Artificial Intelligence Course in ECIL, Hyderabad
- 80 Hours of Intensive Classroom & Online Sessions
- 100+ Hours of Practical Assignments
- 2 Capstone Live Projects
- Receive Certificate from Technology Leader - IBM
- Receive Certificate from Top University - UTM, Malaysia
- 100% Job Placement Assurance
Academic Partners & International Accreditations
"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. Artificial Intelligence Course in ECIL is going to bring a revolution in several industries and there’s a lot of potential in an AI career. With a shortage of skilled and qualified professionals in this field, many companies are coming up and clamouring for the best talent. It is pellucid that AI is rapidly transforming every sphere of our life and is taking technology to a whole new level. It is narrating the ode of new-age innovations in Robotics, Drone Technologies, Smart Homes, and Autonomous Vehicle.
AI & Deep Learning
INR 67,430 47,200/-
Artificial Intelligence Course in ECIL
The Artificial Intelligence training in ECIL 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 ECIL. 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 ECIL.
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 ECIL.
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.
Learning Outcomes of Artificial Intelligence Course in ECIL
This AI Training in ECIL introduces you to the concept of Artificial Intelligence and Deep learning and helps you in understanding Neural Network Architectures, Supervised and Unsupervised Learning, Decision Tree Learning, and Structuring of Algorithms for new AI machines along with learning to minimize errors through advanced optimization techniques. Artificial intelligence is always compared to human intelligence when it comes to solving complex human-centered problems. It has been examining and studying how to incorporate intelligent human behaviors in a computer.It has been researching on topics related to reasoning, problem solving, machine learning, automatic planning and so on.This course provides a challenging avenue for exploring the basic principles, techniques, strengths, and limitations of the various applications of Artificial Intelligence. Students will also gain an understanding of the current scope, limitations, and societal implications of artificial intelligence globally. They will investigate the various AI structures and techniques used for problem-solving, inference, perception, knowledge representation, and learning. This training will also equip them to design AI functions and components for computer games and analyze the algorithms and techniques used for searching, reasoning, machine learning, and language processing. Also, gain expertise in applying the various scientific methods to models of machine learning.
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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
Course Modules of Artificial Intelligence Course in ECIL
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 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
- 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
- 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
- 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.
- Comparison with other Encoders (MP3 and JPEG)
- Implementation in Keras
- Deep AutoEncoders
- Implementing DAE in Keras
- Convolutional Autoencoders
- 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
- 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
- Acoustic Model
- Deep Learning Models
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
- Transformer-XL (pretrained model: “transfo-xl-wt103”)
- 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
- Hyperparameter Optimization
- Neural Architecture Search
- Network Architecture Search
- AutoML Systems
- Auto-Net 1.0 & 2.0
- 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
- 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
- Algorithmic Transparency
- Post-hoc Explainability
- Model-Agnostic Algorithms
- Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
- Feature relevance explanation
- Visual Explanations
- Model-Agnostic Algorithms
- Direct Explainability
- 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.
Artificial Intelligence Course & Deep Learning Trends in ECIL
Data is the anthem for new emerging technologies like Artificial Intelligence.The major trends in this field will be training AI on less data, and using NLP to understand the building blocks of life. AI is the single largest technology revolution of our lives, it is a constellation of technologies that comprise machine learning, deep learning, and natural language processing (NLP). With Industries harnessing its power for practical usage and value it has become the hottest buzzword in the tech industry. Many organizations that have learned to unlock the value trapped in vast volumes of data and are offering impressive remuneration to skilled AI experts. As the market for AI is expected to reach $70 billion by 2020, the job opportunities in this field are abundant.
Industries are investing in Artificial Intelligence to optimize business efficiency, improve productivity, and create new jobs. So, it sounds reasonable for you to look at this emerging field and get training for what it takes to launch yourself into an illustrious career with AI training in ECIL. Excel in AI and Deep Learning concepts and implement a practical application with the certification program by 360DigiTMG in AI and Deep Learning. With 133 million new jobs in the field of AI by 2022, top-notch companies like Amazon, Facebook, Uber, Intel, Samsung, IBM, Accenture, Google, Adobe, Microsoft are on a hunting spree for the smartest professionals with AI skills. So, get set for a career as an AI expert with the training in Artificial Intelligence in ECIL.
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