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Artificial Intelligence & Deep Learning Course Training

Learn AI concepts and practical applications in our Certification Programme in AI and Deep Learning. Get set for a career as an AI expert.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 80 Hours of Intensive Classroom & Online Sessions
  • 100+ Hours of Practical Assignments
  • 2 Capstone Live Projects
  • Receive Certificate from Technology Leader - IBM
  • 100% Job Placement Assurance
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Calendar-On-Campus Classes

AI & Deep Learning

artificial intelligence & deep learning course duration

Total Duration

2.5 months

artificial intelligence & deep learning pre-requisite

Prerequisites

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

"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 clamoring 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.

Artificial Intelligence Training Overview

The Artificial Intelligence certification course kicks start with showing people the power and the potential of AI and how to build Artificial Intelligence. This course had been designed for professionals with an aptitude for statistics and a background in a programming language such as Python, R, etc. The training intends to make this course interesting and fun for students while providing them with a simulated environment for learning. The students will learn to solve real-world AI problems with hands-on live projects that will help them learn about the potential areas where AI can be deployed in real life. The course will help you learn theory, algorithms, and coding simply and effectively.

The Artificial Intelligence (AI) and Deep Learning course commence with building AI applications, understanding Neural Network Architectures, structuring algorithms for new AI machines, and minimizing errors through advanced optimization techniques. GPUs and TPUs will be used on cloud platforms such as Google Colab to run Google AI algorithms along with running Neural Network algorithms on-premise GPU machines. Learn AI concepts and practical applications in the Certification Program in AI and Deep Learning. Get set for a career as an AI expert.

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

Artificial Intelligence Training Outcomes

AI is a broad field that comprises machine learning, deep learning, and natural language processing (NLP). It has become the hottest buzzword in the tech industry with many organizations offering impressive remuneration to skilled AI experts. Artificial intelligence is giving computers the sophistication to act intelligently. 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. During this training, you will build algorithms that make it possible for AI to function. It will be beneficial if you have some basic programming skills to make the most out of the course. This training aims to teach you to implement the basic principles, models, and algorithms of AI. Students will be exposed to the potential areas of AI like neural networks, robotics, and computer vision. The objective is also to create awareness and a fundamental understanding of various applications of AI. Emphasis will be placed on ‘hands-on’ approach for understanding and upon the completion of this course the students will

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 introduced to analyse sequence data and perform Text Analytics and Natural Language Processing (NLP) using Recurrent Neural Network
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

80 hours

Classroom Sessions

artificial intelligence training

100 hours

Assignments

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 Certification Training Modules

This module on AI will help you gain an understanding of AI around design and its implementation. The module commences with an introduction to Python and Deep Learning libraries like Torch, Theono, Caffe, Tensorflow, Keras, OpenCV, and PyTorch followed by in-depth knowledge of Tensorflow, Keras, OpenCV, and PyTorch. Learn about the CRISP-DM process that is used for Data Analytics / AI projects and the various stages involved in the project life cycle in-depth. Build a clear understanding of the importance and the features of multiple layers in a Neural Network. Understand the difference between perception and MLP or ANN. In the module, you will also be building a chatbot using generative models and retrieval models and understand the RASA NLU framework. Last but least you will also learn about architecture and real-world application of Deep Belief Networks (DBNs) and build a speech to text and text to speech models.

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 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, R studio, Jupyter 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 vs Imbalanced datasets
  • Cross-Sectional vs Time Series versus Panel / Longitudinal Data
  • Batch Processing versus Real-Time Processing
  • Structured vs Unstructured vs Semi-Structured Data
  • Big vs Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling Techniques for Handling Balanced vs Imbalanced Datasets
  • Measures of Central Tendency & Dispersion
    • 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 - Y is Continuous -  Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error
  • Error Functions - Y is Discrete - Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross Entropy
  • Machine Learning Projects Strategy

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
  • Linear Regression Ordinary Least Squares using Calculus

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 Biological & Artificial Neuron
  • Compositionality in Data – Images, Speech & text
  • Mathematical Notations
  • Introduction to 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 (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 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 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

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.

 
  • CNN-RNN Variants
  • 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 - Bidirectional vs Deep Bidirectional RNN
  • 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 Autoencoders
    • 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.

 
  • 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
  • AutoML on Cloud - GCP
    • AutoML Natural Language
    • Performing Document Classification
    • AutoML Version API's for Image Classification
    • Performing Sentiment Analysis using AutoML Natural Language API
    • Tensor-Flow Models Using Cloud ML Engine
    • Cloud ML Engine and Its Components
    • Training and Deploying Applications on Cloud ML Engine
    • Choosing Right Cloud ML Engine for Training Jobs

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

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Artificial Intelligence Course Panel of Coaches

artificial intelligence course trainer

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, Hyderabad and Indian School of Business
Read More >
 
artificial intelligence course trainer

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data visualisation
  • Trained over 1,500 professional 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
Read More >
 
artificial intelligence course trainer

Nitin Mishra

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data visualisation
  • 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
Read More >
 
ai with deep learning certification
Artificial Intelligence UTM certificate course - 360digitmg

Certificate

Win recognition for your AI skills with the Certification Programme in AI and Deep Learning. Stand out in this emerging yet competitive field with our certification.

FAQs for Artificial Intelligence Course Training

While we also have industry specific trainings in the space of Data Science, Machine Learning and AI, our regular program covers all industries to accommodate all the participants. Our participants come from varied backgrounds.

Yes, all the widely used neural networks are explained in detail. If there is any new Neural Network algorithm which is introduced in the industry then we shall explain that also as part of the ongoing webinars.

We shall be running CNNs on cloud and hence one need not worry about the laptop configuration. However, as of date a minimum of 32GB RAM and 2080ti GPU from Nvidia is most preferred.

Yes, each session is recorded and videos are placed on our LMS and you receive lifetime access to LMS.

The Deep Learning book is the most preferred from theoretical perspective. However, from programming and practical application perspective, Deep Learning training by 360DigiTMG should suffice. We also provide a few Neural Network tutorials for you to watch before attending the regular classes.

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

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 India for artificial intelligence
Salaries in India 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.

artificial intelligence Projects in India
Artificial Intelligence Projects

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 artificial intelligence course
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 artificial intelligence with Python
Modes of training in Artificial Intelligence course

The course in India 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 Artificial intelligence course
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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