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AI & Deep Learning Course Training in USA

Build AI applications with Deep Learning algorithms and get set for a career as an AI expert.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 80 Hours of Intensive Live Online Sessions
  • 100+ Hours of Practical Assignments
  • 2 Capstone Live Projects
  • Job Placement Assistance
artificial intellignce course review in malaysia - 360digitmg
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artificial intelligence course review in malaysia - 360digitmg
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Academic Partners & International Accreditations
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  • Artificial Intelligence & Deep Learning Course with Microsoft

"The size of the North American market for Artificial Intelligence will be $29,000 million by 2025." - (Source). Huge investments and interest in Artificial Intelligence are expected to increase in the long run in the near future. As per McKinsey predictions, about 15% of vehicles will be fully automated in 2030. The number of startups based on AI in the US has increased by 114% between 2015 to 2018. By the latest advancements in Deep Learning, AI is being extensively used for search engines, virtual assistants, online translators, and many sales and marketing decisions. Tesla and Audi manufactured semi-autonomous cars and are working to improve to reach into full automated cars. AI is rapidly being adopted by many sectors and there is a demand for AI professionals.

AI & Deep Learning

artificial intelligence course duration in singapore- 360digitmg

Total Duration

2 Months

artificial intelligence course pre-requisite in singapore- 360digitmg


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

Artificial Intelligence Training Overview

The objective of this offering is to provide not only a conceptual understanding of the deep learning skills but also the practical applications in marketplace scenarios prevailing in the USA. This course serves as a perfect launchpad for professionals with an appreciation of statistics and knowledge of programming languages such as Python, R and RStudio into a career of AI and Deep Learning. Students will learn how to build AI applications, understand the ever-evolving neural network architectures, create AI algorithms, and minimize errors through advanced optimization techniques. By successfully graduating from this course, they will be able ready for careers in computer vision related image processing domains.

Artificial Intelligence Course Outcomes

The field of Artificial Intelligence is morphing into an unstoppable force that the US-based companies are looking to capitalize on. This provides a tremendous opportunity for professionals to get into this market and command handsome salaries. The demand for AI professionals has grown by 29% from 2018 to 2019 and the average salary could be well over $100,000. This alone should be a prime motivating factor for professionals on the edge about whether to begin their careers in this hot domain. 360DigiTMG’s much researched and backed by industry experts, Artificial Intelligence training for students in the USA ensures that participants become seasoned practitioners in dealing with both structured and unstructured data. From this training, the students will learn concepts of Deep learning algorithms and Natural language processing.

Build AI systems using Deep Learning algorithms
Analyse sequence data and perform text analytics and Natural Language Processing using Recurrent Neural Networks
Implement Deep Learning solutions and Image Processing applications using Convolution Neural Networks
Process and analyse unstructured data such as images, videos and text
Use various Python libraries such as Keras, TensorFlow and OpenCV to solve AI and Deep Learning problems
Apply graphical processing units in using Deep Learning algorithms

Block Your Time

ai course

80 hours

Live Online Sessions

artificial intelligence training

100 hours


ai course

100 hours

Live Projects

Who Should Sign Up?

  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

AI & Deep Learning Training Modules

360DigiTMG has instructors that are industry-leading experts in Machine Learning, Data Science, and related technologies. Artificial Intelligence and Deep Learning training course in the USA is delivered by 360DigiTMG. The main objective of this training is to provide a workforce and bridge the gap between business needs and talent. The module of the course is designed comprehensively by industry stalwarts. The module introduces Artificial Intelligence and Deep learning in neural networks. Students will learn Python libraries that include Keras, Tensor Flow, and OpenCV. The modules also cover different algorithms like perceptron and backpropagation. Learn Images processing and Computer vision data which is quite different from the regular tabular data and needs to be handled in a particular manner. Students also learn LSTMs that are particular types of RNNs. They have something called a ‘memory’ cell which ‘remembers’ information as it flows through the network. Hence this makes LSTMs best for forecasting type problems or any use case that has a temporal component. The modules also explain deeply about Reinforcement and Q-learning. This is different from supervised and unsupervised learning, which uses a concept called reinforcement. It builds upon the Markov Decision Process and builds an architecture that differs from both supervised and unsupervised approaches. Students will have the opportunity to build a chatbot from scratch. We start from a simple rules-based one to a more complex using NLP techniques. 360DigiTMG ensures that the students should gain a thorough knowledge of concepts and applications of statistical tools through hands-on experience. It also helps students to develop relevant skills that are required to grab lucrative jobs in giant companies.

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

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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AI Course Panel of Coaches in United States

data scientist trainers

Bharani Kumar Depuru

  • Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 18+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Deloitte, Hewlett Packard Enterprise, Amazon, Tech Mahindra, Cummins, Accenture, IBM
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
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data scientist trainers

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
  • Trained over 1,500 professional across 12 countries
  • Worked as a Data scientist for 18+ 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, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
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data scientist trainers

Bhargavi Kandukuri

  • Areas of expertise: Business analytics, Quality management, Data visualisation with Tableau, COBOL, CICS, DB2 and JCL
  • Electronics and communications engineer with over 19+ years of industry experience
  • Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
  • Trained over 750+ professionals across the globe in three years
  • Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
Read More >
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"Coming from a psychology background, I was looking for a Data Science certification that can add value to my degree. The 360DigiTMG program has such depth, comprehensiveness, and thoroughness in preparing students that also looks into the applied side of Data Science."

"I'm happy to inform you that after 4 months of enrolling in a Professional Diploma in Full Stack Data Science, I have been offered a position that looks into applied aspects of Data Science and psychology."

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Thanujah Muniandy

"360DigiTMG has an outstanding team of educators; who supported and inspired me throughout my Data Science course. Though I came from a statistical background, they've helped me master the programming skills necessary for a Data Science job. The career services team supported my job search and, I received two excellent job offers. This program pushes you to the next level. It is the most rewarding time and money investment I've made-absolutely worth it.”

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Ann Nee, Wong

"360DigiTMG’s Full Stack Data Science programme equips its graduates with the latest skillset and technology in becoming an industry-ready Data Scientist. Thanks to this programme, I have made a successful transition from a non-IT background into a career in Data Science and Analytics. For those who are still considering, be bold and take the first step into a domain that is filled with growth and opportunities.”

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Mohd Basri

"360DigiTMG is such a great place to enhance IR 4.0 related skills. The best instructor, online study platform with keen attention to all the details. As a non-IT background student, I am happy to have a helpful team to assist me through the course until I have completed it.”

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"I think the Full Stack Data Science Course overall was great. It helped me formalize and think more deeply about ways to tackle the projects from a Data Science perspective. Also, I was remarkably impressed with the instructors, specifically their ability to make complicated concepts seem very simple."

"The instructors from 360DigiTMG were great and it showed how they engaged with all the students even in a virtual setting. Additionally, all of them are willing to help students even if they are falling behind. Overall, a great class with great instructors. I will recommend this to upcoming deal professionals going forward.”

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FAQs for AI & Deep Learning Course

The easiest way to understand the relationship between AI and Deep Learning is to visualise them as concentric circles. In that, AI is an idea that came first in which machines exhibit human intelligence. Machine Learning came next, which is an approach to achieve AI. And finally, Deep learning, which is driving today’s AI explosion, is a technique to implement Machine Learning.

While salaries of AI experts go up to US$ 1 million a year, it could start from US$ 150,000, on an average.

The salary range varies based on experience, industry, domain, geography and various other parameters. However, as a general rule of thumb, we can go with research conducted by job portals. On an average, in Malaysia, Machine learning professionals draw salaries of RM 165,074 per annum. Refer to ->

On an average, Data scientist salaries are around RM 102,000 per annum.

If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.

We assign mentors to each student in this program. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.

Jobs in field of Artificial Intelligence in USA

Jobs in Field of AI & Deep Learning in USA

The job roles in the field of Artificial Intelligence include Artificial Intelligence Developer, Artificial Intelligence Scientist, Artificial Intelligence Engineer, Artificial Intelligence & Machine Learning Engineer, etc.

Salaries in USA for Artificial intelligence Course

Salaries in USA for Artificial Intelligence

The average salary for an AI Engineer in the US is up to $114,121, ranging from $77,000 to $151,000. The salary varies depending upon the experience and roles in the organizations.

Artificial Intelligence Course Projects in USA

Artificial Intelligence Projects in USA

Many projects are being carried out using AI and Deep learning tools in sectors that include Banking, Automation, Finance, and Healthcare.

Role of Open Source Tools in Artificial intelligence

Role of Open Source Tools in Artificial Intelligence

Python, R, and RStudio are eminent statistical tools that help in deploying models. The applications of these statistical tools are very important for students who want to train in AI and Deep Learning.

Modes of training for Artificial Intelligence Course

Modes of Training for Artificial Intelligence

360DigiTMG delivers training through both classrooms as well as online. The Online mode of learning is flexible and students can choose timings as per their schedule.

Industry Application of Artificial Intelligence

Industry Applications of Artificial Intelligence

Artificial Intelligence technology is being adopted rapidly by many industries that include Manufacturing, Automation, Healthcare, Banking, Insurance, Education, Agriculture, Hotel, Finance, Retail, and many more.

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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