMSprop, Adam
  • Convolution Neural Network (CNN)
  • ImageNet Challenge – Winning Architectures
  • Parameter Explosion with MLPs
  • Convolution Networks
  • Recurrent Neural Network
  • Language Models
  • Traditional Language Model
  • Disadvantages of MLP
  • Back Propagation Through Time
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Network (GRU)

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 (MLP)
  • Activation functions – Identity Function, Step Function, Ramp Function, Sigmoid Function, Tanh Function, ReLU, ELU, Leaky ReLU & Maxout
  • Back Propagation Visual Demonstration
  • 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? Pros and Cons
  • Practical Implementation of MLP/ANN in Python using Real Life Use Cases
  • Segregation of Dataset - 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 (Xavier, etc.)

Convolution Neural Network 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 with MLPs
  • Convolution Networks - 1D ConvNet, 2D ConvNet, Transposed Convolution
  • Convolution Layers with Filters and Visualizing Convolution Layers
  • Pooling Layer, Padding, Stride
  • Transfer Learning - VGG16, VGG19, Resnet, GoogleNet, LeNet, etc.
  • 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
  • Introduction to the dnn module
    • Deep Learning Deployment Toolkit
    • Use of DLDT with OpenCV4.0
  • OpenVINO Toolkit
    • Introduction
    • Model Optimization of pre-trained models
    • Inference Engine and Deployment process

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
  • 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
  • Frequency-based Word Vectors
  • Count Vectorization (Bag-of-Words, BoW), TF-IDF Vectorization
  • Word Embeddings
  • Word2Vec - CBOW & Skip-Gram
  • FastText, GloVe

Faster object detection using YOLO models will be learnt along with setting up the environment. Learn pretrained models as well as building models from scratch.

 
  • YOLO v3
  • YOLO v4
  • Darknet
  • OpenVINO
  • ONNX
  • 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.

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 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 - Bidirectional vs Deep Bidirectional RNN
  • Deep RNN vs Deep LSTM
 
  • Seq2Seq (Encoder - Decoder Model using RNN variants)
  • Attention Mechanism
  • Transformers (Encoder - Decoder Model by doing away from RNN variants)
  • Bidirectional Encoder Representation from Transformer (BERT)
  • OpenAI GPT-2 & GPT-3 Models (Generative Pre-Training)
  • Text Summarization with T5
  • Configurations of BERT
  • Pre-Training the BERT Model
  • ALBERT, RoBERTa, ELECTRA, SpanBERT, DistilBERT, TinyBERT

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 Autoencoders
    • Intuition
    • Implementation in Keras
  • Variational Autoencoders
    • IntuitionImplementation 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.

 
  • Introduction to 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
  • Advanced Applications of GANS
    • Pix2pixHD
    • CycleGAN
    • StackGAN++ (Generation of photo-realistic images)
    • GANs for 3D data synthesis
    • Speech quality enhancement with SEGAN

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 vs 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
  • Introduction to Sequence to Sequence models and Attention
    • Transformers and it applications
    • Transformers language models
      • BERT
      • Transformer-XL (pretrained model: “transfo-xl-wt103”)
      • XLNet
  • Building a Retrieval Based Chatbot
  • Deploying Chatbot in Various Platforms

Learn the tools which automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem.

 
  • AutoML Methods
    • Meta-Learning
    • Hyperparameter Optimization
    • Neural Architecture Search
    • Network Architecture Search
  • AutoML Systems
    • MLBox
    • Auto-Net 1.0 & 2.0
    • Hyperas
  • AutoML on Cloud - AWS
    • Amazon SageMaker
    • Sagemaker Notebook Instance for Model Development, Training and Deployment
    • XG Boost Classification Model
    • Training Jobs
    • Hyperparameter Tuning Jobs
  • AutoML on Cloud - Azure
    • Workspace
    • Environment
    • Compute Instance
    • Compute Targets
    • Automatic Featurization
    • AutoML and ONNX

Learn the methods and techniques which can explain the results and the solutions obtained by using deep learning algorithms.

 
  • Introduction to XAI - Explainable Artificial Intelligence
  • Why do we need it?
  • Levels of Explainability
    • Direct Explainability
      • Simulatability
      • Decomposability
      • Algorithmic Transparency
    • Post-hoc Explainability
      • Model-Agnostic Algorithms
        • Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
        • Feature relevance explanation
          • SHAP
          • QII
          • SA
          • ASTRID
          • XAI
        • Visual Explanations
  • General AI vs Symbolic Al vs Deep Learning
  • Check out the Deep Learning Interview Questions here.
  • A open-source AutoML framework based on a popular Python library Keras. It allows a non-programmer also to use advanced high-performance DL models with hyperparameter searching. Check out the AutoKeras - A New Revolution into Deep Learning here.

A Large Language Model (LLM) in the context of data science typically refers to advanced natural language processing (NLP) models, which I am based on. These LLMs are designed to understand and generate human-like text, making them useful for a variety of data science tasks.

Generative AI, Diffusion Models, and Prompt Engineering are all related concepts in the field of artificial intelligence and natural language processing. Let me briefly explain each of them:

  • Generative AI
    • Creative Applications
    • Data Augmentation
  • Diffusion Models
    • Realistic Data Generation
    • Applications Beyond Text
    • Prompt Engineering
      • Fine-Tuning for Specific Tasks
      • Mitigating Bias and Ethical Concerns
      • Tailoring to Domain-Specific Contexts

Playgrounds provide a sandbox-like setting where users can test different algorithms, models, and methodologies to gain insights and improve their skills.

DALL-E is a groundbreaking generative model in the field of data science and artificial intelligence, developed by OpenAI. The name "DALL-E" is a combination of the famous artist Salvador Dalí and the robot character WALL-E from the Pixar film.

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Virtual Instructor-led Training (VILT)
Mode of training: Live Online
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  • 365 days of access to online classes
  • Avail Monthly EMI At zero Interest Rate
  • Lifetime validity for LMS acces
  • 20+ live hours of industry masterclasses from leading academicians and faculty from FT top 20 universities
  • Career support services

INR 40,000

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  • Curriculum with industry relevant use cases
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  • Corporate based learning management system with team and individual dashboard and reports

Learners 1079 Learners

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ai with deep learning certification in Bhilai

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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 Bhilai-360DigiTMG!

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FAQs for AI & Deep Learning Course in Bhilai

This artificail intelligence 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 artificail intelligence training completion certificate. After this, he has to enroll for a compulsory internship with AiSPRY. 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 AiSPRY 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 artificail intelligence training 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 Bhilai
Jobs in the field of Artificial intelligence in Bhilai

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 Bhilai for Data Scientist
Salaries in Bhilai 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 Bhilai
Artificial Intelligence Projects in Bhilai

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 Bhilai
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 Bhilai
Modes of training in Artificial Intelligence course

The course in Bhilai 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 Bhilai
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.

Companies That Trust Us

360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.

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360DigiTMG - Artificial Intelligence, Artificial Intelligence Course Training in Bhilai

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