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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
  • Job Placement Assistance
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artificial intelligence course - 360digitmg
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Calendar-On-Campus Classes

AI & Deep Learning

Program Cost

INR 67,430 47,200/-

"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.

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

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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.

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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